The Implications of Declining Firm-Level Uncertainty for Consumption Variety and Cities (Unicredit & Universities Job Market Best Paper Award)

alumni

Editor’s note: In this post, Federica Daniele (Economics ’13 and PhD candidate at UPF-GPEFM) shares a summary of her paper, “The Implications of Declining Firm-Level Uncertainty for Consumption Variety and Cities,” which has won the 2017 UniCredit & Universities Economics Job Market Best Paper Award. She also offers some advice to aspiring PhD students in the Barcelona GSE Master’s programs.


Paper summary

There is something alarming about the direction in which firm dynamics have been changing over the course of the last decades. Today it’s much rarer to encounter firms that undergo large up/downsizings than it used to be in the past: in other words, firms have become more tied to their rank in the firm size distribution. This has been of concern for many economists, who see this happening jointly with a slowdown in aggregate productivity growth and competitiveness. Being aware that the question on the drivers behind this trend and its consequences was still open to debate, coupled with an interest for entrepreneurship, is what pushed me to dive into this topic to better our understanding of the issue in my paper, “The Implications of Declining Firm-Level Uncertainty for Consumption Variety and Cities.”

An explanation for the decline in business dynamism consistent with the data is that technological change has caused the degree of idiosyncratic uncertainty that firms routinely face about their chances to grow to go down. This implies that today most of the return from starting a firm is determined by its initial (in)success as opposed to luck in the development of the business over its life-cycle. Based on evidence drawn from data on the universe of German establishments, in the paper I argue that a reduction in firm-level uncertainty is consistent with lower incentives for potential entrepreneurs to start a new business. My paper offers a new insight into the literature on the role of uncertainty for economic activity: some degree of uncertainty is beneficial, because – by unlocking the opportunity for a given firm to grow large out of fortuitous events (such as a favourable demand turn) – it encourages entrepreneurship. In this sense, my paper provides a defence of the classical argument by Frank Knight according to which risk-taking is a characterising feature of entrepreneurship.

A deficit in the growth rate of the stock of establishments triggered by a decline in firm-level uncertainty is cause of concern for multiple reasons. In my paper, I investigate the importance of two dimensions: first of all, the fact that consumers get to consume a less wide variety of goods than otherwise; and secondly, the fact that, being the loss in entrepreneurship larger in big cities, fewer consumers find appealing to move to large cities than otherwise, thus diminishing the extent of positive spillovers due to higher urban density. Another outcome of interest would have been, for example, the process of innovation within an industry.

All in all, the contribution of this paper consists of assessing both empirically and theoretically novel long-run consequences on economic activity of declining firm-level uncertainty.

Advice for future PhD students

I think Barcelona GSE masters students who are considering going the PhD / academic career route should be strategic. There is no harm in taking one year to do some exploratory work, working as RA, for example, for some good professor, if that buys the time to figure out what kind of research best matches your interests, in which institution you would feel better fulfilled, or whether academia suits you at all.

In the end, if you choose to pursue the academic route, you will have most certainly achieved a better match with the institution/supervisor, and spared a lot of time later on during the course of the PhD, which you can instead dedicate to producing research of good quality.

But even if you decide that academia is not for you, the value of the investment will still be positive, as experimenting early during one’s working career is much less costly than doing it later.

International Asset Allocations and Capital Flows: The Benchmark Effect

By Tomas Williams (Economics ’12, GPEFM ’17), Assistant Professor of International Finance at George Washington University in Washington, DC.

Tomas Williams (Economics ’12, GPEFM ’17) is Assistant Professor of International Finance at George Washington University in Washington, DC. His paper, “International Asset Allocations and Capital Flows: The Benchmark Effect” (with Claudio Raddatz, Central Bank of Chile and Sergio Schmukler, World Bank Research Group) is forthcoming at the Journal of International Economics.


