High speed internet and connectivity are among the main drivers of economic development in today’s information-intensive societies. Hence, in the context of the social sciences, increasing attention has been devoted to implications of technology adoption for traditional outcomes, such as migration, civic engagement and political participation, with particular emphasis on developing countries. As a matter of fact, numerous scholars argue that bridging the digital divide, hence fostering enhanced communication and ease of information access, has the potential to foster empowerment and beneficial innovation opportunities.
Therefore, this paper analyzes the impact of increased internet access on both internal and external migratory flows in Nigeria, where a national broadband expansion plan targeting existing infrastructure in urban areas was enacted in 2013. This effect is evaluated using two difference-in-difference estimations.
The first one assesses whether the policy was successful in enabling higher internet access rates. Convincing evidence was found to prove the effectiveness of the broadband expansion policy to date: in 2015, urban residents were approximately 7% more likely to have access to the internet on average, and these results are robust to different specifications.
The second estimation sets out to understand whether this effect triggered mobility and relocation. The analysis shows that there is a positive and significant effect in the order of 2%, robust to all specifications. However, the model’s explanatory power is low, as structural characteristics of the relevant sections of this dataset used pose significant limitations to the analyses and their potential for generalization. In any case, in the most basic form of the model, it appears that after the policy change urban residents were 1.5% more likely to move. This marginal impact becomes 1.9% once household fixed effects are accounted for. Finally, with respect to external migration probability, the overall significance of the model is not ascertained, so that even though increased internet access seems to have had a slightly negative effect on this outcome, we cannot safely conclude anything about this particular relationship.
As previously mentioned, results are hardly generalizable due to the structural limitations of the dataset, the lack of meaningful control variables and the mostly unexplored nature of migration dynamics in Nigeria.
Indeed, many of these setbacks arise from the relative scarcity or availability of surveys that include questions on information and communication technologies, particularly in developing countries. This in turn is due to the fact that data on mobile connectivity and other digital technology-related information is difficult to obtain, especially for rural communities. In this particular dataset the range of questions on the topic was very limited, which significantly narrowed the scope for the empirical analysis, and hampered the inclusion of meaningful control variables. Questions on mobility were also rather scarce, given that respondents were not directly asked about reasons for their decision: data on labor, education, health was collected in different sections and often could not be merged given that observations were not uniquely identified.
Consequently, the analysis started here can be meaningfully expanded and improved. Interesting extensions would explore the relationship between intrastate migration, a phenomenon which has only recently received the attention it requires, and internet usage: controlling for educational, health-related and employment decisions more accurately, it would be possible to better isolate the effect of internet access on migratory flows. In particular, it would be insightful to verify whether a causal relationship exists, perhaps by identifying a meaningful instrumental variable for this purpose. This would also shed light on whether determinants of internal migrations are actually comparable to those of external flows, as the literature seems to suggest. More specifically, the latter dynamics are largely unexplored in the specific context of Nigeria, and meaningful contributions could attempt to shed light on country-specific drivers of these migratory flows.
Finally, an interesting area of research which is somehow connected to the scope of this paper analyses the impact of digital technology on mobilization, both violent and peaceful, with important implications for policy making. Indeed, internet increases information availability and enhances communication, solving the collective action problem in some instances, while leading to secret coordination with anti-democratic objectives in others. Understanding through which channels, if any, mobilization works analogously to mobility, as well as which factors determine outcomes favorable to democratization rather than not is an extremely relevant question to answer.
Mara Faccio (Purdue University, NBER, ABFER, ECGI) and John J. McConnell (Purdue University) released a working paper this month which will definitely cause some stir with the general public. One thing is sure already: it made it onto our list of the most entertaining economics papers released this year. Titled “Death by Pokémon GO” it uses an event-study design to estimate the total incremental cost of playing Pokémon GO while driving in Tippecanoe County, Indiana.
Though the paper may seem funny at first, it does touch on some serious issues. It links the widespread use of smartphones and increases in app usage to increased car crashes and fatalities. The authors state that: “[…] [T]he possible connection between smartphone usage and vehicular crashes has been cited by the Insurance Information Institute as one explanation for the 16% increase in insurance premiums between 2011 and 2016.“ Faccio and McConnell also note that: “Attributing any increase in crashes and fatalities to smartphone usage and app availability is, of course, extraordinarily difficult given that many other factors also changed over the years in which both increased.”
Although not being the first to investigate the connection between the rise of the smartphones and vehicular crashes, Faccio and McConnell provide some novel insights by making use of an ingenious idea and providing robust results. By employing a difference-in-difference analysis that controls for a variety of confounding factors, Faccio and McConnell can show that crashes near PokéStops significantly increased from before to after July 6th, 2016 (when the game was released). The authors find that the costs associated with this increase in vehicular crashes range from $5.2 million to $25.5 million1 over the first 148 days following the release of the game. Extrapolation of these estimates to nation-wide levels yields a total cost ranging from $2.0 to $7.3 billion for the same period.
