Trade and Employment From Myths to Facts - International Labour ...

Trade and Employment: From Myths to Facts
their own subjects (e.g. consumers, firms) in what has been called “generative social
science” (Epstein, 2006). Essentially, the argument is that if the researcher can build
a computerized society that has the same large-scale properties as the actual, legitimate
experiments can be run “en silica”, that is on computers. Here, realism is of the
essence: if the model conforms to some erstwhile theory, itself the product of an
oversimplified view of a social process, the simulation is of less value that one that
more accurately replicates the measured properties in the real economy.
Simulation methods are widely used in virtually every branch of scientific inquiry.
They escape the fundamental problems of econometrics of omitted variable
bias and reverse causality by providing a more complete account of the object of
analysis. On the other hand, the models have been criticized as “works of fiction”
by philosophers of science. Finally, as seen in detail below, different models yield
different results and it is therefore incumbent on policy-makers to make their own
judgment about the relative realism of the models at their disposal.
3.2.4 Overcoming implementation obstacles
As noted by Gibson (2008b), data in developing countries can be reliable, noisy
and/or unreliable according to whether there are errors in the data collection process
and whether these errors tend to cancel out. 14 Errors also result from changing definitions
as well as the standard index number or aggregation problem. Populations
tend to be more heterogeneous in developing countries and income is often badly
distributed, leading to problems with aggregating rich and poor. Most fundamentally,
aggregation problems are more likely to occur in developing countries because the
social structure is rapidly changing.
Governments and non-governmental organizations (NGOs) often lack budgets
to do an adequate job of collecting, cross-checking and validat ing data. The existence
of a large informal or traditional sector also causes significant problems, especially
in agriculture, which can make up more than half the economy. Auto-consumption
and barter are perennial problems, of course, and investment in the informal sector
is particularly difficult to track, often appearing in the national accounts as consumption
(Taylor, 1979, p. 23). Employment data, especially when produc tive sectors are
changing rapidly in response to trading opportunities, can be unreliable and tend to
cover urban areas only. With technocrats in short supply, data gathering may be hampered
by poorly trained or untrained field workers, especially for qualitative methods.
Cost-minimizing sample design will lead to over-sampling of urban households
(Deaton, 1995, p. 1790).
Gibson (2008b) lists specific sampling problems including: stratification and
cluster bias; groups of in dividuals with similar unobservable characteristics, such as
ability or entrepreneurship; weather; tastes; or prices. There is also selectivity bias,
14 See the special issue of the Journal of Development Economics on data problems in developing countries.
An overview is provided in Srinivasan (1994). There is no econometric test for unreliable data.
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