International Asset Allocations and Capital Flows: The Benchmark Effect

As financial intermediaries such as open-end funds with benchmark tracking grow in importance around the world, the issue of which countries belong to relevant international benchmark indexes (such as the MSCI Emerging Markets) has generated significant attention in the financial world (Financial Times, 2015). The reason is that the inclusion/exclusion of countries from widely followed benchmarks has implications for the allocation of capital across countries. As institutional investors become more passive, they follow benchmark indexes more closely. These benchmark indexes change over time, as index providers reclassify countries, implying that investment funds have to re-allocate their portfolio among the countries they target. The capital flows generated by these portfolio re-allocations are important since worldwide open-end funds that follow a few well-known stock and bond market indexes manage around 37 trillion U.S. dollars in assets (ICI, 2016). These changes in benchmark indexes can produce unexpected effects in international capital flows, linked to how financial markets work, not necessarily to economic fundamentals.

One clear example of these counterintuitive reallocations happened when MCSI announced in 2009 that it would upgrade Israel from emerging to developed market status, moving it from the MSCI Emerging Markets (EM) Index to the World Index. When the upgrade became effective in May 2010, Israel faced equity capital outflows of around 2 billion dollars despite its better status (Figure 1 below, click image to enlarge). The reason is that Israel became a smaller fish in a bigger pond. Israel’s weight in the MSCI EM Index decreased from 3.17 to 0, while it increased from 0 to 0.37 in the MSCI World Index. Israeli stocks in the MSCI index fell almost 4 percent in the week of the announcement and significantly underperformed the stocks not included in the index. The week prior to the effective date (when index funds rebalanced their portfolio) there was a 4.2 percent drop in the MSCI Israel Index, versus a 1.5 fall in the Israeli stocks outside the index.

Figure 1. Direct Benchmark Effect: Aggregate Flows
This figure shows aggregate data on flows in Israel around the time of large benchmark weight changes. Figure 1 shows data for portfolio equity liability flows and portfolio debt liability flows for Israel quarterly between 2007 and 2011. Figure 2 shows the cumulative flows from frontier markets passive funds around the upgrade of Qatar and United Arab Emirates to the MSCI Emerging Markets.

The effects of index reclassifications go beyond the countries and asset classes being specifically targeted. Spillovers could occur to other countries that share a certain benchmark with countries affected by reclassifications. A clear example of this is the upgrade in June 2013 of Qatar and United Arab Emirates (UAE) from the MSCI Frontier Markets (FM) Index to the MSCI EM Index. Together, these two countries were around 40 percent of the MSCI FM Index before the reclassification. When this reclassification took place, funds tracking closely the MSCI FM Index had to sell securities from these two countries and use the money to invest in the rest of the countries in the MSCI FM Index. This resulted in significant capital inflows and stock market price increases in countries such as Nigeria, Kuwait, and Pakistan (Figure 2, click image to enlarge).

Figure 2. Cumulative Flows from Frontier Passive Funds
Figure 2. Cumulative Flows from Frontier Passive Funds

These movements in financial markets have led to speculations and market movements related to potential new reclassifications. One recent and prominent example is that of China. For the past two years, MSCI delayed numerous times the introduction of China A-shares as a part of the MSCI Emerging Markets. Finally, in June 2017, they confirmed the inclusion of only a fraction of these stocks, creating capital inflows into the Chinese stock markets, and increases in stock prices (Financial Times, 2017). Chinese sovereign bonds may see similar capital inflows if J.P. Morgan, Citibank and Barclays decide to add China into their flagship bond indexes (CNBC, 2017).

In a recent study (Raddatz et al., 2017), we systematically document these benchmark effects, showing the various channels through which prominent international equity and bond market indexes affect asset allocations, capital flows, and asset prices across countries. Benchmarks have statistically and economically significant effects on the allocations and capital flows of mutual funds across countries. For example, a 1 percent increase in a country’s benchmark weight results on average in a 0.7 percent increase in the weight of that country for the typical mutual fund that follows that benchmark. These benchmark effects on the mutual fund portfolios are relevant even after controlling for time-varying industry allocations and country-specific or fundamental factors. Exogenous events that modify benchmark indexes affect benchmark weights. Furthermore, asset prices move both during the announcement and effective dates of the benchmark changes in response to the capital movements.

