by Alan A. Cajes, PhD
In an article entitled Development Economics, Debraj Ray discusses and explains some concepts that are central to the study of development economics, which studies the economies of the developing countries. The concepts include the conventional growth theory (movement to a steady state), the notion of convergence (getting some parameters right), nonconvergence viewpoint (movement along different paths due to differences in histories, institutions, etc.), and the issues related to the microeconomics of development (credit market, collective action for public goods, conflicts, institutional effects).
Although the author describes the concepts and how such concepts affect our understanding of development path of nations, Ray does not take any side. What can be inferred from the discussion is the non-empirical approach in discussing the concepts. The concepts, for instance, could not be used to assess the predicament of the Philippines as a national economy. Some of the concepts are explained via the experiences of different economies that have varied historical and related experiences. The author, however, points to the direction of empirical research in development economics. As researchers use information and communication technology (ICT)-aided empirical studies, there is a direction towards induction based on observations and hard knowledge.
I learned a lot while trying to grasp the various concepts presented in the article. This reminds me of a Facebook post by an economist from another university claiming that economics is moving away from theoretical analysis (theory explains the data) to data-based analysis (facts first, theory later). This also reminds me of Dr. Cielito Habito’s “PiTik” test or the use of key economic indicators like “presyo, trabahao and kita” (prices, jobs and incomes).
In one session where I had the opportunity to sit down with friends of Dr. Habito, he explained to us the other dimensions of economic development. He did not begin with theories or concepts. He showed us health and environment-related data. One has something to do with the issue of sustainability. Overfishing and extensive farming, which do not respect the ecological limits, will eventually constrain production, and then later the market prices.
Thus, the challenge posed by climate change, sea-level rise and related disasters will have to be factored in by specific communities and local government units (LGUs) because the negative effects are differentiated. Specific data on these issues will have to be gathered and analyzed so that these inform local policies and decisions.
I affirm this observation given some experiences I have in conducting natural resources and vulnerability assessments of some LGUs with funding support from the Climate Change Commission. What is evident is that communities need to generate quantitative data on the ground to inform economic policies. Thus, data science and data analytics is gaining ground as a tool for analysis by various disciplines, including economics.