ITFD alum Mihai Patrulescu ’10 analyzes the Romanian market in an article for Emerging Europe.
“Over the past three years, the Romanian economy has recorded some of the fastest growth rates in the European Union, helped by a rapid expansion of consumer spending,” he writes. “During this period, retail sales have benefited from what can be considered as a perfect storm of growth catalysts.”
Mihai has joined Colliers International in October 2016 as Head of Strategic Analysis. Prior to this position, Mihai coordinated the economic research activities of UniCredit Romania, working for the bank between 2012 and 2016. During this period, he has focused on the Romanian economy as well as the CEE region, along with the banking system and financial markets. Prior to UniCredit, Mihai also worked as a research economist for Bancpost, the Romanian subsidiary of EFG Eurobank.
During 2015/2016, Mihai was seconded on assignment to the Milan Headquarters of UniCredit, working as a management consultant on the implementation of the bank’s strategic plan.
Mihai holds a Master’s in International Trade, Finance and Development from the Barcelona Graduate School of Economics. During his academic undertakings, he has focused on economic crises in emerging markets, and particularly their impact on financial systems. Mihai also holds a Bachelor degree from the Academy of Economic Studies in Bucharest.
By Cox Bogaards, Marceline Noumoe Feze, Swasti Gupta, Mia Kim Veloso
Almost a year since the UK voted to leave the EU, uncertainty still remains elevated with the UK’s Economic Policy Index at historical highs. With Theresa May’s snap General Election in just under two weeks, the Labour party has narrowed the gap from Conservative lead to five percentage points, which combined with weak GDP data of only 0.2 per cent growth in Q1 2017 released yesterday, has driven the pound sterling to a three-week low against the dollar. Given potentially large repurcussions of market sentiment and financial market volatility on the economy as a whole, this series of events has further emphasised the the need for policymakers to implement effective forecasting models.
In this analysis, we contribute to ongoing research by assessing whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of the Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance in explaining the uncertainty that ensued in the aftermath of the Brexit vote.
The UK’s referendum on EU membership is a prime example of an event which perpetuated financial market volatility and wider uncertainty. On 20th February 2016, UK Prime Minister David Cameron announced the official referendum date on whether Britain should remain in the EU, and it was largely seen as one of the biggest political decisions made by the British government in decades.
Assessment by HM Treasury (2016) on the immediate impacts suggested “a vote to leave would cause an immediate and profound economic shock creating instability and uncertainty”, and in a severe shock scenario could see sterling effective exchange rate index depreciate by as much as 15 percent. This was echoed in responses to the Centre for Macroeconomics’ (CFM) survey (25th February 2016), where 93 percent of respondents agreed that the possibility of the UK leaving the EU would lead to increased volatility in financial markets and the broader economy, expressing uncertainty about the post-Brexit world.
Resonating these views, the UK’s vote to leave the EU on 23rd June 2016 indeed led to significant currency impacts including GBP devaluation and greater volatility. On 27th June 2016, the Pound Sterling fell to $1.315, reaching a 31-year low against the dollar since 1985 and below the value of the Pound’s “Black Wednesday” value in 1992 when the UK left the ERM.
In this analysis, we assess whether the uncertainty in the aftermath of the UK’s vote to leave the EU could have been predicted. Using the volatility of Pound-Euro exchange rate as a measure of risk and uncertainty, we test the performance of one-step ahead forecast models including ARCH, GARCH and rolling variance. We conduct an out-of-sample forecast based on models using daily data pre-announcement (from 1st January 2010 until 19th February 2016) and test performance against the actual data from 22nd February 2016 to 28th February 2017.
Descriptive Statistics and Dynamic Properties
As can be seen in Figure 1, the value of the Pound exhibits a general upward trend against the Euro over the majority of our sample. The series peaks at the start of 2016, and begins a sharp downtrend afterwards. There are several noticeable movements in the exchange rate, which can be traced back to key events, and we can also comment on the volatility of exchange rate returns surrounding these events, as a proxy for the level of uncertainty, shown in Figure 2.
Figure 1: GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
Notably, over our sample, the pound reached its lowest level against the Euro at €1.10 in March 2010, amid pressure from the European Commission on the UK government to cut spending, along with a bearish housing market in England and Wales. The Pound was still recovering from the recent financial crisis in which it was severely affected during which it almost reached parity with the Euro at €1.02 in December 2008 – its lowest recorded value since the Euro’s inception (Kollewe 2008).
However, from the second half of 2011 the Pound began rising against the Euro, as the Eurozone debt crisis began to unfold. After some fears over a new recession due to consistently weak industrial output, by July 2015 the pound hit a seven and a half year high against the Euro at 1.44. Volatility over this period remained relatively low, except in the run up to the UK General elections in early 2015.
However, Britain’s vote to leave the EU on 23rd June 2016 raised investors’ concerns about the economic prospects of the UK. In the next 24 hours, the Pound depreciated by 1.5 per cent on the immediate news of the exit vote and by a further 5.5 per cent over the weekend that followed, causing volatility to spike to new record levels as can be seen in Figure 2.