Academics, financial institutions, and policy makers have already started paying attention to the potential effects of benchmarks on capital flows and asset prices, as well as on herding, momentum, and risk taking (BIS, 2014; Arslanalp and Tsuda, 2015; IMF, 2015, Shek et al., 2015; Vayanos and Woolley, 2016). More work in this area would be welcomed as passive investing continues expanding.

References

Arslanalp, S., Tsuda, T., 2015. Emerging Market Portfolio Flows: The Role of Benchmark-Driven Investors. IMF Working Paper 15/263, December.

BIS, 2014. International Banking and Financial Market Developments. BIS Quarterly Review.

CNBC, 2017. Chinese Stocks got their Global Stamp of Approval, and now Bonds may be next.

Financial Times, 2015. Emerging Market Investors Dominated by Indices. August 4.

Financial Times, 2017. China Stocks Set for $500bn Inflows after MSCI Move. June 21.

ICI, 2016. Investment Company Institute: Annual Factbook.

IMF, 2015. Global Financial Stability Report.

MSCI, 2016. Potential Impact on the MSCI Indexes in the Event of the United Kingdom’s Exit from the European Union (“Brexit”). June.

Raddatz, C., Schmukler, S., Williams, T., 2017. International Asset Allocations and Capital Flows: The Benchmark Effect. Working Papers 2017-XX, The George Washington University, Institute for International Economic Policy.

Shek, J., Shim, I., Shin H.S., 2015. Investor Redemptions and Fund Manager Sales of Emerging Market Bonds: How Are They Related? BIS Working Paper 509.

Vayanos, D., Woolley, P., 2016. Curse of the Benchmarks. LSE Discussion Paper 747.

Wall Street Journal, 2014. Colombia Wins Investors’ Favor – And That’s the Problem. August 13.

About Tomas Williams

From his website:

I am an Assistant Professor of International Finance at George Washington University in Washington, D.C. My main fields of research are International Finance, Financial Economics and Empirical Banking. I have a special interest on financial intermediaries and how they affect international capital flows and economic activity. More specifically, I have been working on how the use of well-known benchmark indexes by financial intermediaries affects both financial markets and real economic activity.

More personally, I grew up in Buenos Aires, and studied economics at Universidad del CEMA. Afterwards, I moved to Barcelona and completed the Master’s Degree in Economics and Finance (Economics Program) at Barcelona GSE. Later on, I received my Ph.D. in Economics and Finance from Universitat Pompeu Fabra. I also spent one year as a visiting doctoral student in the Financial Markets Group (FMG) at the London School of Economics and Political Science.

Connect with Tomas on Twitter

Optimal density forecast combinations (Unicredit & Universities Job Market Best Paper Award)

Greg Ganics (Economics ’12 and PhD candidate at UPF-GPEFM) provides a non-technical summary of his job market paper, which has won the 2016 UniCredit & Universities Economics Job Market Best Paper Award.

authorEditor’s note: In this post, Greg Ganics (Economics ’12 and PhD candidate at UPF-GPEFM) provides a non-technical summary of his job market paper, “Optimal density forecast combinations,” which has won the 2016 UniCredit & Universities Economics Job Market Best Paper Award.


After the recent Great Recession, major economies found themselves in a situation with low interest rates and fragile economic growth. This combination, along with major political changes in key countries (the US and the UK) makes forecasting more difficult and uncertain. As a consequence, policy makers and researchers have become more interested in density forecasts, which provide a measure of uncertainty around point forecasts (for a non-technical overview of density forecasts, see Rossi (2014)). This facilitates communication between researchers, policy makers, and the wider public. Well-known examples include the fan charts of the Bank of England, and the Surveys of Professional Forecasters of the Philadelphia Fed and the European Central Bank.

