rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5.  The Investment Equation with Sensitivity Checks**

||**OLS**|**OLS**|**OLS**|**OLS**|**OLS**|**OLS**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Loan|0.155***|0.155***|0.155***|0.140***|0.155***|0.138***|
||(9.234)|(9.224)|(9.149)|(8.342)|(9.171)|(8.102)|
|Trade credit|0.079*|0.079*|0.089**|0.073*|0.080*|0.081**|
||(1.899)|(1.899)|(2.134)|(1.813)|(1.918)|(1.981)|
|UCA|0.032***|0.032***|0.032***|0.033***|0.032***|0.033***|
||(4.475)|(4.493)|(4.484)|(4.455)|(4.477)|(4.501)|
|sales/lagged K|0.010***|0.010***|0.010***|0.011***|0.010***|0.011***|
||(6.914)|(6.893)|(6.864)|(6.907)|(6.923)|(6.837)|
|CF/lagged K|0.107***|0.106***|0.107***|0.110***|0.107***|0.110***|
||(5.510)|(5.490)|(5.477)|(5.554)|(5.519)|(5.508)|
|Loss of sales due to electricity||0.196||||0.228|
|||(1.400)||||(1.600)|
|Loss of sales due to transportation||-0.109||||-0.076|
|||(-0.370)||||(-0.245)|
|Road density||-0.115||||0.175|
|||(-0.226)||||(0.349)|
|autonomy in investment|||0.036|||0.033|
||||(1.334)|||(1.161)|
|CEO schooling||||0.019***||0.020***|
|||||(3.791)||(3.931)|
|CEO appointed by gov’t||||-0.055**||-0.052**|
|||||(-2.564)||(-2.400)|
|CEO also board chairman||||0.055***||0.051***|
|||||(2.892)||(2.638)|
|CEO wage/worker wage||||0.002||0.002|
|||||(1.394)||(1.410)|
|CEO has incentive plans||||0.071||0.073|
|||||(1.097)||(1.121)|
|PR protection|||||0.026|0.036|
||||||(0.343)|(0.466)|
|government help|||||0.037|0.031|
||||||(0.727)|(0.595)|
|ETC|||||0.815|1.221|
||||||(0.383)|(0.582)|
|Number of observations|10,207|10,207|10,106|9,945|10,207|9,866|
|Adjusted R2|0.079|0.079|0.080|0.084|0.079|0.084|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies, ln(GDP per capita), ln(city pop), ln(firm age),

46
The analytical framework underlying the simulations presented in this paper is largely the same as in <u>Azevedo, Hasan et al. (2021), with one important extension – the new model allows for systems to be partially closed,</u> something that the previous model did not account for. As in the conceptual framework underpinning the previous simulations, we can think about the expected learning loss through two key channels: (i) the learning that will not take place while schools are closed, and (ii) the already acquired learning that will be lost or forgotten when students become less engaged with schooling. Additionally, the empirical methodology underlying the simulations is the same as documented in <u>Azevedo, Hasan et al. (2021). Details about the</u> framework and empirical methodology can also be found in Annex B.

Table 3 below describes the key input parameters on school closures, mitigation effectiveness, and school productivity used in the model to simulate the learning and earning outcomes under different scenarios.