Figure 2: Volatility of GBP/EUR Exchange Rate
Source: Sveriges Riksbank and authors’ calculations
As seen in Figure 1, the GBP-EUR exchange rate series is trending for majority of the sample, and this may reflect non-stationarity in which case standard asymptotic theory would be violated, resulting in infinitely persistent shocks. We conduct an Augmented Dickey Fuller test on the exchange rate and find evidence of non-stationarity, and proceed by creating daily log returns in order to de-trend the series. Table 1 summarises the first four moments of the log daily returns series, which is stationary.
Table 1: Summary Statistics
Source: Sveriges Riksbank and authors’ calculations
The series has a mean close to zero, suggesting that on average the Pound neither appreciates or depreciates against the Euro on a daily basis. There is a slight negative skew and significant kurtosis – almost five times higher than that of the normal distribution of three – as depicted in the kernel density plot below. This suggests that the distribution of daily returns for the GBP-EUR, like many financial time series, exhibits fat tails, i.e. it exhibits a higher probability of extreme changes than the normal distribution, as would be expected.
To determine whether there is any dependence in our series, we assess the autocorrelation in the returns. Carrying out a Ljung-Box test using 22 lags, as this corresponds to a month of daily data, we cannot reject the null of no autocorrelation in the returns series, which is confirmed by an inspection of the autocorrelograms. While we find no evidence of dependence in the returns series, we find strong autocorrelations in the absolute and squared returns.
The non-significant ACF and PACF of returns, but significant ACFs of absolute and squared returns indicate that the series exhibits ARCH effects. This suggests that the variance of returns is changing over time, and there may be volatility clustering. To test this, we conduct an ARCH-LM test using four lag returns and find that the F-statistic is significant at the 0.05 level.
For the in-sample analysis we proceed using the Box-Jenkins methodology. Given the evidence of ARCH effects and volatility clustering using an ARCH-LM test but lack of any leverage effects in line with economic theory, we proceed to estimate models which can capture this: ARCH (1), ARCH (2), and the GARCH (1,1). Estimation of ARCH (1) suggests low persistence as captured by α1 and relatively fast mean reversion. The ARCH(2) model generates greater persistence measured by sum of α1 and α2 and but still not as large as the GARCH(1,1) model, sum of α1 and β as shown in table 2.
Table 2: Parameter Estimates
We proceed to forecast using the ARCH(1) as it has the lowest AIC and BIC in-sample, and GARCH (1,1) which has the most normally distributed residuals, no dependence in absolute levels, and the largest log-likelihood. We compare performance against a baseline 5 day rolling variance model.
Figure 3 plots the out of sample forecasts of the three models (from 22nd February 2016 to 28th February 2017). The ARCH model is able to capture the spike in volatility surrounding the referendum, however the shock does not persist. In contrast, the effect of this shock in the GARCH model fades more slowly suggesting that uncertainty persists for a longer time. However neither of the models fully capture the magnitude of the spike in volatility. This is in line with Dukich et al’s (2010) and Miletic’s (2014) findings that GARCH models are not able to adequately capture the sudden shifts in volatility associated with shocks.
Figure 3: Volatility forecasts and Squared Returns (5-day Rolling window)
We use two losses traditionally used in the volatility forecasting literature namely the quasi-likelihood (QL) loss and the mean-squared error (MSE) loss. QL depends only on the multiplicative forecast error, whereas the MSE depends only on the additive forecast error. Among the two losses, QL is often more recommended as MSE has a bias that is proportional to the square of the true variance, while the bias of QL is independent of the volatility level. As shown in table 3, GARCH(1,1) has the lowest QL, while the ARCH (1) and rolling variance perform better on the MSE measure.
Table 3: QL & MSE
Table 4: Diebold- Mariano Test (w/5-day Rolling window)
Employing the Diebold-Mariano (DM) Test, we find that there is no significance in the DM statistics of both the QL and MSE. Neither the GARCH nor ARCH are found to perform significantly better than the 5-day Rolling Variance.
In this analysis, we tested various models to forecast the volatility of the Pound exchange rate against the Euro in light of the Brexit referendum. In line with Miletić (2014), we find that despite accounting for volatility clustering through ARCH effects, our models do not fully capture volatility during periods of extremely high uncertainty.
We find that the shock to the exchange rate resulted in a large but temporary swing in volatility but this did not persist as long as predicted by the GARCH model. In contrast, the ARCH model has a very low persistence, and while it captures the temporary spike in volatility well, it quickly reverts to the unconditional mean. To the extent that we can consider exchange rate volatility as a measure of risk and uncertainty, we may have expected the outcome of Brexit to have a long term effect on uncertainty. However, we observe that the exchange rate volatility after Brexit does not seem significantly higher than before. This may suggest that either uncertainty does not persist (unlikely) or that the Pound-Euro exchange rate volatility does not capture fully the uncertainty surrounding the future of the UK outside the EU.
