rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3. Sugar Prices, 1970-2003**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1970 1975 1980 1985 1990 1995 2000<br>World EU Intervention U.S. Producer<br>Source: World Bank.<br>U.S. Cents/lbs<br><!-- End of picture text -->

# **_The EU Sugar Program_**

The EU sugar trade preference program has two parts; the ACP-EU Sugar Protocol (SP) and the Special Preference Sugar Program (SPS). The ACP-EU Sugar Protocol, was established in 1975 under the Lomé Convention to grant preferential access to 46 countries from Africa, the Caribbean, and the Pacific (ACP) with colonial or historical ties plus a special allocation for India. The ACP-EU Sugar Protocol of the Lomé Convention provided for imports of specified quantities of cane sugar, raw or white, which originated in the ACP states at guaranteed prices. Unlike most Articles of the Lomé Convention, the Sugar Protocol does not expire and cannot be changed unilaterally. The original quantities specified were 1,294,700 tons of white sugar equivalents, and an additional amount was allotted to India. The total import commitment was for 1,304,700 tons and this amount has remained constant with reallocation of quotas among existing members when a country did not fulfill its quota. The sugar imported under the Lomé Convention is known as ‘Preference Sugar’ or ‘SP’ sugar. The quantities specified for all ACP and Caribbean producers under the Lomé Convention are shown in Table 2. Notice that Suriname has lost its preference and Trinidad & Tobago have had their preference reduced since the original allocation. Barbados has recently had its quota reduced because it did not meet its quota due to declining production. While the quantities specified in the Lomé Convention cannot be unilaterally changed, the EU can unilaterally change the guaranteed price. This could occur as part of a change to the EU intervention price or as a change only for ACP sugar quota holders. The European Commission has recently proposed reducing the intervention price by 39 percent over two years starting in 2006 (European Commission 2005).

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agency's point of view to obtain earmarked resources, but there is no evidence chat recipients of earmarked resources are more efficient than other agencies or that--given the lack of identity between payozs and beneficiaries--the appropriate amount of resources is being allocated to these sectors. Hence, it is not clear that earmarking leads to an efficiency gain from the point of view of the country.

22. On the basis of the above arguments, the Commission concludes: (3) earmarking should be eliminated;<sup>12</sup> (b) the budgets of all agencies ought to be subject to the same budgetary process; (c) improvements in the administration of the revenue sharing arrangements appear to be desirable. While not pursuing this matter further, the Commission also expressed concern over the effects of payroll taxes on the cost of labor and hence upon employment.