# **Table 3. Parameters for simulations by income level**<sup>**16**</sup>

||**Global**|**High-**<br>**Income**|**Upper-**<br>**Middle-**<br>**Income**|**Lower-**<br>**Middle-**<br>**Income**|**Low-Income**|
|---|---|---|---|---|---|
|**A. Learning gains or school productivity (in**<br>**HLOpoints/year)**<sup>**17**</sup>|39|50|40|30|20|
|_Optimistic Scenario_||||||
|B1. Share of the system affected over observed<br>period (24 months)|42.9%|38.1%|52.7%|42.5%|34.4%|
|C1. Mitigation effectiveness (0 to 100%)|21.1%|30.0%|20.0%|14.0%|10.0%|
|D2. HLO decrease (points) =<br>_B1*(A*((Total school weeks/43.3)*(1-C1))_|24.6|24.8|32.1|22.1|12.4|
|_Intermediate Scenario_||||||
|B2. Share of the system affected over observed<br>period (24 months)|45.4%|40.8%|55.9%|44.7%|36.0%|
|C2. Mitigation effectiveness (0 to 100%)|10.5%|15.0%|10.0%|7.0%|5.0%|
|D2. HLO decrease (points) =<br> _B2*(A*((Total school weeks/43.3)*(1-C2))_|29.8|32.3|38.5|25.2|13.6|
|_Pessimistic Scenario_||||||
|B3. Share of the system affected over observed<br>period (24 months)|49.2%|44.8%|60.7%|48.0%|38.3%|
|C3. Mitigation effectiveness (0 to 100%)|10.5%|15.0%|10.0%|7.0%|5.0%|
|D3. HLO decrease (points) =<br>_B3*(A*((Total school weeks/43.3)*(1-C3))_|32.3|35.4|41.7|27.0|14.5|
|**GEP* (GDP per capita growth %) [g]**|3.3|4|5.1|1.5|1.1|

_Note: Values represent country-level averages._

- - Notes: (*) Global Economic Prospects January 11th 2022 update (https://www.worldbank.org/en/publication/macro <u>poverty outlook), with the regional average imputed if no country value was available for 2020 or 2021.</u>

16 The table provides an overview of parameters by income, though simulation parameters are applied at the country level.

> 17 The World Bank’s Harmonized Learning Outcome (HLO) puts learning data from international and regional assessments on a comparable scale. The data can be accessed here. We assume the learning gains will vary from 20 to 50 learning points depending on the country’s income level, as explained in Azevedo, Hasan et al. (2021).

8
# **Table 4: Adjusting the estimates of wage ratios of observably equivalent workers from pooled regressions adjusting for the quality of schooling**

(a) _Adjustment using Internationally Comparable Assessments of Learning Achievement_

|Row||Average,<br>_N_= 18|Median,<br>_N_= 18|
|---|---|---|---|
|I|Wage ratios, bilateral regressions, basic Mincer, S=9|4.66|3.03|
|II|Unadjusted wage ratios, pooled regressions, basic Mincer, S=9|4.13|2.63|
|III|Wage ratios, pooled regressions, schooling in US in test score<br>adjusted years equivalent|3.08|2.11|
|IV|Ratio row I / row III|**1.34**|**1.25**|
|V|Number of score adjusted years equivalent of schooling that<br>foreign is less than that of the US|2.39|2.18|

(b) _Adjustment using differences in returns in the US labor market of schooling acquired in the US and abroad_

|Row||Average,<br>_N_ =42|Median,<br>_N_ =42|
|---|---|---|---|
|I|Wage ratios, bilateral regressions, basic Mincer, S=9|4.10|3.59|
|II|Unadjusted wage ratios, pooled regressions, basic Mincer, S=9|4.09|3.75|
|III|Wage ratios, pooled regressions, schooling of migrants in US<br>scaled back by_β_<sup>_l_</sup><br>_j_<sup>/</sup><sup>_β_</sup>_0_|2.47|2.18|
|IV|Ratio row I / row III|**1.65**|**1.65**|
|V|Ratio of Mincer coefficient for years acquired in foreign country<br>versus US,_β_<sup>_l_</sup><br>_j_<sup>/</sup><sup>_β_</sup>_0_|0.533|0.495|

## (c) _Adjustment using a hypothetical "evaporation" ratio_

|Row|||M|edian, _N_|= 42|||
|---|---|---|---|---|---|---|---|
|I|Wage ratios, bilateral regressions,<br>basic Mincer,_s_= 9|3.59|3.59|3.59|3.59|3.59|3.59|
|II|Unadjusted wage ratios, pooled<br>regressions, basic Mincer,_s_= 9|–|–|–|–|–|–|
|III|Wage ratios, schooling in US labor<br>market scaled by (1 – evaporation<br>ratio)|3.75|3.37|3.02|2.71|2.44|2.19|
|IV|Ratio row I / row III|**0.96**|**1.07**|**1.19**|**1.32**|**1.47**|**1.64**|
|V|Assumed fraction of a year of|||||||
||schooling acquired abroad that|0.0|0.1|0.2|0.3|0.4|0.5|
||“evaporates” in US labor market|||||||