Abdalla S.Z.S (2012), “Modelling Exchange Rate Volatility using GARCH Models: Empirical Evidence from Arab Countries”, International Journal of Economics and Finance, 4(3), 216-229
Allen K.and Monaghan A. “Brexit Fallout – the Economic Impact in Six Key Charts.” www.theguardian.com. Guardian News and Media Limited, 8 Jul. 2016. Web. Accessed: March 11, 2017
Brownlees C., Engle R., and Kelly B. (2011), “A Practical Guide to Volatility Forecasting Through Calm and Storm”, The Journal of Risk, 14(2), 3-22.
Centre for Macroeconomics (2016), “Brexit and Financial Market Volatility”. Accessed: March 9, 2017.
Cox, J. (2017) “Pound sterling falls after Labour slashes Tory lead in latest election poll”, independent.co.uk. Web. Accessed May 26, 2017
Diebold F. X. (2013), “Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests”. Dukich J., Kim K.Y., and Lin H.H. (2010), “Modeling Exchange Rates using the GARCH Model”
HM Treasury (2016), “HM Treasury analysis: the immediate economic impact of leaving the EU”, published 23rd May 2016.
Sveriges Riksbank, “Cross Rates” www.riksbank.se. Web. Accessed 16 Feb 2017
Taylor, A. and Taylor, M. (2004), “The Purchasing Power Parity Debate”, Journal of Economic Perspectives, 18(4), 135-158.
Van Dijk, D., and Franses P.H. (2003), “Selecting a Nonlinear Time Series Model Using Weighted Tests of Equal Forecast Accuracy”, Oxford Bulletin of Economics and Statistics, 65, 727–44.
Tani, S. (2017), “Asian companies muddle through Brexit uncertainty” asia.nikkei.com. Web. Accessed: May 26, 2017
ITFD alum Charlie Thompson ’14 is an R enthusiast who enjoys “tapping into interesting data sets and creating interactive tools and visualizations.”
ITFD alum Charlie Thompson ’14 is an R enthusiast who enjoys “tapping into interesting data sets and creating interactive tools and visualizations.” His latest blog post explains how he used cluster analysis to build a Coachella playlist on Spotify:
“Coachella kicks off today, but since I’m not lucky enough to head off into the California desert this year, I did the next best thing: used R to scrape the lineup from the festival’s website and cluster the attending artists based on audio features of their top ten Spotify tracks!”
Currently an Analytics Specialist at a tech startup called VideoBlocks, I create models of online customer behavior and manage our A/B testing infrastructure. I previously worked as a Senior Data Analyst for Booz Allen Hamilton, where I developed immigration forecasts for the Department of Homeland Security. I also built RShiny applications for various clients to visualize trends in global disease detection, explore NFL play calling, and cluster MLB pitchers. After grad school I worked as a Research Assistant in the Macroeconomics Department of Banc Sabadell in Spain, measuring price bubbles in the Colombian housing market.
I have an MS in International Trade, Finance, and Development from the Barcelona Graduate School of Economics and a BS in Economics from Gonzaga University. For my Master’s thesis I drafted a policy proposal on primary education reform in Argentina, using cluster analysis to determine the optimal regions to implement the program. I also conducted research in behavioral economics and experimental design, using original surveys and statistical modelling to estimate framing effects and the maximization of employee effort.
“There’s No Such Thing as a Free Lunch” – Milton Friedman
Source: Gary Markstein/Creators Syndicate
In their first lesson of economics, students are introduced to the concept of scarcity – an inherent condition in a world of limited resources – and, as a result, the existence of opportunity costs; Milton Friedman’s famous quote “There’s No Such Thing as a Free Lunch” echoes this idea that everything has a cost, even when it is not obvious. When it comes to government decisions, costs are often scrutinized: the cost of an investment, of giving (or not giving) a public service in concession or implementing a policy; however, the costs of political polarization are rarely analyzed.
What is the cost of political polarization?
Or, rather, which is the most valued asset lost for having political polarization? Certainty. In this essay, the author will provide arguments in favor of the hypothesis that the opportunity cost of the increasing gap between political attitudes of politicians towards major policy dimensions (trade, migration, gender, racial integration, public expenditure) is uncertainty and will discuss its negative effects on economic performance.
A first approach to studying the economic effects of uncertainty resulting from political activities is observing economic markets’ performance during electoral cycles. Brandon and Youngsuk (2012) estimated the effect of elections over corporate investment. Results indicate that, after setting control variables for investment opportunities and economic environment variables, corporate investment rates dropped, on average, by 4.8 percentage points the year prior to elections. In countries with polarization, the effect is expected to increase due to the risk of abrupt changes in policy. The changes may be moderate, for example: contract regulations, taxation, trade policy, or more drastic actions like expropriation of possessions and hostility towards non-supporters. Empirical evidence reveals that political polarization affects investment not only during electoral cycles, but also discourages long-term investments, with investors instead opting to minimize their risk and making short-term opportunistic solutions such as asset stripping, and intensive lobbying with state officials (Frye. 2002).
