rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Moderating Effects of Community-Based Programs for Households in Rural Java Exposed to Post-Onset Low Rainfall Shocks: Average Treatment Effects based on Propensity Score Matching**

||**A**<br>**(perc**<br>**A**|**verage Treatm**<br>**ent difference**<br>**ll households**|**ent Effect of**<br>**between tre**<br>|**Community-Ba**<br>**atment and co**<br>**Households**|**sed Programs**<br>**mparison grou**<br>**engaged in far**|<br>**ps)**<br>**m business**|
|---|---|---|---|---|---|---|
|**Sub-sample:**|<br>**exposed**|<br>**to low rainfall**|<br>**shock**|<br>**and expos**|<br>**ed to low rainf**|<br>**all shock**|
|**Components of per capita expenditure:**|**Total**|**Nonfood**|**Food**|**Total**|**Nonfood**|**Food**|
|**Technical Irrigation ‡**|12.6 **|27.0 ***<br>n=884|3.1|24.3 ***|46.7 ***<br>n=575|8.9|
|**Kampung Improvement Program ‡**<br>**(community-based)**|8.0 *|20.7 ***<br>n=1107|-0.9|6.9|17.4 **<br>n=838|-3.0|
|**Infrastructure Development Program ‡**<br>**(community-based)**|13.9 **|10.3<br>n=632|18.5 ***|-5.0|-9.9<br>n=509|-2.5|
|**INPRES Poor Villages Program ‡**<br>**(credit)**|25.0 ***|4.6<br>n=1390|38.2 ***|11.0|-12.8<br>n=959|28.8 ***|
|**Padat Karya Program †**<br>**(public works)**|16.3 ***|23.5 ***<br>n=1033|13.8 **|2.7|15.7<br>n=632|-4.0|
|**PDM-DKE Program †**<br>**(block grants)**|18.9 ***|23.9 ***<br>n=544|18.6 **|9.5|21.5<br>n=216|9.1|
|**Either of the two programs above †**|28.3 ***|31.4 ***<br>n=722|30.0 ***|14.7 *|20.5 *<br>n=514|14.5 *|
|**IDT Program †**<br>**(block grants )**|17.7 ***|14.5 *<br>n=978|18.2 ***|33.4 ***|39.2 ***<br>n=352|35.9 ***|
|legend: p < 0.1 *, p < 0.05 ** , p < 0.01 ***<br>‡ Pooled data from 1997 IFLS2 and 2000 IFL<br>|S3;† Data fr|om 2000 IFLS3|data only||||

Focusing on the sample of households of rural Java, we find that households exposed to the rainfall shock but residing in communities with the infrastructure or programs mentioned above have on average a significantly higher level of per capita expenditure than households in the comparison group. Households in communities with the INPRES credit program had an average of 25% higher total per capita expenditure and 38% higher food per capita expenditure than comparison households, suggesting that the program furnished an important coping mechanism to households affected by the shocks. The Padat Karya (public works) and PDMDKE (public works / credit) safety net programs also appear to have helped households cope with the impacts of the shocks. The difference in average total per capita expenditure between 15
Structural and Reduced Form Estimates. In: Osmani, S. R. (Ed.), Nutrition and Poverty. Oxford University Press, Oxford.

- Behrman, J., 1997. Intrahousehold Distribution and the Family. In: Rosenzweig, M. R., Stark, O. (Eds.), Handbook of Population and Family Economics. Handbooks in Economics series. Elsevier Science, Amsterdam, pp. 125–187.

- Behrman, J. R., 1988. Nutrition, Health, Birth Order and Seasonality: Intrahousehold Allocation in Rural India. Journal of Development Economics 28, 43–63.

- Behrman, J. R., Deolalikar, A. B., 1989. ”Is Variety the Spice of Life? Implications for Calorie Intake”. The Review of Economics and Statistics 71 (4), 666–672.

- Behrman, J. R., Deolalikar, A. B., 1990. ”The Intrahousehold Demand for Nutrients in Rural South India: Individual Estimates, Fixed Effects, and Permanent Income”. Journal of Human Resources 25 (4), 665–696.

