“厄尔尼诺”现象还是“厄尔比索”现象？菲律宾的经济危机、贫困问题和收入分配

POLICY RESEARCH WORKING PAPER 2466 El Nihno or El Peso? In terms of its impact on poverty, the recent economic crisis in the Philippines was Crisis, Poverty, and Income more of an El Nino Distribution in the Philippines phenomenon than a financial crisis. Gaurav Datt Hans Hoogeveen The World Bank East Asia and Pacific Region Poverty Reduction and Economic Management Sector Unit October 2000  j i(d RFkrmcIi W)ORKING RP.\iAt 2466 Summary findings tIsing household survey data for 1998, Datt and clharacteristics aiffected the impact of the shocks. Hoogeveen assess the distributional impact of the recent Ownership of land made households maore susceptible to economic crisis in the Philippines. The results suggest the El Niflo shocks; higlher levels of educaition made that the impact of the crisis was modest, leading to a 5 househiolds more vulnerable to wage and employment percent reduction in average living standards and a 9 shocks. percent increase in the incidence of poverty-with larger The inpact of the crisis was greater in miore increases indicated for the depth and severity of poverty. commercially developed communities. Occupational The greater shock came from El Ninio rather than diversity within a household helped mitigate the adverse through the labor market. The labor market shock was imipact. progressive (reducing inequality) while the El Nifio shock Ther-e is some1w evidenice of consumlption smoothing by was regressive (increasinig inequality). the houselholds affected bv the crisis, but the poor were Not all households were equally vulnerable to the less able to protect theil consumption, which is a matter crisis-induced shocks. Household and community of policy concern. This paper-a product of the Poverty Reduction and Economic Management Sector Unit, East Asia and Pacific Region- is part of a larger effort in the region to better understanid the social impact of the crisis. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Taranaki M\4ailei, room MC8-142. telephone 202-458-7347, fax 202-522-1557, email address tmailei@;worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbaink.or-g/research/workinigpapers. The authors may be contacted at gdatt(/(worldbank.org or hhoogeveen(@.econ.vLi.nl. October 2000. (38 pages) Ihe Policy Research WXorking Paper Series disseminates the findings of zerk in progrress to encounrae the exchange of idleas alo(t deuelopment issues. An objective of the series is to get the fi/nciigs out quickly, even if the preseltationIs ar-e less than fully polished. The papers carry the names of the authors anid shonid be cited accordingly. Thet findi,s, inloepretations, and conclusios expressed inl tbis paper are entirely those of the authors. They do not necessarily represent thz viezv of the W"orld Baik, its Lxecutiz e Directors, or the countries they represenzt. Produced by rhe Policy Resear-ch Dissemiinationi Center  El Niino or El Peso? Crisis, Poverty and Income Distribution in the Philippines Gaurav Datt and Hans Hoogeveen' World Bank Washington D. C. We are grateful to the National Statistics Office, Manila, for their help with the provision of survey data. For useful comments and other forms of help we would like to thank Arsenio Balisacan, Benu Bidani, Jyotsna Jalan, Tamar Manuelyan-Attinc, Martin Ravallion, Hugh Waters, and in particular to Norbert Schady for many rounds of useful discussions. We would also like to thank participants at the poverty seminar at the Tinbergen Institute, Amsterdam for their comments.   Contents 1. Introduction ....................................................... I 2. What do we know about the distributional impact of the crisis .3 3. Data .6 3.1. 1998 APIS survey: an opportunity? .6 3.2. Direct self-reported measures of shock .7 3.3. Alternative specifications of the shock variable .8 4. Methodology..9 5. Results.14 5.1. What madefor lesser or greater impact?. 15 5.2. Impact on poverty and inequality .16 5.3. El-Nino or El-Peso?.21 5.4. Income vs. consumption impact .21 6. Conclusion.24 Table 1: Macro-economic indicators, by quarter: 1997-1998 .27 Table 2: The incidence of crisis-related economic shocks .27 Table 3: The estimated consumption and income models (1998 APIS) .28 Table 4: Impact of the crisis on consumption poverty and inequality .31 Table 5: Impact of the crisis on income poverty and inequality .32 Figure 1: Some key macro indicators in recent years, by quarter .33 Figure 2: Change in the cumulative distribution function due to the crisis .34 Figure 3: Percentage change in the cumulative distribution function due to the crisis .34 Figure 4: The relative magnitudes of income and consumption shocks .35 References ...................................................36 Table Al: Descriptive statistics of model variables (1998 APIS) .................................................. 