# IV. AN EVALUATION OF EARMARKING

# A. Revenue Sharing

23. One of the prominent characteristics of Colombian pubiic finance is the significant transfer of revenues between levels of government and from the various levels to a number of decentralized agencios. In the main, the flow is from the central government proper to its own decentralized agencies and to lower levels of government and their decentralized agencies. Part of the transfers are discretionary, the amounts being decided each year as part of the budgetary process, but a growing proportion of transfers are automatic, the result of turning over revenues from specified taxes or fees to decentralized agencies and the sharing of 12/ Reference hers seems to be to a subset of earmarking that excludes revenue sharing, payroll taxes (except for SENA, ICBF, and the Cajas de Compensacion Familiar), and the profits of public enterprises.
|Table 4.1: Cluster Analysis,Argentine|Youth 2005||Clus|er number|||
|---|---|---|---|---|---|---|
||1|2|<br>3|<br>4|5|6|
|**Variable**|||||||
|Male|0.43|0.43|0.55|0.54|0.20|0.46|
|Age|18.45|18.93|18.33|18.54|21.38|19.89|
|Work|0.13|0.18|0.10|0.17|0.34|0.45|
|**Behaviors/Consequences***|**0.79**|**0.74**|**0.80**|**0.72**|**0.47**|**0.58**|
|_Not idle_|1.00|0.99|1.00|0.99|0.50|0.45|
|_Not dropped out of school_|1.00|1.00|1.00|0.96|0.62|0.28|
|<br>_Age started working_|0.69|0.57|0.64|0.65|0.49|0.56|
|<br>_Age at sexual initiation_|0.76|0.63|0.78|0.75|0.53|0.61|
|_Age at first child_|0.98|0.95|0.98|0.98|0.61|0.90|
|_Not married_|1.00|1.00|1.00|1.00|0.00|0.97|
|_Participate in societies_|0.01|0.11|0.31|0.15|0.09|0.07|
|_Not victim to violence_|0.94|0.88|0.92|0.81|0.90|0.89|
|_Not victim to crime_|1.00|0.80|1.00|0.00|0.71|0.82|
|_Do not drink or smoke_|0.28|0.38|0.23|0.29|0.33|0.38|
|_Do not use drugs_|0.97|0.92|0.97|0.93|0.92|0.90|
|Voted at last election|0.87|0.87|0.86|0.88|0.69|0.76|
|Do not have children|0.96|0.91|0.97|0.96|0.23|0.79|
|Have safe sex (or no sex)|0.68|0.54|0.73|0.66|0.18|0.47|
|Attend school|0.87|0.80|0.90|0.82|0.16|0.00|
|No early employment|0.65|0.49|0.57|0.60|0.46|0.49|
|<br>Education level|0.74|0.69|0.73|0.73|0.63|0.61|
|**Protective Factors***|**0.60**|**0.57**|**0.61**|**0.59**|**0.45**|**0.50**|
|_Trust in the government_|0.61|0.59|0.58|0.60|0.57|0.62|
|<br>_Trust in the media_|0.44|0.37|0.42|0.45|0.43|0.38|
|_Trust in the community_|0.47|0.50|0.47|0.46|0.45|0.45|
|_Living with parents_|0.76|0.71|0.75|0.71|0.22|0.67|
|No negative reasons for no jobs|0.93|0.94|0.93|0.91|0.91|0.83|
|Healthcare insurance|0.64|0.50|0.63|0.56|0.46|0.37|
|Member of sports or cultural society|0.32|0.41|0.50|0.42|0.17|0.26|
|<br>Incomeproxy|0.59|0.56|0.60|0.59|0.39|0.45|
|**Risk Factors***|**0.13**|**0.29**|**0.13**|**0.16**|**0.26**|**0.22**|
|_Domestic violence in household_|0.00|1.00|0.00|0.00|0.06|0.17|
|_Lack of parental education_|0.43|0.50|0.43|0.45|0.62|0.63|
|_Insecure neighborhood_|0.42|0.48|0.42|0.58|0.57|0.51|
|Indigenous|0.02|0.02|0.03|0.04|0.05|0.01|
|<br>Left school because of no motivation|0.02|0.00|0.02|0.03|0.08|0.16|
|Left school because of child|0.00|0.00|0.00|0.00|0.25|0.05|
|Left school to work in household|0.00|0.00|0.01|0.01|0.18|0.03|
|Number of observations|507|82|176|192|118|200|
|Proportion(percent)|39.8|6.4|13.8|15.1|9.3|15.7|
|Grou|Low|Medium|Low|Medium|High|High|
|p|Risk|Risk|Risk|Risk|Risk|Risk|

_Note_ : Italic indicates variables used for clustering. Because some variables are ranked the means do not always indicate the proportion of youth. * Average of the category. _Source_ : Own calculations on ESCVJA (four urban areas) 19
to 4.1% in 2000 and 5.4% in 2001. As Table 4.2 shows, however, less than half of the increase in the interest bill between 1995 and 2001 can be attributed to the rise in implicit interest rates on public debt -- which accelerated after the Russian crisis and especially in 1999-2001 due to the perceived weakening of Argentine fundamentals. The rest of the increase in the interest burden was due to the growth in the stock of outstanding debt. The additional deterioration in the Federal government's overall balance, beyond that due to changing fiscal stance and interest charges, can be attributed to the effects of the slowdown, and is captured by the gap between the two deficit measures in Figure 4.7.