- Bouis, H. E., Haddad, L. J., Kennedy, E., 1992. Does It Matter How We Survey Demand for Food? Evidence from Kenya and the Philippines. Food Policy 17 (5), 349–360.

- Bourguignon, F., Browning, M., Chiappori, P.-A., Lechene, V., 1993. Intrahousehold Allocation of Consumption: A Model and Some Evidence from French Data. Annales D’Economie et de Statistique 29, 137–56.

- Bourguignon, F., Browning, M., Chiappori, P.-A., Lechene, V., 1994. Incomes and Outcomes: a Structural Model of Intra-Household Allocation. Journal of Political Economy 102 (6), 1067– 1096.

- Chiappori, P.-A., 1988. Rational Household Labor Supply. Econometrica 56, 63–89.

- Chiappori, P.-A., 1992. Collective Labor Supply and Welfare. Journal of Political Economy 100, 437–67.

- Deaton, A., Paxson, C., 1998. Economies of Scale, Household Size, and the Demand for Food. Journal of Political Economy 106 (5), 897–930.

- Deolalikar, A. B., 1988. Do Health and Nutrition Influence Labor Productivity in Agriculture? Econometric Estimates for Rural India. Review of Economics and Statistics 70, 406–413.

- Doss, C., 1999. Intrahousehold Resource Allocation in Ghana: The Impact of Distribution of Asset Ownership within the Household. In: Peters, G. H., von Braun, J. (Eds.), Food Security,

28
<!-- Start of picture text --> 3 Gross domestic product<br><!-- End of picture text --> <!-- Start of picture text --> 0 Emissions<br>-10 \ —<br>—<br><!-- End of picture text --> <!-- Start of picture text --> policy commitment<br> policy fo r ecast<br><!-- End of picture text --> <!-- Start of picture text --> g<br>=a61<br>.<br>- 0<br>So ViRf a<br>3 a<br>2 ——— Benchmark policy forecast<br>x —— Credible policy commitment<br>-2 | —-— Expected policy reversal<br>—— Lower risk premia<br>— Easier access to green bank lending<br>3<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text -->

<!-- Start of picture text --> 7<br>——<br>————TM<br>~<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> g -20 Ne — Lower<br>3v8 30 wea— —— Easier<br>= -40 —<br>= -50 S<br>-s -60 :<br>3 70<br>a<br>S -80<br>-90<br>-100<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> risk premia<br> access to green bank lending<br>—<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> 500 Final renewable energy<br>400<br><!-- End of picture text --> <!-- Start of picture text --> ——<br><!-- End of picture text --> <!-- Start of picture text --> 1400 Private renewable<br>100<br><!-- End of picture text --> <!-- Start of picture text --> ee<br><!-- End of picture text --> <!-- Start of picture text --> =<br>3 7-"<br>8 300 aa<br>fo)<br>= 200 YZ<br>B<br>> 100 fi"4<br>g 4 ——<br>0 “ ——~~<br>—— Lower<br>—— Easier<br>-100<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> policy forecast<br>policypolicy commitmentreversal<br>risk premia<br>access to green bank lending<br>Q1-2045 Q1-2050<br><!-- End of picture text --> <!-- Start of picture text --> £18= 1000 A<br>8 800 aw l. Z<br>Ee° jfi; ae<br>= 600<br>Ss y<br>g® 400<br>> 200 ——<br>xs 4 —————<br>0 —— Lower<br>—— Easier<br>-200<br>Q1-2025 Q1-2030 Q1-2035 Q1-2040<br>Quarters<br><!-- End of picture text --> <!-- Start of picture text --> —<br>policy forecast<br>policypolicy commitmentreversal<br>risk premia<br>access to green bank lending<br>Q1-2045 Q1-2050<br><!-- End of picture text -->
# The World Bank’s Publication Record

