# **5. Conclusion: Implications of our results**

Even with the caveats and limitations discussed in Section 3, our model appears to yield reasonable and intuitively appealing results, based on a transparent and flexible approach using macro projections and household survey data that are typically available in a developing country. The value that our model _adds_ to the simpler models (e.g. POVSTAT or elasticity based methods that are most commonly used in the World Bank) is the ability to analyze potential distributional impacts in detail, linking these changes to the channels through which the impact of the crisis is likely to flow. The application of our model to Philippines yields some useful insights relevant to the country, in three main areas discussed below: monitoring of impacts of the crisis, providing deeper insights into who are likely to be affected by the crisis, and how that in turn can inform the design of policies to mitigate impacts.

_Firstly_ , our results suggest that in the Philippines, a select few indicators are prime candidates to monitor as “leading” or real‐time indicators for the poverty and distributional impact of the financial crisis, or of future shocks with macroeconomic impacts similar to this crisis. To be useful for rapid monitoring of poverty and distributional impacts, the indicators must be easily obtained and measurable, sensitive to changes in economic conditions, and correlated with changes in poverty and distribution. By these criteria, movement of _wages and employment in manufacturing and services_ , changes in _remittance flows from abroad_ and the movement of _prices_ (particularly food prices) can serve as useful indicators for rapid monitoring of economic shocks in Philippines. It is important to notice, however, that data on wages is only collected in the October round of the Labor Force survey and made available with a large time gap. Inclusion of monitoring questions on wages in other LFS quarters, together with timelier processing and publication of the information could significantly enhance the government’s capacity to monitor potential poverty and distributional impacts of macroeconomic shocks.

Why these indicators? From our analysis, the labor market emerges as the major source of impact in Philippines, with the effects being transmitted through loss of employment in certain sectors (primarily manufacturing) and lower labor earnings as output growth slows. Another channel of impact is remittance flows, since remittance growth in 2010 is expected to fall below what was expected before the crisis had occurred. Any rapid, unexpected change in food (mainly rice) prices could also have a significant impact on poverty – at least in the short‐run when wages would lag behind food prices. These indicators also have the advantage of being relatively easy to monitor – with administrative or financial data (for aggregate remittance flows), and quick market or household surveys (prices and wages).

Tracking wages as a part of rapid monitoring would be particularly important. First, growth slowdown in a country like Philippines is more likely to be manifested in lower earnings rather than open unemployment – a hypothesis supported by our results as well. Second, while wages represent only a _part_ of the labor earnings from a sector, wages are much easier to track (e.g. through quarterly labor force or quick enterprise surveys) than income from household enterprises and self‐employment. Wages are also likely to be reasonably correlated with broader measures of labor income, particularly in the manufacturing sector where most of the labor market impact is expected.

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