**Figure 4.7. Current and Structural Overall Federal Budget Balance (Percentage of potential GDP)**

<!-- Start of picture text -->
1.5  -<br>0.5<br>0.0<br>-0.5  - I I  "a  Ra  o<br>-1.0<br>-1.5-<br>.2.0  13Structural<br>-'&Cyclical  component of GDP<br>-2.5-<br>-3.0-<br><!-- End of picture text -->

**Source:** Ministerio de Economia **de la** Republica Argentina **and** Ministero de Obras y Servicios Publicos.

More often than not Latin American fiscal problems have originated in booms, when weak fiscal institutions and policy complacency do not facilitate the achievement of surpluses. As a consequence fiscal policy has to be pro-cyclical also in bad times, contributing to a deepening of recessions and social tensions, and occasionally ending up in a severe fiscal crisis. Argentina in the nineties was no exception to this unfortunate Latin American policy tradition.<sup>34</sup>

> 34 The procyclicality of fiscal policy in LAC has been examined in Gavin and Perotti (1996); Gavin, et al. (1996) and Perry (2002)

35
55

# Table 26

# Regression Estimates for the Public Sector (t-values in brackets)

1. TFP - ao + alXl + 92X2 + 63X3 + Time dumies + Industry<sup>dumises + u</sup>

2. TFP - Po _+ plXl_ + 12X2 + p3X3 + Am + Time duuuies + Industry dummies<sup>u</sup>

|Independent<br>Variables|Model|1|I<br>Mode<br>I|l 2|
|---|---|---|---|---|
|Intercept|-.10|(1.1)<br>|1<br>-.10<br> <br>|(1.2)<br>|
|xi|.49|(4.9)|I<br>.47<br>|(4.7)|
|X2|-.59|(1.8)|I<br>-.44<br> <br>|(1.3)<br>|
|13|.09|(.2)|I<br>-.06|(.15)|
|m|||-.03|(1.5)|
|F-test for alm-62|.09||.01<br>||
|F-test for a1' a3|2.0|||<br>1.6||
||.211||.217||
|F-Test|2.59||1<br>2.60<br>||
|Number of|||I<br>||
|observations|185||I<br>165||

Xl - Growth in output attributed to growth in domestic demand. X2 - Growth in output attributed to grovth<sup>in import penetration.</sup> X3 - Growth in output attributed to export demand.

m - Growth in import penetration.
# **The Short-Term Impact of Inter-Community Volunteering Activities and Soft Skills**

# **Training on Self-Reported Social Cohesion Values:**

Quasi-Experimental Evidence from Lebanon Jumana Alaref (World Bank) Rene Leon Solano (World Bank) Carlos Asenjo (United Nations Organization on Drugs and Crime) Marwan Alawiye (Ministry of Social Affairs, Lebanon) Marwa El-Kik (Ministry of Social Affairs, Lebanon) _Keywords:_ social cohesion, civic engagement, employment, employability, volunteering, softs skills, impact evaluation, Lebanon _JEL_ classification: quantitative methods, econometrics, experiment, data collection
objective values (cost) and subjective indicators (perception). As a result of this classification the 46 variables are grouped in six sets which represent the backbone of the ICI and aim at measuring the cost and quality of infrastructure services, input markets, and institutions.

**Table 1. Variables used in the construction of the composite Investment Climate Index (ICI)**

|**INFRAS**|**TRUCTURE**|
|---|---|
|**COST**|**PERCEPTION**|
|1 Hours of power outages last year|1 Perception on electricity|
|<br>2 Hours of telephone outages last year|<br>2 Perception on telecom|
|<br>3 Percentage of sales lost in transit|<br>3 Perception on transport|
|<br>4 Percentage of sales lost due to power outages|<br>4 Perception on access to land|
|<br>5 Days of inventories kept for main input||
|(proxy for quality of transportation)||

and the ICI recalculated. The results were almost the same as in the original ICI. The correlation coefficient among the two indices was 90% with a significance level of 1%.

3
that the availability of long-term fixed-interest-rate loans in domestic currency reduces the indebtedness of households in foreign currency. According to the study, willingness to borrow in foreign currency would likewise decline if the monetary regime did not exhibit a ―fear of floating‖ behavior: in other words, if households had more experience in interest rate volatility. Last, it is important for banks to have access to long-term savings in domestic currency, which also facilitates home currency lending.

**Chart 8. Estimation of equilibrium credit/GDP and credit dynamics of nonfinancial corporations**

<!-- Start of picture text --> Per cent Per cent<br>40 4,0<br>35 3,5<br>30 3,0<br>25 2,5<br>20 2,0<br>15 1,5<br>10 1,0<br>5 0,5<br>0 0,0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Loan to GDP actual level Equilibrium level based on median of country constants<br>Equilibrium level dynamics (right-hand scale) Convergence dynamics (right-hand scale)<br>Average annual change (right-hand scale)<br><!-- End of picture text --> Source: Kiss et al. (2006); authors‘ calculations.

