Towards a Prosperous and Productive Chiapas: Institutions, Policies, and Public-Private Dialog to Promote Inclusive Growth

Since the Zapatista revolution of January 1994, enormous amount of resources coming from the federal government have poured over Chiapas. The gap in years and quality of education has been reduced significantly; and road, port and airport infrastructure have undergone a dramatic transformation. And yet, the income gap between Chiapas and the rest of Mexico has only widened. To understand why, a multi-disciplinary team of twelve experts have devoted significant time and resources to study different aspects of the development dynamic of Chiapas. As a result, 5 base documents have been published analyzing Chiapas:

– Complexity profile
– Growth Diagnostic
– Institutional Diagnostic
– Poverty profile
– Pilot of productive dialogs and inclusive growth in an indigenous community

This report resumes the findings from these and articulates their corresponding recommendations into a policy plan.

According to our hypothesis, Chiapas is wedged in a low productivity trap. A modern production system, responsible for productivity increases, income and development elsewhere in the world, requires a number of complementary inputs or capacities that are absent in Chiapas. As a result, its economy consists of a few primary products of little or no technological sophistication, and a vibrant service industry fueled by public expenditure in its larger cities. In this situation, there are no incentives to acquire additional education or skills because there is no demand for them in the economy. As we have proved, the few that manage to emigrate earn salaries elsewhere in Mexico slightly above other migrants with similar qualifications. As it turns out, it is not about the Chiapanecos, it is about Chiapas.

To overcome the current dilemmas and spark the engine of growth, Chiapas needs to resolve its issues of coordination, connectivity and gradually promote economic activities of higher complexity. Yazaki, one of the few manufacturers present in Chiapas, is an example of the role of the state in helping the economy to overcome the chicken-and-egg dilemmas, providing the public goods required – in an initial push – by a more complex economy. Our recommendations are based in identifying the productive capabilities embedded within the current productive structure of Chiapas four largest urban agglomerations, and leveraging on them to board on different potential, more complex industries that use a similar base of knowledge. To conquer those industries and diversify its economy, Chiapas needs a public-private agency empowered to iteratively solve the issues and bottlenecks these potential industries face in each particular place. Public transport and housing policy can be used as means to incorporating the surrounding communities into the increasingly modern economies of urban centers. Special economic zones and agro-industrial parks can be used to spur productivity in those areas where labor and appropriability are the most binding constrains.

Special Economic Zones in Panama: Technology Spillovers from a Labor Market Perspective

Special Economic Zones (SEZ) have played an important role in Panama’s successful growth story over the previous decade. SEZ have attracted local and foreign investment by leveraging a business-friendly environment of low transaction costs, and created many stable, well-paid jobs for Panamanians. Beyond that, SEZ shall be assessed as place-based policy by their capacity to boost structural transformations, namely attracting new skills and more complex know-how not to be found in the domestic economy.

The aim of this paper is to evaluate the three largest SEZ in Panama:

• Colon Free Zone
• Panama-Pacific
• City of Knowledge

Our results suggest that SEZ have been successful as measured by static indicators, such as foreign investment, job creation and productivity. We also find that SEZ have boosted inflows of high-skill immigrants, who are most likely generating positive knowledge spillovers on Panamanians productivity and wages. However, significant legal instruments and institutional designs are preventing Panama from taking full advantage of the skill variety hosted at the SEZ. Complex immigration processes inhibiting foreigners from transitioning out of the SEZ, a long list of restricted professions and even citizenships considered as a national security concern, are hindering the flow of knowledge, keeping the benefits coming from more complex multinational companies locked inside the gates of SEZ.

Panama beyond the Canal: Using Technological Proximities to Identify Opportunities for Productive Diversification

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.

Shifting Gears: A Growth Diagnostic of Panama

Panama has been one of the fastest growing economies in the world over the previous decade. Growth has been spearheaded by the development of a modern service sector on the activities surrounding the Canal, and non-residential construction. Large public infrastructure projects and the private provision for infrastructure demanded by the service sector, have fueled growth and created a vibrant labor market for non-skilled workers.

Two warning signals hover over Panama´s stellar performance. The construction sector has been growing for a decade at a rate that is equivalent to doubling its stock of structures every four years. The demand for non-residential construction cannot grow indefinitely at a higher rate than the rest of the economy. This feeds into the second signal: Income inequality. In spite of the minor improvements registered over the accelerated-growth spell, Panama remains amongst the world´s top five most unequal countries.

Both warning signals point out to the need of further diversifying the Panamanian economy, and promoting economic activity in the provinces so as to deconcentrate growth and make it more inclusive.