# 3.3 _Natural experiments_

A third way to bound the bias is to rely on natural experiments that permit identification of the wage gain from migration _per se_ . McKenzie, Gibson, and Stillman (2006) present a unique experimental estimate of the wage gain to migration from a poor to a rich country. They take advantage of New Zealand’s Pacific Access Category residence visa, which is designed to allow a limited number of citizens of Tonga (and three other island states) to settle in New Zealand each year via a random lottery. Any person age 18-45 who is a citizen and natural of the four PAC countries may register for the lottery, and among those registered a certain number are randomly allocated the chance to apply for residence. Though not all lottery winners later acquire New Zealand residence, a survey comparing the wages of Tongan lottery losers with those of Tongan lottery winners 24
These variables are obtained from TUIK and the Ministry of Finance, and have varying levels of data availability.

The sectoral composition of regions has direct implications on the labor intensity and skill composition of workers. These factors are strong predictors of wages and hence poverty and welfare. At the NUTS1 level, Istanbul has the largest share of national GVA owing in large part to a highly diversified services and industry sectors (Figure 23). However, this series is also limited and only available from 2006 to 2011. Moreover, GDP by sector and region are not available from administrative sources. Since industries can receive large amounts of subsidies, GVA may give an inaccurate snapshot of the size of sectors across regions.

Table 4 illustrates the estimation results of Equation (1). Coefficients can be interpreted as elasticities. In estimations without regional interaction terms, GVA growth in agriculture appears to be the only sector driving reductions in poverty. However, when allowing the growth effects to be heterogeneous between East and West regions, it becomes clearer that agriculture primarily benefitted Eastern regions, and to a lesser extent Western regions. Notice, that the R-squared, or explanatory power of the regressions almost doubles as well when the regional interaction is added, highlighting the importance of regional differences in the patterns of growth.

Table 4. Sector Patterns of Growth (log GVA)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|�∆ln ���<br>��<br>�∆ln ���<br>��|-0.107***<br>(0.0295)<br>0.0245<br>(0.0321)|-0.102***<br>(0.0313)<br>0.0232<br>(0.0303)|||
|�∆ln ���<br>��|-0.00682|-0.00467|||
||(0.0579)|(0.0582)|||
|�∆ln ���<br>��*�_����|||-0.0859***|-0.0801**|
||||(0.0289)|(0.0314)|
|�∆ln ���<br>��*�_����|||-0.429***|-0.442***|
||||(0.106)|(0.106)|
|�∆ln ���<br>��*�_����|||0.000813|0.00419|
||||(0.0357)|(0.0360)|
|�∆ln ���<br>��*�_����|||0.186***|0.201**|
||||(0.0638)|(0.0849)|
|�∆ln ���<br>��*�_����|||-0.00669|-0.00140|
||||(0.0635)|(0.0674)|
|�∆ln ���<br>��*�_����|||0.471***|0.562***|
||||(0.0698)|(0.132)|
||-0.00422*|-0.00264*|-0.00641***|-0.00158|
||(0.00223)|(0.00134)|(0.00198)|(0.00126)|
|NUTS1 Controls||X||X|
|Number of Observations|104|104|104|104|
|R-Squared|0.137|0.246|0.292|0.373|

Notes: Table 22 show full regression results under various region groupings. Results from Equation 1 are shown. Regressions are limited to the 2007-2011 period. + p<0.10, * p<0.05, ** p<0.01 _Source_ : TUIK, LFS, SILC 2007-2011, author’s calculations When using the OECD regional groupings, again we find that agricultural gross value added was significant for poverty reduction, but other terms are insignificant (Table 22). Under the OECD classifications, the R- squared also reduced, which signals perhaps a poorer classification than simply an East-West indicator.