Other negative effects of polarization
Especially in countries with parties that exhibit diverging ideologies such as ex-communists and anticommunists, other negative effects of polarization are the imposed barriers to create consensus. There is a constant conflict over the economic reforms to be implemented, given the conflicting principles, and it does not allow politicians to reach agreements to effectively address economic crisis with coherent policies (Frye. 2002).
The struggle between opposing factions also has a detrimental effect on the quality of institutions by increasing the state officials’ incentives to make opportunistic decisions, for example populism, clientelistic relationships, bribing and interference of power groups in government policies, just to name a few
According to a growing mass of literature on the subject, when a country lacks strong institutions and has a polarized government, it will be more likely to default on sovereign debt. It is important to bear in mind that sovereign debt crises do not occur only when governments choose to default, as recent events have shown that crises can arise from investor’s uncertainty about a country’s ability or intentions to honor its responsibilities. Qian (2012) uses an economic model to show the dynamics between the quality of institutions, the level of government polarization and the sovereign default risk, for a sample of 90 countries. Her findings support the premise that the lack of strong institutions and a clear set of rules allows powerful groups to capture government and influence policies to their benefit, without considering their impact on other groups.
Additional evidence of the negative effects of polarization and weak institutions is found when combined with a globalized financial market. In particular, countries with low income and weak institutions are perceived as unreliable by investors and experience a threshold effect that will hinder their access to all the benefits of globalization, as presented by Alfaro, Kalemli-Ozcan and Volosovych (2008), as well as by Kose, Prasad and Taylor (2011).
Moreover, Broner and Ventura (2006) discuss the conditions under which globalization lead to higher financial market volatility. According to their model, the instability of domestic financial markets can be explained by: 1) uncertainty of governments’ behavior (incentives to default on foreign liabilities increased with globalization) and 2) the probability of a financial crisis (i.e., it depends largely on the nature of regulations and strength of judicial systems to enforce contracts). As a result of financial liberalization and the existence of the previously mentioned sources of uncertainty, the economy will alternate between two possible outcomes: an optimistic equilibrium (in which institutions are strong in enforcing contracts) or a pessimistic equilibrium (one with weak, opportunistic institutions). In a polarized government, the effect of the uncertainty sources would be amplified, potentially destroying the possibility of an optimistic equilibrium.
After analyzing polarized countries using these arguments, it is not a surprise to find that some countries have low levels of investment, slow economic growth, high volatility and recurring economic and institutional crises.
“There’s No Such Thing as a Free Lunch”… especially when it comes from a politician.
Layman, G. C., Carsey, T. M., & Horowitz, J. M. (2006). Party polarization in American politics: Characteristics, causes, and consequences. Annu. Rev. Polit. Sci., 9, 83-110.
Baldassarri, D., & Bearman, P. (2007). Dynamics of political polarization. American sociological review, 72(5), 784-811.
We want to know what the BGSE community is thinking and reading about the Brexit.
We invite all Barcelona GSE students and alumni to share their early reflections on the potential economic consequences of the UK’s recent vote to leave the EU. Did you focus on a related topic in your master project? Are you working at a think tank, central bank, or consulting firm where your projects will be impacted by this decision? Have you seen any articles or links that you found useful for understanding what lies ahead?
Here are a couple of pieces we’ve found to get the discussion going:
The BGSE participates in A Dynamic Economic and Monetary Union (ADEMU), a project of the EU Horizon 2020 Program. Last week, ADEMU researchers held a webinar to discuss the Brexit.
Europe has grown out of its crises when reason and solidarity have prevailed, but it has also been devastated by its crises when fear and nationalism have taken the lead. Brexit, in the aftermath of the euro crisis, brings this dichotomy back to the foreground. Since 2010 there have been important advances in the development of the Economic and Monetary Union (EMU) and flexible forms of participation have allowed other EU countries, reluctant to join the euro, to share the basic principles that define the EU and have a common presence in the interdependent global world.
According to the panelists, Brexit raises 3 crucial questions:
Should the EMU be accelerated to become a centre of gravity within the EU, or slowed down to avoid a centrifugal diaspora? If accelerated, how?
Should an ‘exit’ country be allowed free entry to the single market and other EU public goods without accepting freedom of movement?
Should the EU remain as it is, or increase its capacity to offer common public services (Banking Union, border security, research funding, environment, etc.), or limit its scope of activity to the EU single and integrated market?
– Joaquín Almunia (Former Vice-President of the European Commission, honorary president of the Barcelona GSE)
– Ramon Marimon (European University Institute and UPF – Barcelona GSE; ADEMU)
– Gorgio Monti (European University Institute; ADEMU)
– Morten Ravn (University College London; ADEMU)
Annika Zorn (European University Institute; Florence School of Banking & Finance)
Nobel Laureate and Barcelona GSE Scientific Council member Joseph Stiglitz shares some reflections in the wake of the Brexit decision
What are you thoughts on Brexit?
We want to know what the BGSE community is thinking and reading about the Brexit. Please share your ideas, favorite sources for analysis, or observations from economists you respect in the comments below.
