The main objective of this three year research project is to develop a microeconomic approach to enhance our knowledge and understanding of the origins of booms and busts in macroeconomic activity, with a special focus on Kazakhstan. Since the financial and global crisis that started in 2007, traditional macroeconomic approaches have not been able to provide much policy guidance and economists do not seem to agree on which recipes to use for economic recovery. Or as The Economist (January 10th, 2015, p8) formulates it “…Not only have macroeconomists been embarrassed by a decade of failed predictions, but they are also losing their edge. For anyone starting out in economics, the future is micro…Macroeconomics are puritans, creating theoretical models before testing them against data. The new breed (micro) ignore the whiteboard, chucking numbers together and letting computers spot the patterns.”
This research program therefore builds on recent micro datasets (big data), using insights and new methodological approaches in microeconomics/econometrics, to understand better the micro channels that affect key macroeconomic indicators that are commonly used in economic policy, such as inflation, GDP growth and unemployment. To this end, the program is based on the research infrastructure (micro data) that is jointly being developed at the School of Social Sciences and Humanities (Economics’ department) and the Graduate School of Business at Nazarbayev University. In particular, confidential firm level data covering all medium and large sized firms active in Kazakhstan have been made available to the research team, which allows detailed analysis of the research questions put forward in the current project. The focus of the research program is to analyze how idiosyncratic shocks may (or may not) affect the macroeconomic performance of a country or region. Idiosyncratic shocks refer to shocks that are specific to particular firms, products, workers or regions, which are assumed to average out. And since there are many firms, such idiosyncratic shocks are believed to have a negligible impact for the overall macro economy.
However, recent research shows that firm heterogeneity matters for understanding the impact of idiosyncratic shocks for the overall economy for a number of reasons. For instance, an idiosyncratic shock to one particular product or firm may become important through its central role in the supply chain and hence the inter-linkages between firms can amplify such shocks†. The role of firm heterogeneity has been exploited in recent work explaining fluctuations in GDP growth (Davis, et al., 2007; Gabaix, 2011; Acemoglu, et al, 2012), unemployment (Moscarini and Postel-Vinay, 2012), trade (di Giovannni, Levchenkov, and Mejean, 2014; Bernard, Van Beveren and Vandenbussche, 2014) and aggregate (export) prices (Amiti, Itskohki and Konings, 2014). These papers build on the insight that when the firm size distribution is fat-tailed, idiosyncratic shocks to large firms contribute more to aggregate fluctuations. This is also known as Gibrat’s law, i.e. when the standard deviation of the percentage growth rate of a firm/city is independent of its size, individual firms/cities can matter in the aggregate (Eeckhout, 2004, Desmet and Parente,2010). Network effects may explain this as argued by for example Acemoglu, Ozdaglar and Tahbaz-Salehi (2015), who show that large economic downturns may result from the propagation of microeconomic shocks over the input- output linkages across different firms and sectors within the economy. Similarly, Kelly, Van Nieuwerburgh and Lustig (2013) develop a network model of firm volatility in which larger suppliers have more customers. They show that network effects are essential to explain the joint evolution of the empirical firm size and firm volatility distributions.
This research project wants to provide a new and different approach to analyze macroeconomic performance, by recognizing that heterogeneous behavior of the underlying micro economic parts provide important new insights about the channels of macroeconomic fluctuations. These approaches are sometimes also referred to as the ‘granular origins’ of aggregate fluctuations.
A key novelty of this program is that the approach we follow is structured along the micro data sets that have been and are being developed recently, which we elaborate on in section 3. As such, we aim to identify the ‘DNA’ of the Kazakh economy and extend the work that was started in Duparcq and Konings
(2016), which developed an ‘enterprise map for Kazakhstan’. To this end, the project will engage in unravelling the micro channels affecting the key macroeconomic indicators commonly used in economic policy: Inflation, GDP growth and Employment (or Unemployment). These define the three broad work packages (WP) of our project. Hence we want to find out about price formation of individual products supplied by firms, about firm level productivity growth, and about employment turnover in firms and worker responses to shocks. We discuss this in more detail in section 3. In doing so, we will focus on the Kazakh economy, but we will engage in a comparative analysis with another emerging economy, Slovenia, for which similar micro data exist.
Micro Data Infrastructure
Since the early 1990s large longitudinal data sets on firms and workers became increasingly available to researchers, which boosted empirical research in economics. This led to a series of new applications in fields like labor, international and industrial economics using a variety of microeconometric tools. Examples include a large literature analyzing wage setting between firms and trade unions‡, competition and price setting§, globalization, trade policy and firm performance**. The PI of this project was among the first to work on this first generation of applications using micro data in particular fields in economics. While these applications using ‘big data’ were useful to obtain insights about one particular dimension of firm behavior, the overall macroeconomic implications were mostly ignored†† and hence the macroeconomic models (like VAR and DSGE models) were mostly using time series macroeconomic data (such as data on GDP, unemployment, inflation, exchange rates, etc.) to model business cycle fluctuations and to analyze economic policy.
Recently, a number of researchers‡‡, including the team of the current program, start spelling out the new and richer insights one may obtain from exploiting the heterogeneity in micro data for a better understanding of macroeconomic fluctuations. The type of micro data that are being used for teasing out the relationship between micro dynamics and macroeconomic fluctuations are typically much more detailed than those used before. This is triggered by the increased digitalization and digital screening firms have started to implement (e.g. price scanning of products in supermarkets; or the registration of firm linkages through vat transactions), which allows micro economists to explore richer patterns and relationships in the data. These newly available micro data sets introduce a second ‘generation’ of applications and disrupt the standard approaches in macroeconomics. Building on the data infrastructure, expertise and insights accumulated by the PI over the last two decades, we want to weigh and shape this new approach in economics, here focusing primarily on the Kazakh economy and one other emerging economy, Slovenia as a benchmark. This is of particular relevance given the severe crisis the RKZ went through in recent years and given the massive economic shock the devaluation of the Tenge in 2015 has triggered.