Banai et al. (2010a) examine the motivations of the supply side. They argue that the expansion of foreign banks in the region and the nearly unlimited foreign currency liquidity also contributed to the rise of foreign currency lending. Indeed, large European parent banks provided their Hungarian subsidiaries with easy access to foreign currency financing. Moreover, striving to maintain their high profitability, banks appearing in the region were engaged in a risk-based competition for the household segment, which also encouraged foreign currency lending.

In Hungary the main trigger of the upswing in retail foreign currency lending was the drastic cutback in the state subsidy on forint-denominated housing loans.<sup>7</sup> This increased the interest rate differential between forint-denominated and foreign currency loans (in particular, Swiss francs—CHF<sup>8</sup> ), which has been shown by previous research to be a driving force of foreign currency lending. Following its peak in 2003, the net flow of household loans declined

> 7 The study is not intended to examine either the reasons for the cutback, or its reasonability.

8 In Hungary the spread of CHF lending could be explained by the important role of Austrian banks. CHFdenominated loans were popular products in Austria and the outstanding volume of CHF loans to non-bank sector is still the highest in Austria among EU countries. In Rumania, Austrian banks were not really active in the pre-crisis period until 2007. In Slovakia and Czech Republic, FX retail lending was not significant. Baltic States and Bulgaria maintain an exchange rate fixed to the EUR so EUR-denominated loans were more favorable than other FX products. In Poland, rapid growth of CHF lending slowed down due to tightening regulations. Nevertheless, the share of CHF loans in total loan portfolio is significant.

10
# 'Iable 5

|Region||_Di_|_stance between A_|_ctual and Pr_|_edctfed GDP Per cav_|_ita Growth (Pred_|_ictioni ba_|_sed on Institu_|_tions Measures_|_)_||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Voice and<br>Accounta-<br>bility|Quality of the<br>Bureaucracy|Civil<br>Liberties|Property rights &<br>rule-based<br>governance|Government<br>Effective-ness|Graft<br>|Law &<br>Order<br>Tradition|Political<br>Instability &<br>Violence|Political<br>rights|Regulatory<br>Burden|Rule of<br>Law|Confidence in<br>Parliament|
|East Asia|Mean|3.21|2.68|3.28|3.15|2.60|2.75|2.60|2.64|3.18|2.64|2.51|3.04|
||Sid. Dev.|0.96|0.94|1.45|0.82|0.90|0.93|1.07|1.01|1.34|1.10|0.84||
||Obs|7|7|6|4|7|7|7|7|6|7|7|1|
|South Asia|Mean|0.61|0.86|0.65|0.63|1.40|1.20|1.47|1.53|0.33|1.10|0.80||
||Std. Dev.|1.02|0.54|0.90|0.74|0.38|0.35|0.53|0.92|0.89|0.82|0.86||
||Obs|4|3|4|4|3|3|3|3|4|4|4|0|
|Pacific|Mean|-1.37|-1.15|-1.22|-1.53|-1.26|-1.27|-1.17|-1.39|-1.16|-1.67|-1.23|0.53|
||Std. Dev.|0.40|1.11|0.75|.|0.65|0.93|1.02|0.61|0.64|0.78|0.77||
||Obs|3|3|3|1|3|3|3|3|3|3|3|1|
|North Africa<br>and Middle<br>|Mean|1.01|0.85|1.08|-0.17|0.67|0.68|0.82|0.94|1.01|0.89|0.56|-0.59|
|East||||||||||||||
||Std. Dev.|0.68|0.78|0.54|0.69|0.56|0.56|0.56|0.58|0.74|0.83|0.61||
||Obs|9|9|9|6|9|9|9|9|9|9|9|1|
|Subsahara<br>Africa|Mean|-0.32|-0.78|-0.18|-0.33|-0.31|-0.46|-0.74|-0.39|-0.09|0.30|-0.43|-2.20|
|,|Std. Dev.|1.77|1.94|1.71|1.72|1.98|1.83|1.86|1.83|1.67|1.69|1.70|1.07|
||Obs|22|16|22|22|20|20|16|20|22|22|22|2|
|Europe|Mean|-0.53|-0.51|-0.46||-0.60|-0.55|-0.49|-0.57|-0.46|-0.64|-0.45|0.08|
||Std. Dev.|0.80|0.91|0.80||0.91|0.94|0.85|0.87|0.81|0.81|0.89|0.95|
||Obs|15|14|15|0|15|15|14|15|15|15|15|11|
|North Americ|a Mean|1.01|0.85|1.08|-0.17|0.67|0.68|0.82|0.94|1.01|0.89|0.56|-0.59|
||Std. Dev.|0.68|0.78|0.54|0.69|0.56|0.56|0.56|0.58|0.74|0.83|0.61||
||Obs|9|9|9|6|9|9|9|9|9|9|9|1|
|Middle and<br>South Americ|Mean<br>a|-0.61|-0.13|-0.71|-0.27|-0.34|-0.24|-0.24|-0.44|-0.72|-0.97|-0.17|0.05|
||Std. Dev.|1.05|0.84|1.04|1.28|0.97|0.96|0.93|1.06|1.11|1.18|0.89|1.28|
||Obs|20|18|20|19|19|19|18|19|20|20|20|15|
# **4.2 Data**