38   1. Introduction When devaluation of the Thai Baht in July 1997 marked the beginning of the Asian financial crisis, the Philippine economy was in relatively good shape. In the three years prior to the crisis, the Philippines was not only enjoying favorable economic growth, inflation had returned to manageable levels after the double digit rates of 1988-91, the Peso was stable against the US dollar, net international reserves had grown to comfortable levels, and the fiscal budget was in surplus. Poverty rates had been declining; for instance, the incidence of poverty declined from 32% in 1994 to 25% in 1997 (Balisacan 1999, 2000).2 Nonetheless, the Thai financial crisis was rapidly transmitted to the Philippine economy and large capital outflows instantly created downward pressure on the Peso. The Bangko Sentral ng Pilipinas (BPS) initially tried to defend the Peso but as foreign reserves were insufficient to counter the massive capital outflows, the Peso depreciated from P26.40/$ in June 1997 to P37.20/$ in December 1997 to a peak level of P42.66/$ in January 1998. To ease the pressure on the exchange rate the government raised interest rates. In tandem with the depreciating exchange rate, interest rate on 91 day treasury bills rose from 10.5% in the first half of 1997 to a high of 19.1% in January 1998. Net domestic credit stopped growing and there was a sharp decline in investment (by 17% during 1998). With the setting in of the financial crisis by the last quarter of 1997, the Philippine economy stalled in 1998. Real GNP shrank by 0.5% in 1998 (Table 1). Per capita real GNP declined by 2.7%. The financial crisis was compounded by the worst drought in 30 years caused 2 The decline in the poverty headcount was much less by official estimates, from 41% in 1994 to 37% in 1997, though most of the difference seems attributable to the use of per capita income rather than per capita consumption as the welfare indicator. See Balisacan 1999, for further details. 1  by the El-Ninio beginning September 1997. This was reflected in the 1998 sectoral growth rates. Agriculture contracted the most, by 6.6%, while industrial production fell by 1.7%. With the slowdown in output growth came the slowdown in employment. Unemployment rates increased to double-digit levels during 1998 (averaging 10.1% in 1998 against 8.7% in 1997). Inflation also accelerated to double-digit levels. With the plummeting of agricultural output, food prices increased even faster than the general level of prices (Figure 1). The crisis also reduced government revenues, which constrained public spending despite an overall counter-cyclical fiscal policy adopted by the government. And real per capita spending on the social services declined in 1998. These macroeconomic developments raise a number of questions related to the potential impact of the crisis on living standards of the Filipino population. In this paper, we address the following four. i) How large was the impact in terms of the effect on average living standards and measures of absolute poverty? ii) How was the impact distributed across the population? What factors contributed to rendering some households more vulnerable to the adverse shock than others? iii) How did the impact on household consumption compare with that on household incomes? Is there any evidence of consumption smoothing by households? iv) Was the Philippines crisis more of an adverse weather phenomenon than a financial crisis? What was the relative contribution of the El-Nifio shock to the total impact? In addressing these questions, this paper limits its focus to the consumption or income dimension of the welfare impact. The crisis of course potentially affected other dimensions of 2  welfare, however their analysis remains beyond the scope of this paper.3 The paper is organized as follows. The following section reviews what is known about the impact of the crisis in the Philippines. In the course of this review, we also make some methodological comments on related literature for other countries in the region. Sections 3 and 4 respectively describe the data and our methodology. Our results are presented in Section 5. The final section sums up with some concluding observations. 2. What do we know about the distributional impact of the crisis? While it is generally believed that the Philippines escaped the worst of the regional financial crisis4, relatively little is known about the distributional impact of the crisis (which for the Philippines turned out to be a combination of financial and weather-related shocks). One strand of work for other countries in the region has involved comparisons of distributional parameters, including measures of absolute poverty, based on household survey data before and after (or during) the crisis.5 For the Philippines, the latest available household survey is the 1998 Annual Poverty Indicators Survey (APIS) conducted by the National Statistics Office (NSO).6 Using these data in conjunction with data from the 1997 Family Income and Expenditure Survey (FIES), Reyes, de Guzman, Manasan and Orbeta (1999) reported that per capita income declined 3 Some of the non-income effects may of course be mediated through changes in household incomes or consumption. An assessment of the income or consumption impact thus has some relevance for the potential magnitude of non-income effects too. 4 See for instance, World Bank (1999). 