We deployed our Growth Diagnostic methodology in order to identify potential binding constraints to that process. Skilled labor, necessary to gradually diversify into more complex and high value-added activities, is relatively scarce. This scarcity manifests into large wage-premiums to foreigners across all occupations, which are particular large within more complex industries.

Major investments in education have improved indicators of schooling quantitatively, but quality remains a major concern. We find that Panama’s immigration policies are preventing skills from spilling over from their special economic zones into the rest of the economy. On top of that, the list of professions restricted to Panamanians and other constraints on skilled labor flows, are constraining even further the pool of skills. As we document here, these efforts are not helping the Panamanian workers, quite the contrary.

We also find that corruption, and to a lesser extent, red tape, are other important factors that shall be addressed in order to allow Panama to shift the gears of growth, tackle inequality and continue growing at a fast pace.

Agglomeration Economies: The Heterogeneous Contribution of Human Capital and Value Chains

**Updated version of this paper published here**

We document the heterogeneity across sectors in the impact labor and input-output links have on industry agglomeration. Exploiting the available degrees of freedom in coagglomeration patterns, we estimate the industry-specic benefits of sharing labor needs and supply links with local firms. On aggregate, coagglomeration patterns of services are at least as strongly driven by input-output linkages as those of manufacturing, whereas labor linkages are much more potent drivers of coagglomeration in services than in manufacturing. Moreover, the degree to which labor and input-output linkages are reflected in an industry’s coagglomeration patterns is relevant for predicting patterns of city-industry employment growth.

Why is Chiapas Poor?

No matter which way you look at it, Chiapas is the most backward of any state in Mexico. Its per capita income is the lowest of the 32 federal entities, at barely 40% of the national median (Figure 1). Its growth rate for the decade 2003-2013 was also the lowest (0.2%),¹ causing the income gap separating Chiapas from the national average to increase from 53% to 60%. That is to say that today the average income for a worker in Mexico is two and a half times greater than the average in Chiapas. The two next poorest states, Oaxaca and Guerrero, are 25% and 30% above Chiapas.² According to the Instituto Nacional de Estadística y Geografía de México (INEGI, National Institute of Statistics and Geography), Chiapas is also the state with the highest poverty rate (74.7%) as well as extreme poverty (46.7%).³

These major differences in income levels among Mexican federal entities are reproduced as in a fractal within Chiapas. In fact, while the wealthiest entity (Mexico City) is wealthier than the poorest (Chiapas) by a factor of six, the difference within Chiapas between the wealthiest municipality (Tuxtla Gutiérrez) and the poorest (Aldama and Mitontic) is by a factor greater than eight.⁴

As there are different “Mexicos” within Mexico,⁵ in Chiapas there are also different sorts of Chiapas (Figure 2). Income per capita in Tuxtla Gutiérrez, to the right of the distribution, is five standard deviations above the state average. Next comes a series of intermediate cities, San Cristóbal de las Casas, Comitán de Domínguez, Tapachula, and Reforma, between two and a half to four standard deviations above the average. The remaining municipalities of Chiapas follow (122 in all), clustered to the far left of the distribution. In addition, both the statistics available at the town level and our visits to various municipalities in Chiapas seem to indicate that significant differences also exist within these municipalities.

From this vantage point, questions as to why Chiapas is poor, or what explains its significant backwardness compared to other areas of Mexico, become much more complex. Why do some regions in Chiapas have high income levels, while other regions remain stagnant, fully dependent on federal transfers and deprived from the benefits of modern life?

¹ This is the non-oil gross domestic product growth rate reported by INEGI, considered to be more representative of the productive spectrum. In any case, the overall rate of growth in Chiapas (-0.2%) was also the lowest amongst all Mexican entities for the decade.
² Refers to non-oil GDP; in general terms, Guerrero and Oaxaca are 19% and 16% above Chiapas.
³ Growth figures refer to the decade 2003-2013, poverty figures are those published by INEGI for 2012.
⁴ Comparisons of Chiapas municipalities are made based on the data from the 10% sample of the 2010 Population Census, which is representative at the state level.
⁵ This is a reference to the report, A tale of two Mexicos: Growth and prosperity in a two-speed economy, McKinsey Global Institute (2014).

Poverty, coverage of the Missions and social protection needs for the economic reform of Venezuela

Even before the oil price crisis began in 2014, progress in reducing poverty in Venezuela had ceased and official figures showed that. According to the INE, between 2008 and 2013, the percentage of the population living in poverty remained almost the same, going from 33.1% to 34.2%.