Marco Antonielli ’12 (International Trade, Finance, and Development) is a consultant with Nathan Associates in London. Prior to this he was a consultant at the OECD in Paris and a research assistant at the Bruegel think tank in Brussels. The following piece by Marco originally appeared on Nathan’s website. (All opinion and analysis are only those of the author.)
In a global economy with fewer opportunities to industrialize, low-income countries will need to embed the service sector in their vision for inclusive growth.
Amid a gloomy global economic outlook and crashing commodity prices, low-income countries ended 2015 with the slowest growth since 2009, and remain in serious need of new sources of inclusive growth. One major challenge to achieving higher living standards stems from the vast income and productivity gaps within these countries and in relation to the rest of the world.
Large-scale industrialization has traditionally been viewed as the main solution for bridging these gaps, as well as a strategic objective to create jobs and support future growth. Yet latecomers to development may have embarked on a path on which manufacturing—arguably the most promising sector—is expanding slowly in absolute terms, and often shrinking in relation to GDP. The questions are then: why do low-income countries struggle to industrialize? And could alternative sectors such as services replace manufacturing as engines of inclusive growth?
Growing out of the Traditional Economy
Let’s take a step back. While all economies are characterized by varying degrees of productivity and dynamism among sectors and businesses, the low-income countries feature tremendous structural gaps within their economies. Most of the workforce is employed in informal and traditional agricultural businesses, while manufacturing is limited and not fully organized and the dynamic services are largely confined to the cities. Also the modern and formal agricultural businesses are not as widespread as they could be.
To escape poverty, millions of workers need to move from low-productivity sectors and businesses, mainly agriculture, to high-productivity ones, where they will find better and more secure jobs. The reallocation of resources to modern and dynamic sectors can generate positive transformation and help low-income countries achieve inclusive growth.
However, economic transformation can lead to labor and capital being reallocated to more inefficient activities. Recent studies have found that from a macroeconomic perspective, structural transformation (i.e., intersectoral movement of resources) can be a drag on growth for long periods of time, and this is part of the reason why the growth dynamics of low- and middle-income countries have been so diverse. Such a pattern is illustrated in figure 1. Observing the breakdown (“decomposition”) of aggregate productivity growth in the sum of sectoral components and a component accounting for cross-sectoral labor reallocation, it can be noted that between the 1990s and the 2010s Asian and Eastern European countries benefited from the structural transformation of their economies, while Latin American and Sub-Saharan African countries had the opposite experience. Developing countries are therefore not necessarily transforming well over their growth paths.
Figure 1—Decomposition of aggregate productivity growth, 1990–2008
CESEE: Central, Eastern and Southeastern Europe; CIS: Commonwealth of Independent States; LAC: Latin America and the Caribbean; MENA: Middle East and North Africa; SSA: Sub-Saharan Africa
Organized and modern manufacturing is commonly understood as the business where workers in informal or more traditional forms of agriculture should be reemployed. This is because, while manufacturing is not necessarily the most efficient sector in the economy, it can be a growth accelerator and engine of inclusive growth for at least three reasons. First, manufacturers in emerging economies can benefit from manufacturing technologies developed in more advanced countries, and can achieve fast productivity growth. Second, manufacturing can absorb unskilled labor—thus providing improved employment opportunities for agricultural workers in low-income countries. Finally, manufacturers can export their products, so their growth will not be confined by limited domestic demand. Tradability is key, because high productivity growth can quickly lead producers to lower their prices and shed labor and capital if they cannot scale up their sales in bigger markets.
Is Industrialization a Broken Engine?
Virtually all successful emerging economies in the past 30 years have industrialized by leveraging this potential. Manufacturing offers opportunities to diversify away from agricultural and other traditional products, and helps the country pull itself out of poverty. But is this growth trajectory still feasible for today’s developing countries?
In most countries, the share of jobs and GDP arising from manufacturing expands in the early stages of development, then peaks and starts shrinking as relative prices decline and the economy matures. As Dani Rodrik and others have recently argued, latecomers to development in Africa and Latin America are hitting the peak earlier in the process, and are starting to deindustrialize when manufacturing has exploited only part of its potential. Ghani and O’Connell, for example, explore this inverted-U relationship between the level of economic development and the industry’s share of total employment, in a panel of 100 countries. They show how, in recent times, jobs in industry have grown more slowly and shrunk earlier in the development process (figure 2). The engine of industrialization seems to be running out of steam.
According to Rodrik, this manufacturing decline is mainly due to the adverse effects of trade and globalization on low- and middle-income countries in Africa and Latin America in two respects. First, these countries struggle in the international goods market because of a decline in the relative price of manufacturing in advanced economies, where technological progress has pushed up efficiency and reduced the need for expensive labor. Second, low transport costs and low trade barriers expose them to hyper-cheap production from East Asia, effectively reducing the scope for “import substitution” to expand the boost in manufacturing exports to the wider economy. This would suggest that today’s low-income countries will need to wait until East Asia becomes expensive before they industrialize.