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BOE fan chart. Source: Bank of England Inflation Report, November 2016

Forecasters often use a variety of models to generate density forecasts. Naturally, these forecasts are different, and therefore researchers face the question: how shall we combine these predictions? While there is an extensive literature on both the theoretical and practical aspects of combinations of point forecasts, our knowledge is rather limited about how density forecasts should be combined.

In my job market paper “Optimal density forecast combinations,” I propose a method that answers this question. My main contribution is a consistent estimator of combination weights, which could be used to produce a combined predictive density that is superior to the individual models’ forecasts. This framework is general enough to include a wide range of forecasting methods, from judgmental forecast to structural and non-structural models. Furthermore, the estimated weights provide information on the individual models’ performance over time. This time-variation could further enhance researchers’ and policy makers’ understanding of the relevant drivers of key economic variables, such as GDP growth or unemployment.

Macroeconomists in academia and at central banks often pay special attention to industrial production, as this variable is available at the monthly frequency, therefore it can signal booms and busts in a timely manner. In an empirical example of forecasting monthly US industrial production, I demonstrate that my novel methodology delivers density forecasts which outperform well-known benchmarks, such as the equal weights scheme. Moreover, I show that housing permits had valuable predictive power before and after the Great Recession. Furthermore, stock returns and corporate bond spreads proved to be useful predictors during the recent crisis, suggesting that financial variables help with density forecasting in a highly leveraged economy.

The methodology I propose in my job market paper can be useful in a wide range of applications, for example in macroeconomics and finance, and offers several avenues for further research, both theoretical and applied.

References:

Ganics, G. (2016): Optimal density forecast combinations. Job market paper

Rossi, B. (2014): Density forecasts in economics and policymaking. Els Opuscles del CREI, No. 37

Heterogeneous Inputs, Human Resource Management and Productivity Spillovers: What Do Poultry Farm Workers Have to Say? – Job Market Paper

authorThe following job market paper summary was contributed by Francesco Amodio (Economics ’10 and GPEFM). Francesco is a job market candidate at UPF. He will be available for interviews at the SAEe (Palma de Mallorca, December 11-13) and ASSA (Boston, January 3-5) meetings.

 


Management matters. Differences in management practices can explain a considerable amount of variation in firms’ productivity and performance, both across and within sectors and countries (Bloom and Van Reenen 2007, 2010, 2011). Several studies have shown how human resource management and incentive schemes may affect overall productivity by making the effort choices of coworkers interdependent (Bandiera, Barankay and Rasul 2005, 2007, 2009). In more complex settings, however, workforce management features may interact with production arrangements and jointly determine the overall result of the organization. Understanding the nature of this interplay is of primary importance in the adoption and implementation of productivity-enhancing management practices.

In my job market paper, coauthored with Miguel A. Martinez-Carrasco, we shed light on these issues by focusing on settings where workers produce output by combining their own effort with inputs of heterogeneous quality. This is a common feature of workplaces around the world. For instance, in Bangladeshi garment factories, the characteristics of raw textiles used as inputs affect the productivity of workers. Similarly, the purity level of chemicals affects the productivity of researchers in biological research labs.

Now, suppose we pick a worker and endow her with higher quality inputs, thus increasing her productivity. What happens to the productivity of coworkers around her? Do they exert more effort, or do they shirk? How do human resource management features shape their response?