## Martin Ravallion and Adam Wagstaff<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, USA_

> 1 We owe an enormous debt of gratitude to Qinghua Zhao; without his programming skills this paper would have been substantially less interesting. We are also grateful to Imran Hafiz for help in retrieving data from online databases. Helpful comments were received from Deon Filmer, Asli Demirguc-Kunt, Dominique van de Walle and Colin Xu. The views expressed here are those of the authors and need not reflect those of the World Bank, its Executive Directors, or the countries they represent.
# **Table 4.   Topics with increasing focus**

|||**1999-2001**|**2002-2004**|**2005-2007**|**Score**<sup>**7**</sup>|
|---|---|---|---|---|---|
|1|_Access Promotion - Pro-market Activism_|0.0|0.1|0.8|187.0|
|2|_Transparency - Banking - Consumer Protection_|0.0|0.1|0.4|185.4|
|3|_Global Oversight - Crisis Management_|0.0|0.1|0.3|160.9|
|4|_Human Resources Management - Monetary and FX Mgmt_|0.0|0.1|0.3|160.9|
|5|_Research and Information Support – Pro-market Activism_|0.0|0.1|0.3|160.9|
|6|_Performance Assessment – Public Banks_|0.0|0.2|0.4|156.1|
|7|_Institution based risk management - Pensions Supervision_|0.0|0.3|0.5|141.4|
|8|_Performance Assessment - Development Banks_|0.0|0.2|0.3|141.4|
|9|_Performance Assessment – Public Insurance_|0.0|0.1|0.2|141.4|
|10|_Resources & Capacity Building - Pensions Oversight_|0.0|0.2|0.3|121.9|
|11|_Market based risk management - Insurance Supervision_|0.0|0.2|0.2|112.1|
|12|_Microfinance - Market competition_|0.0|0.2|0.2|112.1|
|13|_Strengthening - Real Pledges_|0.2|0.7|1.1|102.5|
|14|_Implementation – Auditing_|0.2|0.6|0.8|88.2|
|15|_Guarantees - Indirect Intervention_|0.1|0.2|0.4|86.1|
|16|_Independence & Accountability - Pensions Oversight_|0.1|0.3|0.4|82.5|
|17|Auditing|0.5|0.8|1.4|82.4|
|18|_Real Pledges - Registries_|0.4|1.2|1.4|81.3|
|19|_Microfinance - Legal and Org Framework for Oversight_|0.4|1.1|1.3|80.5|
|20|Pro-market Activism|0.5|0.6|1.4|79.3|
|21|_Microfinance – Supervision_|0.4|0.4|1.0|74.4|
|22|_General Development Strategies – Pro-market Activism_|0.4|0.4|1.0|74.4|
|23|_Housing - Direct Intervention_|0.4|0.4|1.2|73.6|
|24|_Specialized Courts - Judiciary_|0.2|0.4|0.5|68.4|
|25|_Oversight – Payments System_|0.4|0.6|0.8|68.0|
|26|_Parameters - Pensions & Social Security_|0.8|1.1|1.8|63.9|
|27|_Banking - Consumer Protection_|0.5|0.6|1.0|62.3|
|28|Indirect public intervention|0.5|0.6|1.0|62.3|
|29|Registries – Informational|2.3|3.9|4.7|56.8|
|30|_Debtors - Registries - Informational_|1.2|1.4|2.3|56.3|
|31|_Subsidies - Indirect Intervention_|0.2|0.3|0.5|55.9|
|32|_Pensions – Supervision_|0.5|0.7|0.9|55.7|
|33|_Reserve Requirements - Operations Monetary and FX Mgmt_|0.4|0.5|0.6|50.9|
|34|_General Framework – Payments System_|1.5|2.0|2.7|50.1|
|35|_Pensions & Social Security - Direct Intervention_|1.3|2.1|2.3|49.0|
|36|_Informational Framework_|4.8|7.3|8.6|49.0|
|37|_Market based risk management - Banking Supervision_|0.4|0.4|0.6|46.5|
|38|_Pensions - Legal and Org Framework for Oversight_|1.1|1.6|1.8|45.7|
|39|_Coordinated Oversight - Global Oversight –Legal Framework_|0.7|0.9|1.2|45.1|
|40|_Legal Framework – AML_|0.7|1.1|1.2|42.1|

7 The score is the sum of the percentage increases from each column to the next, where the percent is calculated with respect to the later period.