Estas son las últimas cifras oficiales de pobreza de ingreso que disponemos ya que la última contabilización oficial de porcentaje de población en situación de pobreza es la del segundo semestre de 2013^[1]. A partir de ese momento la descripción social de la pobreza en Venezuela ha dependido de estudios independientes realizados entre otros, por un consorcio de varias universidades del país^[2] que dan cuenta de la evolución de la pobreza entre 2014 y 2015 (ENCOVI, 2014 y 2015), años donde se precipitaron los precios del petróleo hasta un tercio de lo que llegaron a ser durante 2008 acelerando un proceso de deterioro en los indicadores de desempeño económico y bienestar del hogar.

Según estas fuentes independientes de información la pobreza de ingresos en Venezuela habría llegado hasta un 55% en 2014 y 76% en 2015. Cifras que por sí solas hablan de la necesidad diseñar un plan de reformas económicas y sociales para hacerle frente al impacto social de la caída de los precios del petróleo, así como al conjunto de factores, más allá de los precios del crudo, que han llevado al país a tres años continuos de recesión y aumento de la pobreza.

En atención a lo anterior, el presente trabajo se enmarca dentro del conjunto de ejercicios de investigación que son necesarios para poder diseñar un programa de estabilización económica y su correspondiente plan de protección social. En ese sentido, en lo que sigue trataremos de dimensionar el número de familias que necesitarían formar parte de este potencial plan de protección social.

Para ello nos valdremos como fuente de información de la ENCOVI 2014 y 2015, encuestas desde las cuales no sólo tenemos información para contabilizar los hogares e individuos en estado de necesidad, sino además las coberturas probables de los programas sociales (Misiones) que actualmente implementa el gobierno de Venezuela, para de esta forma estimar; en primer lugar, las familias en situación de pobreza que reciben beneficios sociales; en segundo lugar las que estando en esa condición de pobreza no los reciben y; por último, y con miras a la reforma de los programas y la introducción de elementos de progresividad distributiva, los beneficiarios que aún sin ser población objetivo, por no estar en situación de pobreza, son receptores de transferencias, pensiones o becas por parte del Estado.

Adicionalmente a lo anterior, las políticas de control de cambio y la regulación de los precios, aunado a los problemas de abastecimiento, han hecho que los precios de los bienes a los que tienen acceso los distintos grupos sociales varían según si se adquieren en los mercados controlados o en los informales. Estos diferenciales de precios son muy importantes y están generando impactos distributivos difíciles de estimar, pero fundamentales para entender las necesidades de protección social que requieren los hogares para cubrir la canasta de productos básicos.

Es por ello que este trabajo también se propone describir a muy alto nivel los problemas distributivos generados por los diferenciales de precios. Si bien probablemente no sea posible llegar a conclusiones definitivas, al menos plantearemos lo relevante del tema para entender como la escasez de productos y las regulaciones de precios han introducido un conjunto de distorsiones en los precios y en el acceso a los bienes esenciales y presentaremos algunos de los dilemas y preguntas que estas distorsiones generan al momento de analizar la capacidad de satisfacer necesidades básicas en Venezuela.

Governance and the Challenge of Development Through Sports: A Framework for Action

Previous papers such as Russell, Barrios & Andrews (2016), Guerra (2016), and Russell, Tokman, Barrios & Andrews (2016) have aimed to provide an empirical view into the sports economy. This proves to be a difficult task, given the many definitions of ‘sports’ and data deficiencies and differences in the sports domain (between contexts and over time). The emerging view in these previous papers provides interesting information about the sports sector, however: it shows, for instance, that different contexts have differently intensive sports sectors, and that sports activities overlap with other parts of the economy. This kind of information is useful for policymakers in governments trying to promote sports activities and use sports to advance the cause of broad-based social and economic development.

This paper is written with these policymakers in mind. It intends to offer a guide such agents can use in constructing sports policies focused on achieving development goals (what we call development through sports^[1]), and discusses ways in which these policymakers can employ empirical evidence to inform such policies.  