A competing theory is that the low-income countries have subscribed to a trade system that is altogether unfavorable to them. On the one hand, to get access to international markets they are required to forgo protectionist policies that foster import substitution and screen nascent industries from foreign competition during their early development (see e.g., Ha-Joon Chang). On the other hand, trade barriers to advanced markets like the EU are set low for raw materials such as coffee beans and cocoa pods but high for the products obtained from processing of materials—in these examples, roasted coffee and chocolate. This means that the entry points to industrialization of commodity-dependent countries are essentially shut down.
Figure 2—Is Industrialization Running out of Steam?
Both theories offer plausible explanations of why low-income countries struggle to industrialize. While more evidence on the causes of the problem is needed, it is increasingly clear that vast-scale industrialization has not featured in the development of most low-income countries. In contrast, the service sector has grown rapidly and absorbed lots of labor. Looking at Sub-Saharan Africa, for example, in the 15 years of this century . This pattern does not adequately represent how low-income countries grow and expand their productive capabilities, at least in that it does not capture the role of the variety and complexity of the products menu offered by these countries. Yet it can raise the question of how services can replace manufacturing as an engine of inclusive development. At least three routes can be identified.
First, there is a fringe of dynamic and tradable services that can boost the economy just as manufacturing does. Banking, customer services, and communications are examples of services which the ICT revolution has opened up to trade, and which can take low-income countries on a growth escalator, as the Indian boom has demonstrated. Crucially, investments in infrastructure, education, and human capital need to be made to facilitate development in these services. An alternative service attracting foreign demand with decent labor-absorption capacity is tourism.
Second, services are crucial inputs to manufacturing and there is evidence that their importance is growing. Hence cheap and efficient services such as transport and telecommunications can translate into stronger competitiveness of the tradable sector—both manufacturing and services.
Finally, the fact that manufacturing and services are becoming increasingly “blurred,” with services activities making up a higher share of manufacturing output, means that low-income countries could exploit a competitive edge on relevant service tasks. Moreover, these tasks can often be unbundled from merchandise production and traded along the global value chain. Logistics, marketing and post-sales services have been on the rise, not only in developed economies but also in developing ones. Furthermore, this trend could lead to a misinterpretation of statistics based on obsolete sector categories, effectively misleading our understanding of structural change.
In sum, the service sector offers new and interesting opportunities for growth, both through tradable services that plug directly into the global economy and through services that support competitiveness of manufacturing. In a global economy with fewer opportunities to industrialize, low-income countries will need to embed the service sector in their vision of inclusive growth and focus on the conditions that enable these opportunities.
Why are so many Catalans advocating independence? What would be the economic consequences of a potential separation from Spain? To find answers, BGSE students from the Master’s Program International Trade, Finance, and Development organized a talk on the economic effects of Catalan independence with Prof. Jaume Ventura. Prof. Ventura is a senior researcher at the Centre de Recerca en Economia Internacional (CREI), research professor at Barcelona GSE and member of the Wilson Initiative, a pro-Catalan-independence association of academics in the fields of economics and political science.
What is the optimal size of a state?
From a theoretical viewpoint, the ‘right’ size of a state is determined by a trade-off between two opposing forces. On the one hand, economies of scale and the border effect (i.e. political borders hamper trade) create a force towards larger countries. Such benefits are especially pronounced in areas such as economic markets and defense. On the other hand, heterogeneity of people’s preferences with respect to culture, the legal system or welfare, embodies a force for smaller countries. According to Prof. Ventura, these two forces have shaped the size and structure of the state in two waves throughout the history of globalization.
In the first wave, spanning from the Congress of Vienna to the beginning of the First World War, the number of countries more than halved, implying that states, on average, became larger. Political and economic integration proceeded hand in hand, and larger markets were created by sacrificing heterogeneity of preferences. After the Second World War, the second wave of globalization began. International trade reached higher levels and the number of countries multiplied to over 190. At the same time, international collaboration in the form of international organizations, such as the World Trade Organization, emerged. While this new era was characterized by political fragmentation regarding the nation state, larger markets were created through international cooperation and sacrificing economies of scope.
The creation of supra-national organizations enabled countries to exploit economies of scale irrespective of their size. As supra-national entities took over functions such as defense, which had previously mandated a larger state, even small states were able to thrive. At the same time, competencies such as culture, law and order and the welfare state remained on national agendas, as cultural globalization proceeds more slowly than economic globalization. All in all, it seems that the homogeneity of constituents’ preferences has become a more decisive determinant of a country’s size in the second wave of globalization.
The Catalan perspective
With this theoretical background in mind, Prof. Ventura turned to the specific case of Catalonia. First, he argued that small states in Europe, such as Norway and Switzerland, are competitive and wealthy. A potential Catalonian state with 7.5 million inhabitants would be larger than Denmark, Norway and Ireland, and only slightly smaller than Switzerland. Studies also find that the effect of size on economic growth depends on the degree of openness (Alesina, Spolaore and Wacziarg 2005). If a country is very open, size seems to have negative effects on growth. Catalonia, with a high degree of openness of 130%, could thus potentially grow faster if independent from Spain.