11
# **3. Data Description**

The data used in the estimations consist of a sample of 400 Venezuelan individuals. These individuals were approached by the author of this paper and a co-researcher in summer 2005 and asked to respond to a standardized questionnaire.<sup>3</sup> The dependent variable was derived from the question: “In general, how satisfied are you with your life these days?” Four answers were possible – ‘very satisfied’ (4), ‘satisfied’ (3), ‘not very satisfied’ (2), and ‘not satisfied at all’ (1). Definitions for all relevant variables as well as sample summary statistics are listed in table A.1 in the Appendix. The percentages of individuals in each life satisfaction category are displayed in table 1, which also illustrates the reported satisfaction levels for selected subgroups.

While the sample stratification was not formalized by means of a sampling frame, the sample was approximately stratified by income and location. Individuals were approached roughly according to the income and location criteria in bus stations, shopping malls, town squares, at the beach, in restaurants, in hospital waiting rooms, near universities, in their homes, and at work. This strategy has produced a sample with means that exhibit relatively close overlaps with the national means for the Venezuelan population.<sup>4</sup> As questionnaire responses have been found to depend on the ordering of questions (Bertrand and Mullainathan, 2001), this questionnaire was constructed with the aim to

> 3 The data collection was carried out by the two researchers in Venezuela from July 11 to August 29 2005. English and Spanish versions of the questionnaire are available from the author at request.

4 The sample is comprised by individuals aged 17 to 80 years with a median age of 32 years. In comparison, the median age of the Venezuelan population above 15 lies between 30 and 34 years (United Nations, 2006). While women account for 50% of the population, 52% of sample respondents are women. In the sample, individuals report to live on average in households that consist of 4.75 members, whereas the national average household size for the first semester of 2005 is 4.34 (Instituto Nacional de Estadística, 2006). In a question about their attitude toward the government, 55% of the individuals in the sample answered that they approve of President Hugo Chávez. Election outcomes from August 2004 show that 59% of voters supported Chávez in a recall referendum, while 41 percent polled for new elections and the abstention rate was at 30% (Consejo Nacional Electoral, 2004).

10
- 16 -

|_BICY_|0.235 (0.333)|0.304 (0.325)|0.072 (0.337)|0.318|(0.377)|
|---|---|---|---|---|---|
|Constant|5.007(0.412)***|5.497(0.373)***|6.202(0.458)***|4.707|(0.587)***|
|Obs.|2587|2587|2587|2587||
|R-squared|0.347|0.364|0.351|0.317||
|Wald chi2|1490.0|1490.0|1471.9|1408.8||
|Dummy variab|les:|||||
|Crops|Yes|Yes|Yes|Yes||
|Prefectures|Yes|Yes|Yes|Yes||
|Endogeneity te|st:|||||
|C stat. chi2|23.808 ***|1.219|1.968|28.808|***|
|Overidentifyin|g restriction test:|||||
|Hansen's J|3.817|20.235 ***|19.150 ***|2.689||
|stat. chi2||||||

29. To estimate the market sales equation, the IVTOBIT model was applied: The results are presented in **Table 4** . The market accessibility variables to Moroni and a regional city are again found to be significant. The coefficient is estimated at -1.95 when _COSTMORONI_ is only included in the equation. The coefficient is also significant at -0.95 when _COSTCITY_ is used. Thus, the crop sales have a higher elasticity with respect to the costs to Moroni. The statistical significance looks relatively weak when _MAI_ is used.

30. When all three variables are included, the accessibility to Moroni remained statistically significant. In this model, the endogeneity test statistic is estimated at 20.09, which implies that the connectivity variables are likely endogenous. Thus, the result can be interpreted to mean that the connectivity to Moroni is particularly important to support agricultural market transactions. The finding may not be surprising because Moroni is the primary market in the Comoros. All the indications are that improved market accessibility can boost agricultural growth. Especially given the current distribution of population (see **Figure 11** ) and agricultural production areas ( **Figure 14** ), inter-island connectivity is an important challenge to the country.