20
expenditures survey. Delays in salary payment, our measure of a pecuniary incentive, affect teacher effort, especially in the form of after-school tutoring. The non-pecuniary incentives that promote teacher effort, particularly teachers’ willingness to offer after-school tutoring, include giving teachers more autonomy in choosing their teaching methods, developing teaching materials, and setting standards for student promotion. When there are stronger monitoring mechanisms in the school, teachers tend also to substitute from the unobservable activity of preparing for classes at home towards the more observable activity of providing after-school tutoring. Teachers are more likely to tutor or tutor more students in schools that have a PTA and in schools where the school principal has dismissal authority.

Although our results are consistent across several estimation models, we do not want to overstate them. First, our measures of teacher effort are not derived from direct observations; the data on after-school work hours for preparing classes and after-school tutoring, whether for pay or not, are self-reported. We argue above that while self-reported teacher absences are known to be unreliable, hours of work after school and after-school tutoring are less susceptible to deliberate misreporting because there are no officially mandated hours for these activities. We also find that that a large proportion of teachers report only a small number of after-school work hours rather than the other way around (Figure 1), lending some credence to the data on teacher effort.

Second, we rely on non-experimental data in this paper to estimate the effect of incentives on teacher effort. This approach may be subject to omitted variable bias. To mitigate such bias, we estimate a simultaneous OLS-probit model with school random-effects, which is then supplemented with further analysis of different after-school tutoring as well as PTA activities. Likewise, we control for a set of teacher and school characteristics in different model 28
_Table 5: Overall estimates of child poverty and inequality, by gender and location_

|**_Welfare measure_**|**_Type of index_**|**_All_**<br>**_children_**|**_Girls_**|**_Boys_**|**_Rural_**|**_Urban_**|
|---|---|---|---|---|---|---|
|**_Multidimensional_**<br>**_child poverty_**|Number of<br>deprivations(C)|3.5<br>(0.014)|3.47<br>(0.021)|3.52<br>(0.020)|4.04<br>(0.002)|1.52<br>(0.019)|
||Poverty<br>incidence(H)|0.54<br>(0.025)|0.54<br>(0.026)|0.54<br>(0.025)|0.66<br>(0.027)|0.11<br>(0.022)|
||Poverty intensity<br>(A)|0.48<br>(0.004)|0.48<br>(0.005)|0.48<br>(0.005)|0.48<br>(0.005)|0.45<br>(0.008)|
||Adjusted|0.26|0.26|0.26|0.32|0.05|
||<br>(MPI=H*A)|(0.013)|(0.014)|(0.013)|(0.015)|(0.011)|
|**_Monetary child_**|Poverty|0.34|0.36|0.31|0.39|0.15|
|**_poverty*_**|headcount<br>Poverty gap|(0.004)<br>0.11|(0.006)<br>0.12|(0.006)<br>0.11|(0.005)<br>0.13|(0.005)<br>0.05|
||Poverty severity|(0.002)<br>0.05|(0.002)<br>0.06|(0.002)<br>0.05|(0.002)<br>0.06|(0.002)<br>0.02|
|||(0.001)|(0.002)|(0.001)|(0.001)|(0.001)|
|**_Overlaps of_**<br>**_absolute monetary_**<br>|Poor in both|0.24<br>(0.006)|0.22<br>(0.008)|0.26<br>(0.008)|0.30<br>(0.007)|0.05<br>(0.004)|
|**_&_**<br>**_multidimensional_**<br>**_child poverty_**|Money-poor but<br>mutid-nonpoor<br>Money nonpoor<br>but mutid-poor<br>Non-poor in both|<br>0.09<br>(0.004)<br>0.29<br>(0.006)<br>0.37|<br>0.08<br>(0.005)<br>0.32<br>(0.009)<br>0.38|<br>0.10<br>(0.006)<br>0.27<br>(0.008)<br>0.36|<br>0.09<br>(0.005)<br>0.36<br>(0.007)<br>0.25|<br>0.10<br>(0.006)<br>0.06<br>(0.005)<br>0.79|
|||(0.006)|(0.009)|(0.009)|(0.007)|(0.008)|
|**_Overlaps of_**|1st quintile and|0.77|0.78|0.76|0.81|0.39|
|**_relative monetary_**|multid-poor|(0.012)|(0.017)|(0.017)|(0.013)|(0.033)|
|**_&_**<br>|2nd quintile and|0.63|0.63|0.64|0.69|0.21|
|**_multidimensional_**<br>**_child poverty_**|multid-poor<br>3rd quintile but|(0.014)<br>0.53|(0.021)<br>0.54|(0.020)<br>0.51|(0.015)<br>0.61|(0.025)<br>0.11|
||multid-poor<br>4th quintile but|(0.015)<br>0.44|(0.021)<br>0.45|(0.021)<br>0.44|(0.017)<br>0.55|(0.017)<br>0.10|
||multid-poor|(0.015)|(0.020)|(0.022)|(0.018)|(0.015)|
||5th quintile but<br>|<br>0.31|<br>0.35|<br>0.27|<br>0.56|<br>0.03|
||multid-poor|(0.014)|(0.019)|(0.019)|(0.021)|(0.005)|
|**_Monetary child_**|Overall|0.31|0.32|0.3|0.26|0.3|
|**_inequality**_**|Within-group|||0.31|0|.27|
|**_Source:_**_Authors’ calc_<br>**_Notes_**_: *FGT poverty i_|Between-group<br>_ulations; Data from ES_<br>_ndices based on dispos_|_S 2018/19._<br>_able income an_|<br>_d using a ca_|0.01<br>_librated povert_|0<br>_y line (ETB_|.04|