5 See, for instance, estimates in World Bank (2000). Some of this literature is also reviewed in Booth (1999). For recent estimates for Indonesia, see Suryahadi, Sudarno, Suharso, and Pritchett (1999). 6 A second round of the APIS for 1999 was also recently fielded by the NSO, though data from this survey are not yet available. 3  by 3.6% in nominal tenns and 12.1% in real terms.7 However, as Reyes et al. acknowledge, even these before-after comparisons are problematic for the Philippines due to non-comparability of the income and consumption modules across the two surveys (see below for details). The before-after comparisons also run into the problem of a misspecified counterfactual. Even for a systemic shock, "before" estimates may not be a good approximation of the estimates "in the absence of a shock". A different approach has been used for Thailand and Korea (Kakwani 1998, Kakwani and Prescott 1999) where the counterfactual level of an indicator of interest is constructed by obtaining a predicted value from past trends of the indicator up to the crisis. Thus, if y, is the value of, say, the poverty indicator in the crisis period t, and y, is its predicted value based on past trends, then a crisis index for the poverty indicator is defined as ((y, /yt) - 1) and it measures the percentage change in poverty due to the crisis. This approach, though unimplementable for the Philippines for lack of comparable post-crisis distributional data, is also methodologically problematic on two counts. First, it is not clear over what period should one estimate trends prior to the crisis, particularly so for a country such as the Philippines which has had a checkered history of booms and busts (Lim 1998). In the end, the choice of the estimation period often becomes an arbitrary expedient of data availability. Second, since the counterfactual is constructed using unconditional trends, the approach attributes a 100% of the departure from trend to the crisis, thus making no allowance for changes in other non-crisis determinants of living standards. 7There were some studies done before the APIS data became available in 1999. For instance, Reyes and Mandap ( 1999) used an existing CGE model to simulate the likely impact of the crisis on incomes. They found that the crisis would lead to a fall in average incomes of all deciles and an increase in the Gini ratio. No attempt was made to separate the El Nifno from the financial crisis effects, nor were there any simulated effects on consumption. There were also studies undertaken by the World Bank and UNDP, and by Lim (June 1998). These studies were done shortly after the financial crisis and in the midst of the El Niflo drought, and had to rely on secondary data to explore anticipated rather than actual effects. 4  There is also work on panel data-based analysis of the impact of the crisis for Indonesia. For instance, Beegle, Frankenberg and Thomas (1999) estimate how changes in per capita consumption and transitions into and out of poverty during 1997 and 1998 were related to a set of household and community covariates in 1997. Such panel data analysis however also comes with its own set of problems. First, a potential advantage of panel data is that we can eliminate potential bias due to any omitted observed or unobserved household level determinants of welfare, using household fixed or random effects. However, without an independent measure of the household-specific shock, the presumption is that everyone was hit by the crisis-induced shock, and this rules out the use of "difference-in-differences" estimation that has often been used in impact evaluation analysis.8 Second, while panel data holds the promise of providing a direct measure of welfare change, there is also the thorny issue of measurement error. It is not clear how much of the observed change in household welfare or transitions into or out of poverty are signal rather than noise. Thus, even with panel data, empirical determination of the distributional impact of the crisis is not easily resolved. There have also been estimates of self-rated poverty for the Philippines based on quarterly surveys conducted by the Social Weather Stations (Mangahas 1999). According to these surveys, the incidence of poverty9 averaged 59% for the period 1996-97 while the average for 1998 was 61%. Similarly, Reyes et al. (1999) reported an increase in self-rated poverty from 40% just before the crisis to 43% in January l999."0 Quite apart from the use of a very different 8 A useful discussion of the properties of this and other estimators commonly used in the impact evaluation literature can be found in Angrist and Krueger (1999). 9 The incidence of self-rated poverty is not calculated using a pre-determined poverty line but by asking households where would they place their family on a card marked with the words "poor" and "nonpoor" and a line in between. See Mangahas (1999) for further details. '