Kazakhstan is one of the least densely populated countries in the world. It has an area roughly equal to that of all of Western Europe and a population of 19 million – around that of the Netherlands. Kazakhstan became Independent in 1991 after the fall of the Soviet Union and shortly thereafter experienced an oil boom. The global commodity super-cycle ended in 2014, and oil prices fell. Kazakhstan’s unique socioeconomic history and its vulnerability to commodity price shocks have led to three interrelated development challenges. The country’s large size and low population density mean it is a remote place, and economic innovation and productivity growth are impeded by the difficulty in agglomerating people in major urban centers. The dominance of oil in its export basket puts Kazakhstan at risk of the consequences of Dutch disease and resource-dependent growth. Finally, its history as a former Soviet-state leaves Kazakhstan with domestic macroeconomic imbalances and a loss of productive capacities as a result of the centrally planned economy of the Soviet Union.
Kazakhstan was able to achieve an impressive growth episode between 2000-2020, more than doubling its GDP per capita. This strong growth witnessed in the early and mid-2000s was fueled by the country’s hydrocarbon resources, structural reforms, and high Foreign Direct Investments (FDI). However, the fall in oil prices starting in 2014, the lack of economic diversification into non-oil tradables despite ample policy support, and recent geopolitical challenges have put the sustainability of growth in jeopardy.
The Growth Lab is currently engaged with the Government of Kazakhstan to help decision-makers promote sustained long-term growth and inform investment policy design. The research collaboration involves analyzing the country’s productive structure, identifying binding constraints to growth, and providing support for policy formulation and implementation with the aim of initiating sustainable and inclusive growth.
My internship with the Growth lab coincided with analyses that aimed to link growth diagnostics with policy recommendations. My internship, therefore, had three objectives. First, to dig deeper into some of the puzzles identified in constraints analysis. One of these puzzles I focused on was stagnant corporate growth despite generous policy support. The second objective was to formulate policy recommendations to ease the binding constraints identified in the areas of macroeconomics and finance. The third objective was to discuss the team’s diagnostics findings and policy recommendations with stakeholders on the ground in Kazakhstan.
My first few weeks of the internship were based at the Harvard Kennedy School, where I worked on assessing whether access to finance was a constraint to investments and growth in Kazakhstan. Kazakhstan has a stable deposit-to-GDP ratio but a declining private-credit-to-GDP ratio. Recognizing the role of access to finance in investment promotion, the government has implemented several credit initiatives to boost corporate credit, including subsidized lending, credit guarantees, and direct lending. However, corporate credit growth remained dampened despite policy support (Figure 1).
Figure 1.
In its diagnostic framework, the Growth Lab assesses whether access to finance is an obstacle to growth for many reasons. Constrained access to finance can reduce the potential for innovation by forcing companies to forgo investment opportunities and affecting companies’ survival and growth by contributing to working capital constraints. It might also reflect perceptions about the appropriability of returns on innovation and investment. Low corporate credit growth can be a result of supply constraints, demand constraints, or a mix of both. Supply constraints can result from a high price of credit, tight lending conditions, or poor intermediation that fails to allocate available capital to the most promising investment. On the other hand, demand-side constraints can be attributable to most of the branches in the Growth Lab diagnostic tree, including low social return on investments, low skills, and an unfair competition landscape. Thus, my analysis focused on assessing whether there are obstacles to access to finance and whether these obstacles result from supply-side constraints.
The analysis I did while in Cambridge enabled me to understand aggregate trends about credit growth in Kazakhstan and the aggregate scope and size of credit support policies. We learned that Kazakhstan’s private-credit-to GDP is well below the expected levels in its income group (Figure 2). We also found that while interest rates are slightly above average for its income group, collateral requirements are well above the average for Kazakhstan’s income group. This analysis helped us map potential constraints that affect almost all borrowers in Kazakhstan. We were also able to map these constraints to the available credit support policies to triangulate that while high interest rates were targeted with subsidized lending, high collateral constraints represented a blind spot in current support policies.
Figure 2.
I then began to analyze the size of the different credit support measures and found that most of the support goes into interest rate subsidies, which aims to decrease the cost of lending for firms. However, support aimed at mitigating the high collateral requirement was limited in comparison.
Building upon the framework taught to us in the MPA/ID program at HKS, the Growth Lab’s “holy trinity” for sound policy design relies on policies that are technically correct, administratively feasible, and politically supportable. It made technical sense to consider a policy shift from interest rate subsidies to credit guarantees, based on the signals we observed of high collateral requirements. However, we then tested the administrative feasibility of our policy proposal. To assess the administrative feasibility for expanding support to mitigate the collateral requirement constraint, I estimated the fiscal cost of interest rate subsidies and credit guarantees, assuming different scenarios for credit growth.
I then traveled to Nur-Sultan, Kazakhstan’s capital, where I stayed and worked for a month to test our findings and policy ideas with businesses in different sectors. My trip to Kazakhstan gave me many “Aha” moments as it provided me with hands-on conceptualizations of the importance of many of the concepts we covered during my first year in the MPA/ID. Professor Dani Rodrik’s class taught us, among many things, the importance of establishing channels of communication with the private sector that are governed by an institutional framework to keep the private sector at arm’s length. We discussed how this institutional framework should aim to keep the private sector close enough to provide feedback on implementation and insights on constraints, but also far enough to avoid risks of corruption and favoring certain businesses over others.
The opportunity to discuss issues such as low corporate credit growth and less-than-optimal investments with businesses on the ground provided me with insights on bottlenecks in the implementation of credit support policies and exporters’ support policies that I otherwise would have missed if I solely relied on quantitative analysis. Another “Aha” moment was seeing first-hand the heterogeneity of constraints and bottlenecks faced by businesses depending on their sector, size, level of innovation, and age. This heterogeneously is easy to miss when relying purely on quantitative desk research.
While I was aware of the unique insights I obtained during my time in Astana, I was also aware of the potential selection bias because I met with a small sample size of firms. I also knew that they might be telling me one thing but acting out other behaviors in reality. These were risks I knew going into the interviews, but nevertheless, the experience helped me reflect on common criticism towards the international development field due to the limited face time development practitioners have with local stakeholders and their limited and distant understanding of the context. We often tend to overlook that, in the absence of direct and frequent communication channels, even local bureaucrats and government officials have limited face time with businesses and beneficiaries of their policies. With low data availability in many developing countries, qualitative data from interactions with policy beneficiaries is even more crucial for policy design and implementation.
As I start my second year of the MPA/ID and prepare to rejoin the policymaking field after graduation, the lessons learned during my Growth Lab internship on the role of comparative statistics in diagnostics, diagnostic methodology of identifying constraints, and role of qualitative data in policy design and assessment are an essential addition to my Kennedy school experience.