Next, Prof. Ventura focused on the long-run economic benefits of independence. If Catalonia became independent, this would imply giving up economies of scale arising from the union with Spain. However, these costs remain limited, in his opinion. The fixed costs of running a Catalan state have been generously estimated to be €2.793m which represents 1.4% of Catalan GDP, or €383 per Catalan citizen. Additionally, markets and defense have already been outsourced to the EU and NATO, suggesting that Catalonia would not lose out if it gave up the union with Spain (provided that it remained a member of EU and NATO). A major benefit for the Catalan economy would be the stop of fiscal transfers to the rest of Spain. Currently, taxes paid to the central government exceed public spending in Catalonia by €16.409m (8.4% of GDP). Moreover, current public capital in the region is the lowest throughout Spain. Public investment in Catalonia accounted for merely 8-9% of Spanish public spending, even though Catalonia contributes roughly 20% to the Spanish GDP.
In the short-run, there is a chance that costs might arise from retaliation by the Spanish state, and maybe others. However, Prof. Ventura estimates such costs, e.g. commercial boycotts, to be small and short-lived. He argues that retaliation would not be a sub-game perfect outcome, as most of the EU’s foreign investments and trade with Spain flows through Catalonia.
While the potential economic gains are substantial, Prof. Ventura emphasized that the heterogeneity of preferences between Catalonia and the rest of Spain remains the key reason behind Catalonia’s longing for independence. He pointed to his experience in the U.S., where the states enjoy a high degree of autonomy regarding education, justice, infrastructure, welfare and culture. In contrast, Spain’s central government dominates most aspects of public policy and previous attempts to increase Catalonia’s autonomy within Spain have failed.
While the future of Catalonia remains uncertain, Prof. Ventura advocated the right to self-determination and believes that “Catalan independence offers a unique window of opportunity to reform a bankrupt state and adapt it to modern times, both in Catalonia and Spain”.
Lecture summary by Tuomas Kari ’16 (Master’s in International Trade, Finance, and Development)
The former Chief Economist of the World Bank and member of Barcelona GSE Scientific Council Justin Yifu Lin visited Barcelona GSE on May 2nd to give a special talk to the Master students on a new approach to development policy, titled “New Structural Economics: The Third Wave of Development Thinking”. Professor Lin, who currently teaches at the National School of Development at the University of Beijing, outlined the history of development economics and its shortcomings. The goal of the lecture was to derive lessons for optimal policy and then expand upon the idea of new structural economics, the approach Prof. Lin himself advocates.
Structuralism and neoliberalism
Prof. Lin divided the history of development into two time periods: structuralism that was dominant from 1950 to the 1980s, and neoliberalism that has been the main viewpoint up to this day. Structuralism tended to assume that there were market failures that needed to be corrected with industrial policy, such as import substitution. The failure of these policies is well documented as the government-subsidized industries rarely survived at global markets and distorted the countries’ economies. Neoliberalist reaction emphasized deregulation to rid the economy of rent seeking and liberalization to let markets determine the allocation of resources. But this too failed in developing countries to reach steady growth. Often, liberalization led to the collapse of entire sectors, high unemployment and subsequent political unrest.
The main exception to these consensus policies throughout the last half a century have been the East Asian Tigers, Hong Kong, Singapore, South Korea and Taiwan, countries that followed a dual track of capitalist and state-directed policies and achieved unmatched growth rates. As these countries were initially too poor to afford expensive subsidies to heavy industry, they promoted production lower in the value chain, and even then only by piece-meal measures. According to Prof. Lin, this lack of better options guided the Tigers to good policies by accident.
Economic growth as a result of structural transformation
New structural economics is an attempt to study the determinants of economic structure and its evolution using neoclassical methods. Prof. Lin starts from the hypothesis that economic structure is endogenous to the country’s endowments and optimal policy guides the economy to activities where it enjoys comparative advantage. If a country attempts to transform its economy to activities other than those that utilize its endowments, this will only result in distortions, breaking down of market mechanisms and rent seeking. Optimal policy must start from the development of endowments (capital stock, human capital etc.) and only after try to deal with the production structure. As economic growth is ultimately a result of structural transformation, Prof. Lin argued that governments must engage in first building up the necessary endowments and then using industrial policy to help firms enter into business.
The preconditions for economic growth are having a functioning market economy efficiently allocate resources across sectors and firms, and a facilitating state that provides transitional support for firms entering and exiting the market and liberalizing the economy gradually using discretion. Lin claimed would lead to competitiveness, openness to trade, and strong fiscal and external accounts, which allow the economy to avoid crises and engage in countercyclical policies. Another benefit would be high returns to investment that provide incentives to save.
Room for more economic research
Prof. Lin promoted the setting up of Special Economic Zones to allow firms to do business free from distortions and also work as laboratories for the government to see what the comparative advantages of the economy are. He ended the lecture by proposing the development of theoretical models capable of explaining these dynamics as a fruitful avenue for the future economists in the audience.
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