31. The results also suggest that the accessibility to intermediary services, such as cooperatives and collectors, are instrumental to facilitating the farmers’ market participation to sell their produce. All the coefficients of _COOP_ , _MKT_ and _TRAD_ are estimated to be
Figure 3c: Poverty Dynamics - Synthetic vs. Actual Panel Data for Alternative Poverty Lines, Peru 2008 and 2009 <!-- Start of picture text --> Poor in 2008 and Poor in 2009 Poor in 2008 and Non-poor in 2009<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Non-poor in 2008 and Poor in 2009 Non-poor in 2008 and Non-poor in 2009<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>2 3 4 5 6 7 8 9 10 11 12 13 14 15 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>Poverty Line (USD at 2005 PPP) Poverty Line (USD at 2005 PPP)<br>Lower Bound Actual Upper Bound<br>Poor, Poor<br>Poor, Non-poor<br>Non-poor, Poor<br>Non-poor, Non-oor<br><!-- End of picture text --> Data source: INEI - Peruvian National Household Survey (ENAHO), 2008-2009. Note: Results are constrained to the panel sample of households whose heads are between 25 and 65 years old. Results are weighted using household-level survey-sampling weights. Upper bound estimations are based on 50 repetitions.

40
|P096198|2007|2012|3/11/2012|11 Closed|Niger|
|---|---|---|---|---|---|
|P096198|2007|2012|4/29/2012|12 Closed|Niger|
|P096198|2007|2013|11/3/2012|13 Closed|Niger|
|P096198|2007|2013|3/26/2013|14 Closed|Niger|
|P096198|2007|2020|6/22/2020|15 Closed|Niger|
|P096200|2006|2006||0 Closed|Bosnia and Herzegovin|
|P096200|2006|2007|10/23/2006|1 Closed|Bosnia and Herzegovin|
|P096200|2006|2007|4/9/2007|2 Closed|Bosnia and Herzegovin|
|P096200|2006|2007|6/28/2007|3 Closed|Bosnia and Herzegovin|
|P096200|2006|2008|11/1/2007|4 Closed|Bosnia and Herzegovin|
|P096200|2006|2008|5/23/2008|5 Closed|Bosnia and Herzegovin|
|P096200|2006|2009|10/29/2008|6 Closed|Bosnia and Herzegovin|
|P096200|2006|2009|1/12/2009|7 Closed|Bosnia and Herzegovin|
|P096200|2006|2009|4/29/2009|8 Closed|Bosnia and Herzegovin|
|P096200|2006|2010|7/21/2009|9 Closed|Bosnia and Herzegovin|
|P096200|2006|2010|12/5/2009|10 Closed|Bosnia and Herzegovin|
|P096200|2006|2010|4/6/2010|11 Closed|Bosnia and Herzegovin|
|P096200|2006|2011|9/16/2010|12 Closed|Bosnia and Herzegovin|
|P096200|2006|2011|1/24/2011|13 Closed|Bosnia and Herzegovin|
|P096200|2006|2012|9/18/2011|14 Closed|Bosnia and Herzegovin|
|P096200|2006|2012|12/13/2011|15 Closed|Bosnia and Herzegovin|
|P096200|2006|2012|3/18/2012<br>|16 Closed|Bosnia and Herzegovin|
|P096200|2006|2012|6/24/2012|17 Closed|Bosnia and Herzegovin|
|P096205|2007|2007||0 Closed|Albania|
|P096205|2007|2008|6/30/2008|1 Closed|Albania|
|P096207|2008|2008|6/26/2008|1 Closed|Ukraine|
|P096207|2008|2009|6/23/2009|2 Closed|Ukraine|
|P096207|2008|2010|2/27/2010|3 Closed|Ukraine|
|P096207|2008|2010|5/21/2010|4 Closed|Ukraine|
|P096207|2008|2011|11/28/2010|5 Closed|Ukraine|
|P096207|2008|2011|5/15/2011|6 Closed|Ukraine|
|P096207|2008|2012|1/11/2012|7 Closed|Ukraine|
|P096207|2008|2012|6/26/2012|8 Closed|Ukraine|
|P096207|2008|2013|12/19/2012|9 Closed|Ukraine|
|P096207|2008|2013|6/19/2013|10 Closed|Ukraine|
|P096207|2008|2014|12/26/2013|11 Closed|Ukraine|
|P096207|2008|2014|6/22/2014|12 Closed|Ukraine|
|P096207|2008|2015|1/7/2015|13 Closed|Ukraine|
|P096207|2008|2016|7/22/2015|14 Closed|Ukraine|
|P096207|2008|2016|12/1/2015|15 Closed|Ukraine|
|P096207|2008|2016|6/29/2016|16 Closed|Ukraine|
|P096213|2007|2007||0 Closed|Azerbaijan|
|P096213|2007|2008|8/14/2007|1 Closed|Azerbaijan|
|P096213|2007|2008|11/9/2007|2 Closed|Azerbaijan|
|P096213|2007|2008|2/26/2008|3 Closed|Azerbaijan|
|P096213|2007|2008|6/26/2008|4 Closed|Azerbaijan|
|P096213|2007|2009|12/7/2008|5 Closed|Azerbaijan|
_s r_ _s r_ Jones and Williams argue that for a very conservative<sup>estimateof 28%returnto R&Din</sup> the US, a long run 7% rate of return on<sup>the stock marketoverthelast century suggests</sup> that the U.S. should be investing perhaps 4<sup>times the presentR&D levelobservedin this</sup> country, which averaged approximately 2.6%<sup>of GDP during1995-2000.</sup>