**_Notes_** _: *FGT poverty indices based on disposable income and using a calibrated poverty line (ETB 5050/yr/ad.eq.) that gives a similar headcount as the official ratio._

_**Inequality is computed as a Theil’s index based on disposable income. Standard errors in parenthesis._ 14
# **References**

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- gramme for schooling on prevalence of HIV and herpes simplex type 2 in Malawi: cluster randomized trial". _The Lancet_ 379, 1320-1329.

- [2] Barberis, N.C. 2013. "Thirty Years of Prospect Theory in Economics: A Review and Assessment". _Journal of Economic Perspectives_ 27(1): 173–196.

- [3] Baeten, J.M., C. Celum, and T.J. Coates. 2009. "Male circumcision and HIV risks and benefits for women". _The Lancet_ 374 (9685): 182-184.

- [4] Bertrand JR, O’Reilly K, Denison J, Anhang R, Sweat M. 2006. "Systematic review of the effectiveness of mass communication programs to change HIV/AIDS-related behaviors in developing countries", _Health Education and Research_ ; 21:567-97.

- [5] Boily MC, Baggaley RF, Wang L., Richard M., White G., Hayes R.J., Alary M. 2009."Heterosexual risk of HIV-1 infection per sexual act: systematic review and meta-analysis of observational studies", _Lancet Infect Dis._ 9(2):118–29.

- [6] Crosby R.A., R.J. DiClemente, G.M. Wingood, D. Lang, K.F. Harrington. 2003. "Value of Consistent Condom Use: A Study of Sexually Transmitted Disease Prevention Among African American Adolescent Females". _American Journal of Public Health_ 93: 901–902.

- [7] de Walque, D., W.H. Dow, R. Nathan, R. Abdul, F. Abilahi, E. Gong, Z. Isdahl, J. Jamison, B. Jullu, S. Krishnan, A. Majura, E. Miguel, J. Moncada, S. Mtenga , M.A. Mwanyangala, L. Packel, J. Schachter, K. Shirima, and C.A. Medlin. 2012. "Incentivising safe sex: A randomised trial of conditional cash transfers for HIV and sexually transmitted infection prevention in rural Tanzania". _British Medical Journal Open_ doi:10.1136/bmjopen-2011-000747.

- [8] de Walque, D., W.H. Dow, R. Nathan. 2014. "Rewarding safer sex : conditional cash transfers for HIV/STI prevention", Policy Research Working Paper #7099, The World Bank.