The paper draws on the concept of ‘governance’ to structure its discussion. Taking a principal-agent approach to the topic, governance is used here to refer to the exercise of authority, by one set of agents, on behalf of another set of agents, to achieve specific objectives. Building on such a definition, the paper looks at the way governmental bodies engage in sports when acting to further the interests of citizens, most notably using political and executive authority to promote social and economic development. This focus on governance for development through sports (asking why and how governments use authority to promote sports for broader social and economic development objectives^[2]) is different from governance of sports (which focuses on how governments and other bodies exercise authority to control and manage sports activities themselves), which others explore in detail but we will not discuss.^[3]

The paper has five main sections. A first section defines what we mean by ‘governance’ in the context of this study. It describes an ends-means approach to the topic—where we emphasize understanding the goals of governance policy (or governance ends) and then thinking about the ways governments try to achieve such goals (the governance means). The discussion concludes by asking what the governance ends and means are in a development through sports agenda. The question is expanded to ask whether one can use empirical evidence to reflect on such ends and means.  One sees this, for instance, in the use of ‘governance indicators’ and ‘governance dashboards’ in the international development domain. A second section details the research method we used to address these questions. This mixed method approach started by building case studies of sports policy interventions in various national and sub-national governments to obtain a perspective on what these policies tend to involve (across space and time). It then expanded into an analysis of sports policies in a broad set of national and sub-national governments to identify common development through sport ends and means.  Finally, it involved experimentation with selected data sources to show how the ends and means might be presented in indicators and dashboards—to offer evidence-based windows into development through sports policy regimes.

Based on this research, sections three and four discuss the governance ends and means commonly pursued and employed by governments in this kind of policy process. The sections identify three common ends (or goals)—inclusion, economic growth, and health—and a host of common means—like the provision of sports facilities, organized activities, training support, financial incentives, and more—used in fostering a development through sports agenda. Data are used from local authorities in England to show the difficulties of building indicators reflecting such policy agendas, but also to illustrate the potential value of evidence-based dashboards of these policy regimes. It needs to be stated that this work is more descriptive than analytical, showing how data can be used to provide an evidence-based perspective on this domain rather than formally testing hypotheses about the relationship between specific policy means and ends. In this regard, the work is more indicative of potential applications rather than prescriptive. A conclusion summarizes the discussion and presents a model for a potential dashboard of governance in a development through sports policy agenda.

More Goals, More Growth? A Take on the Mexican Sports Economy through the Economic Complexity Framework

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.
 

Keeping One’s Eye on the Ball: Exploring the Intensity of Sports Activities across Europe

As described in Russell, Barrios & Andrews (2016), past attempts to understand the sports economy have been constrained by a number of data limitations. For instance, many of these accounts use revenues when value added measures would be more appropriate. Similarly, many accounts use top-down definitions that result in double counting and an inflated estimate of the size of the sports economy. More importantly, past accounts have focused most of their efforts estimating the overarching size of the sports economy. Constrained by aggregated data that groups a wide range of sports-related economic activities together, they primarily discuss the size of the sports-related economic activity. Their focus on answering the question of “How big?” conceals substantial differences between activities. Core sports activities, such as professional sports teams, behave very differently than activities, like sporting goods manufacturing that are closer to the periphery of the sports economy. Likewise, there are even important differences amongst core sports activities. Professional sports teams are very different than fitness facilities, and they might differ in different respects.

Guerra (2016) demonstrates that, when detailed, disaggregated data are available, the possibilities to analyze and understand the sports are greatly increased. For instance, Guerra (2016)  were able to conduct skills-based analyses, magnitude analyses, employment characterizations, geographic distribution analyses, and calculations of the intensity of sports activities. The sector disaggregation, spatial disaggregation, and database complementarity present in the Mexico data used in that paper therefore enables a more detailed and nuanced understanding of sports and sports-related economic activity.

Data with characteristics similar to those found in Mexico are few and far between. We have, unfortunately, been unable to completely escape such data limitations. However, we have compiled and analyzed a large array of employment data on sports-related economic activities in Europe. In the paper that follows, we describe our analyses of these data and the findings produced.

Section 1 begins with a discussion of employment in sports and an explanation of why we chose this variable for our analyses. Section 2 provides an overview of the data used in this paper particularly focusing on the differences between it and the Mexico data discussed in Guerra (2016). It also describes the methodology we use. We analyze these data using one of two related measures to understand the intensity of sports-related activities across different geographic areas in countries. We also construct measures at the level of a single country in order to compare across entire economies. At the international level, we adopt the revealed comparative advantage (RCA) measure that Balassa (1965) first developed to analyze international trade. Within specific countries, however, we use a population-adjusted version of the RCA measure known as RPOP. Section 3 presents the most relevant findings and Section 4 discusses their limitations. Section 5 concludes with the lessons learned and avenues for future research. While there are limitations on these analyses, they can give policymakers a better understanding of the distribution and concentration of sports across space. Such information can serve as an important input for sports-related investment decisions and other sports-related policies.