Labor informality, associated with low productivity and lack of access to social security services, dogs developing countries around the world. Rates of labor (in)formality, however, vary widely within countries. This paper presents a new stylized fact, namely the systematic positive relationship between the rate of labor formality and the working age population in cities. We hypothesize that this phenomenon occurs through the emergence of complex economic activities: as cities become larger, labor is allocated into increasingly complex industries as firms combine complementary capabilities derived from a more diverse pool of workers. Using data from Colombia, we use a network-based model to show that the technological proximity (derived from worker transitions between industry pairs) of current industries in a city to potential new complex industries governs the growth of the formal sector in the city. The mechanism proposed has robust strong predictive power, and fares better than alternative explanations of (in)formality.

Cities thrive through the diversity of their occupants because the availability of complementary skills enables firms in the formal sector to grow, delivering increasingly sophisticated products and services. The appearance of new industries is path dependent in that new economic activities build on existing strengths, leading cities to both diversify and specialize in distinct areas. Hence, the location of necessary capabilities, and in particular the distance between firms and people with the skills they need, is key to the success of urban agglomerations. Using data for Colombia, this paper assesses the extent to which cities benefit from skills and capabilities available in their surrounding catchment areas. Without assuming a prioria a definition for cities, we sequentially agglomerate the 96 urban municipalities larger than 50,000 people based on commuting time. We show that a level of agglomeration equivalent to between 45 and 75 minutes of commuting time, corresponding to between 62 and 43 cities, maximizes the impact that the availability of skills has on the ability of agglomerations to generate formal employment. Smaller urban municipalities stand to gain more in the process of agglomeration. A range of policy implications are discussed.

The prevalence of many urban phenomena changes systematically with population size¹ . We propose a theory that unifies models of economic complexity^2,3 and cultural evolution⁴ to derive urban scaling. The theory accounts for the difference in scaling exponents and average prevalence across phenomena, as well as the difference in the variance within phenomena across cities of similar size. The central ideas are that a number of necessary complementary factors must be simultaneously present for a phenomenon to occur, and that the diversity of factors is logarithmically related to population size. The model reveals that phenomena that require more factors will be less prevalent, scale more superlinearly and show larger variance across cities of similar size. The theory applies to data on education, employment, innovation, disease and crime, and it entails the ability to predict the prevalence of a phenomenon across cities, given information about the prevalence in a single city.

The economy of Panama has thrived for more than a decade, based on a modern service sector on the activities surrounding the Canal. Panama has inserted its economy into global value chains, providing competitive services in logistics, ship handling, financial intermediation, insurance, communication and trade. The expansion of the modern service sector required significant non-residential construction, including office buildings, commercial outlets, warehouses, and even shopping malls. Large public infrastructure projects such as the expansion of the Canal, the Metro, and Tocumen airport, have provided an additional drive and paved the road for productive diversification. But productive diversification does not spread randomly. A country diversifies towards activities that demand similar capacities than the ones already in place. Current capabilities and know-how can be recombined and redeployed into new, adjacent activities, of higher value added.

This report identifies productive capabilities already in place in Panama, as signaled by the variety and ubiquity of products and services that is already able to manufacture and provide competitively. Once there, we move on to identifying opportunities for productive diversification based on technological proximity. As a result, we provide a roadmap for potential diversification opportunities both at the national and sub-national level.

In order to appropriately understand the sports sector, its magnitude, embeddedness in the economy, and strategic value, it is necessary to develop a framework through which to study it. Having a standardized and comprehensive methodology to analyze the sports sector will allow policymakers, academics, and other stakeholders to look at the sports sector at a new level of detail and rigor.

Previous work has outlined the numerous data quality and aggregation challenges currently present in the sports economy literature (Russell, Barrios & Andrews 2016). In light of these challenges, this paper attempts to build on the suggested categorization of the sports industry and develop a sound strategy to analyze the sector through an empirical exercise in a specific context: the Mexican Economy.

To this end, we first attempt to understand how connected the sports sector is to other activities in the economy and identify which sectors share similar know-how with m1. Additionally, we attempt to determine the relative magnitude of the sports sector through variables such as value added and employment.

Similarly, we consider study the spatial considerations around sports related economic activities at a subnational level. The advancement of spatial economics has allowed us to understand a new dimension of how an economic sector can develop and how characteristics inherent to a given geography can play a role in determining why some activities end up appearing and developing in the places they do.

Lastly, some descriptive and regression analysis efforts in this paper enabled us to better understand and characterize the sports sector. Such exercises allow us to learn what type of workers typically comprises the sports sector, and whether such profile is different across the different categories of sports activities. Among the variables analyzed I the descriptive exercise, we can look at education level and wages–among others–of those who work on this sector, and compare them to the overall employed population.

This paper is structured as follows: Section 1 will make the case for how publicly available data in Mexico meets the level of detail required for this type of study. Section 2 will look at the way in which the sports sector is nested in the overall economy. Section 3 studies the magnitude of the sports sector through different metrics. Section 4 looks at the type of jobs that comprise the sports sector. Section 5 looks at the differences in intensity of sports activities and early work on its potential causal roots. Section 6 provides some conclusions.

The literature on Dutch disease is extensive when it comes to documenting the negative impacts of natural resource exports on non-resource tradable goods as an aggregate. Little has been said on the impact of natural resources on non-resource export concentration, either from a broad perspective or at the product level.

We explore this relationship using a variety of non-resource export concentration indexes for the period 1985 - 2010. We find significant evidence indicating that countries with high share of natural resources in exports tend to have less diversified non-resource export baskets.

Furthermore, using highly disaggregated data at the product level we study what type of products are more likely to thrive or suffer in resource rich countries. We find that capital intensive goods tend to have larger shares on the non-resource export basket when natural resources are high.

We also find that homogeneous goods make for a larger share of the non-resource export basket the lower their technological sophistication. For differentiated goods the pattern is reversed: they tend to make for a larger share of the non-resource export basket, the higher they are in the technology scale.