- [9] Duflo E., Dupas P., Kremer M. 2014. "Education, HIV, and Early Fertility: Experimental Evidence from Kenya", working paper.

- [10] Duflo, E., R. Glennerster, and Michael Kremer. 2008. "Using Randomization In Development Economics Research: A Toolkit." In T. Schultz and John Strauss (eds.)

22
Table 54

|Dependent variable: Nee|d of fiscal|adjustment|based on E|scolano et|al.(2014)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.031***|0.026***|0.034***|0.026***|0.029**|0.015|-0.002|0.024**|
|structural as % GDP|(0.006)|(0.006)|(0.006)|(0.006)|(0.012)|(0.024)|(0.014)|(0.011)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-1.425***||-1.533***|-1.503***|-1.560***|-1.458***|-1.535***|
|||(0.260)||(0.273)|(0.264)|(0.276)|(0.271)|(0.271)|
|Inflation (t-1)||-0.102**||-0.111|-0.115|-0.107|-0.126|-0.111|
|||(0.048)||(0.084)|(0.085)|(0.081)|(0.089)|(0.084)|
|U.S. Interest rate||-0.047***||-0.064***|-0.064***|-0.063***|-0.067***|-0.064***|
|||(0.013)||(0.014)|(0.015)|(0.014)|(0.015)|(0.014)|
|**Political factors**|||||||||
|Gini|||-0.001|-0.001|-0.001|-0.003|-0.001|-0.001|
||||(0.007)|(0.006)|(0.006)|(0.008)|(0.006)|(0.006)|
|Rule of law|||0.008|0.066**|0.065**|0.068**|0.012|0.066**|
||||(0.024)|(0.029)|(0.029)|(0.029)|(0.040)|(0.029)|
|Margin of Majority|||-0.092|-0.142|-0.140|-0.142|-0.141|-0.173|
||||(0.098)|(0.086)|(0.086)|(0.087)|(0.086)|(0.138)|
|Election's year|||0.002|0.003|0.004|0.003|0.005|0.004|
||||(0.016)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||-||||
|EMEs * Deviation|||||-0.005<br>(0.014)||||
|Gini * Deviation||||||0.000<br>(0.001)|||
|Rule of law * Deviation|||||||0.007*<br>(0.003)||
|Majority * Deviation||||||||0.005<br>(0.017)|
|Constant|0.079|0.369***|0.090|0.294|0.298|0.370|0.517|0.313|
||(0.051)|(0.071)|(0.313)|(0.301)|(0.299)|(0.359)|(0.320)|(0.307)|
|Observations|1,753|1,707|1,538|1,502|1,502|1,502|1,502|1,502|
|R-squared|0.059|0.130|0.071|0.166|0.166|0.166|0.172|0.166|
|Number of ifscode|83|83|74|74|74|74|74|74|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors c<br>***p<0.01,**p<0.05,*|lustered by<br> p<0.1|country in|parenthese|s|||||

- 83 -
WPS2771

**Exchange Rate Appreciations, Labor Market Rigidities, and Informality**<sup>**•**</sup>

Norbert M. Fiess<sup>∗</sup> Marco Fugazza<sup>°</sup> William Maloney<sup>∗</sup> The World Bank February 2002

**Abstract:** This paper works at the interface of the literature exploring the raison d’etre of the informal labor market and that explaining the real exchange rate appreciations occurring in many Latin American countries during periods of reform. We first build a small country-Australian style model where the informal sector is seen as an unregulated non-tradables sector, augmented by heterogeneity in entrepreneurial ability and capital adjustment costs.  We then examine the behavior of the model with and without a formal sector rigidity. We show that the co-movements of relative formal/informal incomes, formal/informal sector size, and the real exchange rate can offer insight into the level of distortion in the labor market and the source of ER fluctuations.  We then explore time series data from Brazil, Colombia and Mexico using multivariate co-integration techniques to establish what “regime” each country is in at various periods of time. Mexico, for instance, appears to be relative undistorted and the 1987-92 appreciation appears to be largely a function of a boom in the non-tradables sector rather than wage inertia.  In spite of a secular expansion of the informal sector and there is little evidence of dualism or of a rigidity driven appreciation of the Real, from 1993-1996.  Post 1995 Colombia corresponds to a classic segmented labor market and an appreciation partly driven by labor market rigidities. Graphical analysis suggests that neither the Argentine appreciation (1988-1992) or the celebrated Chilean appreciation (1975-1982) were driven by inertial forces.