# _B. Estimation of the rates of return_

We begin with a basic specification that can<sup>nest much of the existingwork on the</sup> empirics of economic growth: Where yi, dot is the log difference of per<sup>capitaGDP of countryi inperiodt,y,,tlis log</sup> income per capita at the beginning of<sup>theperiod,X thematrixof conditioning variables,</sup> in this case the growth of labor,<sup>**s,t**</sup> represents<sup>investmentsin both physicaland innovative</sup> capital expressed as a share of income.<sup>piis anindividualcountryfixedeffect,Rtisa</sup> sample-wide time effect, and **Eit** is a country<sup>and time specificeffect.</sup> One important type of omitted variable bias<sup>mightbeinduced bythecorrelation</sup> of unobserved country-specific factors<sup>andthevariablesofinterest;E(I.,,</sup><sup>**s,t)**maybe</sup> large. Casselli, Esquivel and Lefort (1996),<sup>for instance,pointedoutthatthedifference</sup> with respect to the highest level of income<sup>inthe sampleof countries(i.e.,thelevelto</sup> which she other countries are converging)<sup>acts as aproxy of the country-specific effectin</sup> cross sectional regressions, and thus the<sup>resultingestimatesare inconsistent.</sup> Panel data offers the only real solution<sup>totheendogeneityproblem throughthe</sup> use of lagged values as instruments for<sup>endogenousvariables. Theissue of unobserved</sup> country specific effects can also be<sup>addressedalthoughthestandardfixedorvariable</sup> 10
<!-- Start of picture text --> Figure 2: Savings in Colombia Adjused by Capital Gains<br>28<br>1951 1958 1965 1972 1979 1986 1993<br>Private Saving (% of GNP) - - - - - Public Saving<br>--- - - National Saving ------- Private Saving (%YD)<br>Source: National Accounts and Authors Calculations<br><!-- End of picture text --> (i) The adjusted series show greater fluctuation, particularly before 1967 when the exchange rate system was characterized by an adjustable peg. In that period several maxidevaluations of the nominal exchange rate were carried out leading to significant capital losses for the private and public sectors (Figure 3)7. However, the effects of the nominal devaluation upon the real exchange rate were quickly compensated by higher inflation and the saving rates returned to the levels attained before the adjustment (For a detailed description of that period, see Diaz-Alejandro (1976)). The differences between the corrected and traditional measures of savings are reduced after the implementation of the crawling peg system in 1967. An important exception occurs in the mid 1980s when the public sector suffered significant capital losses due to a real devaluation of the exchange rate at a time where external debt had increased. As a consequence, in 1985 and 1986 the public sector experienced capital losses of 3.2% and 4.0% of GNP, respectively, whereas the private sector's capital losses reached 1% of GNP in each of these years.

(ii) On average, between 1951-1993 the national saving rate suggested by the National Accounts was 0.2% of GNP higher than the adjusted measure. However, whereas the traditional measure of private saving was on average 0.6% of GNP below the adjusted measure, the National Accounts measure of public savings was 0.8% of GNP above the adjusted measure. As far as the private sector is concerned, the capital gains 7 The capital losses of the private sector are likely to be overestimated since the adjusted measure of saving does not include the income from private assets held abroad. In fact, it could be possible to argue that before the devaluation these assets were increased through unrecorded capital flight.

7