The setting

In order to answer these questions, we collected data from an egg production plant in Peru. Production is carried out in production units located one next to the other in several sheds. In each production unit, a single worker is assigned as input a batch of laying hens. Workers’ main tasks are to feed the hens, to maintain and clean the facilities, and to collect the eggs. The characteristics of the hens and worker’s effort jointly determine productivity, as measured by the daily number of collected eggs. Figure 1 shows the picture of one shed hosting four production units. Notice how workers in neighboring production units can easily interact and observe each other.

figure

The specific features and logistics of this setting generate the quasi-experiment we need in order to answer the questions of interest. All hens within a given batch have very similar characteristics. When reaching their productive age, they are moved to one production unit and assigned to the corresponding single worker who operates the unit. After approximately 16 months, they reach the end of their productive age and are discarded altogether. The age of hens in the batch exogenously shifts productivity. Indeed, Figure 2 shows the reversed U-shaped relationship that exists between hens’ age and productivity. Perhaps more importantly, the timing of batch replacement varies across production units, generating quasi-random variation in the age of hens assigned to workers.1 We can thus exploit these differences to credibly identify the causal effect of an increase in coworkers’ productivity – as exogenously shifted by coworkers’ hens age – on own productivity, conditional on own hens’ age.

figure

Main Results

We find evidence of negative productivity spillovers. The same worker, handling hens of the same age, is significantly less productive when coworkers in neighboring production units are more productive, with variation in the latter being induced by changes in the age of their own hens. This finding is pictured in Figure 3, which shows that a U-shaped relationship exists between own productivity and coworkers’ hens age. In other words, workers exert less effort and decrease their productivity when coworkers are assigned higher quality inputs.

figure

We also find similar negative effects on output quality, as measured by the fraction of broken and dirty eggs collected over the total number of eggs. Furthermore, we find no effect of an increase in the productivity of coworkers located in non-neighboring production units or in different sheds, suggesting that workers only respond to observed changes in coworkers’ productivity.

The role of HR

Why do workers exert less effort when coworkers’ productivity increases? Our hypothesis is that the way the management processes information on workers’ productivity in evaluating them and taking employment termination decisions generates free ride issues among coworkers. When observed productivity is only a noisy signal of workers’ exerted effort, the management combines available signals and best guesses the level of effort exerted by the worker. Even when observable input characteristics can be netted out, individual signals are still imperfect, and possibly excessively costly to process. The management thus attaches a positive weight to aggregate or average productivity in evaluating a single worker. As a result, workers free ride on each other.

In order to test for this hypothesis, we collected employee turnover data from the same firm. As expected, we find that the likelihood of employment termination is lower the more productive the worker is. More importantly, being next to highly productive workers improves a given worker’s evaluation and diminishes her marginal returns from effort, yielding negative productivity spillovers.

We also find that providing incentives to workers counteracts their tendency to free ride. First, we find no effect of coworkers’ productivity when workers are exposed to piece-rate pay. Second, we collected data on the friendship and social relationship among workers, and find again no effect of coworkers’ productivity when a given worker recognizes any of her coworkers as friends. We interpret this as further evidence that the main result of a negative effect of coworkers’ productivity indeed captures free riding issues, mitigated by the presence of social relationships.

Discussion

Our focus on production inputs and their allocation to working peers represents the main innovation with respect to the previous literature on human resource management and incentives at the workplace. In our case study, the allocation of inputs of heterogeneous quality among workers triggers free riding and negative productivity spillovers among them, generated by the workers’ evaluation and termination policies implemented at the firm.

The analysis of more complex production settings reveals the existence of intriguing patterns of interplay between production arrangements and human resource management practices. Our plan for the next future is to proceed further along this line of inquiry. In a companion paper still work in progress, we investigate both theoretically and empirically how workers influence each other in their choice of inputs while updating information on the productivity of the latter from own and coworkers’ experience.


1 Grouping all observations belonging to the same shed and week and taking residuals, we show that the age of hens assigned to coworkers is orthogonal to the age of own hens. We test this hypothesis in several different ways, addressing the issues arising when estimating within-group correlation among peers’ characteristics (Guryan, Kroft, and Notowidigdo 2009; Caeyers 2014). We cannot reject the hypothesis of zero correlation in all cases.