> • This work partially financed by the Regional Studies Program of the Office of the Chief Economist for Latin America and the Caribbean.  The development of the conceptual framework  was supported by the Franco-Italian Foundation for Theoretical Philanthropy.  We are grateful to Rashmi Shankar for early conceptual discussions, to Leonardo Gaspirini for helpful comments, to Norman Loayza and Claudio Montenegro for help with data, and to Gabriel Montes for expert research assistance.  Corresponding author: wmaloney@worldbank.org.

> ∗ The World bank

> ° CERAS, Ecole Nationale des Ponts et Chaussées
**Table 1: Macro-economic indicators, by quarter: 1997-1998**

||**_QJ_**|<br>_Q2_|_1997_<br>_Q3_|_Q4_|_Average_|_Ql_|_Q2_|_1998_<br>_Q3_|_Q4_|_Average_|
|---|---|---|---|---|---|---|---|---|---|---|
|_Output growth (% per_<br>_year):_|||||||||||
|Agriculture|4.9|1.8|0.4|4.1|2.9|-3.6|-11.5|0.0|-1.2|-6.6|
|Industry|5.1|7.6|6.4|5.6|6.1|1.3|-1.5|-0.7|-1.9|-1.7|
|Services|6.1|5.7|5.6|4.6|5.5|4.9|3.6|-3.1|-7.8|3.5|
|Gross Domestic Product|5.5|5.6|4.9|4.8|5.2|1.6|-0.8|-0.7|-1.9|0.1|
|Gross National Product|5.4|5.3|5.2|5.3|5.3|2.0|-0.3|0.0|-1.2|-0.5|
|Unemploymentrate(%)|7.7|10.4|8.7|7.9|8.7|8.4|13.3|8.9|9.6|10.1|
|Inflation (% per year)|4.7|4.5|4.9|5.8|5.0|7.0|9.6|10.5|10.6|9.4|
|Exchange rate (Peso/$)|26.3|26.4|29.8|35.4|29.5|40.7|39.4|42.9|40.6|40.9|
|Net foreign reserves<br>(million $)|10268|10065|8261|7456|9013|6990|8418|7970|8004|7845|

_Note:_ The figures for inflation, exchange rate and net foreign reserves are quarterly mean calculated from of monthly data.

_Source:_ National Statistical Coordination Board and Bangko Sentral ng Pilipinas (BSP).

**Table 2: The incidence of crisis-related economic shocks**

|_Crisis-related shocks_|_Percent of sample_<br>_households affected_|_Percent of population_<br>_affected_|
|---|---|---|
|Price shock|89.9|91.4|
|Domestic employment shock|18.4|20.3|
|Overseas/migrant employment shock|4.32|4.93|
|Wage shock|15.5|16.7|
|DroughtlEl-Nifto shock|56.6|59.8|
|Price shock only|28.2|26.0|
|Domestic employment shock only|0.16|0.16|
|Overseas/migrant employment shock only|0.02|0.02|
|Wage shock only|0.23|0.19|
|Drought/El Niflo shock only|2.36|2.28|
|Hit by at least one of the five shocks|93.0|94.4|
|Hit by at least one of the four shocks other than<br>the price shock|64.8|68.4|
|Labor market shock (regardless of the price<br>shock)|8.1|8.6|
|El-Niflo shock (regardless of the price shock)|39.0|40.4|
|Both El-Niflo and labor market shocks (regardless<br>of price shock)|17.6|19.4|

_Source:_ Calculated from 1998 APIS data.

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