To implement (8), we augment the FAI dataset with additional information on tax rates and macroeconomic conditions. Statutory tax rates are headline corporation tax rates drawn from the IMF World Tax Rate Database. Data on _KF_ - _METR_ s are from the Oxford University Centre for Business Taxation Database;<sup>34</sup> these, however, are available only for a subset of 46 out of 187 parent countries and for 28 of the 32 host countries in our dataset.<sup>35</sup> The FAS data provide useful information, at the bilateral host-parent level, on several further aspects of foreign affiliate activity, including sales, exports, number of workers, and value-added. Additional macroeconomic indicators are taken from the World Bank’s _World Development Indicators_ . Bilateral gravity variables are from CEPII;<sup>36</sup> we also control for standard gravity variables such as the physical distance between two countries, and common language or colonial ties that may shape cross-border investment. When estimating the effect of the host country _KF_ - _METR_ , and therefore, unable to include effects _aht_ , we instead include year fixed effects and several additional control variables, varying over both parent and host countries, which have been found to be important determinants of cross-border investment. These include country-level GDP, capital account openness, trade openness, exchange rate, inflation, and government expenditure.<sup>37</sup> We of course control for macroeconomic variables of parent countries when we include _aht_ .

Table 1 reports the descriptive statistics for the main variables used in the empirical analysis.<sup>38</sup> It remains to specify the weights used in constructing the rate differential term, ∆ _hp_ .

> 34At: https://oxfordtax.sbs.ox.ac.uk/cbt-tax-database.

> 35The exceptions are Cyprus, Malta, Latvia, and Lithuania.

> 36Centre d’Etudes Prospectives et d’Informations Internationales, see Head and Mayer (2014).

> 37Capital account openness is measured using the Chinn-Ito index, which is standardized between zero and one (Chinn and Ito, 2006), with higher values indicating greater openness. Trade openness is defined as exports plus imports as a share of GDP. The inflation rate is the annual percentage change in the consumer price index.

> 38Additional summary statistics (by host country) are provided in Appendix tables C.1 and C.2. The former summarizes the main tax variables, while the second summarizes the average, minimum, maximum number of parent countries per host country in a given year. The first and last years in the dataset with available FAI data are also listed.

18
# **Appendix 4: US Services Imports by Service Category, Ranked by Q4:2009 YTY Growth**

<!-- Start of picture text --> 12.5%<br>14.3%<br>‐21.3% ‐11.3% Passenger Fares<br>‐24.8%<br>‐21.0%<br>9.7%<br>2.3%<br>‐8.3% Total<br>‐12.2%<br>‐31.1%<br>‐14.4%<br>10.8%<br>‐3.1%<br>‐31.2% ‐22.3% Other Transportation<br>‐7.0%<br>‐5.2%<br>22.9%<br>0.4%<br>‐13.0%‐11.5% Royalties and License Fees<br>‐12.6%<br>‐4.1%<br>20.3%<br>2.6%<br>‐4.0%‐3.6% Direct Defense Expenditures<br>‐23.4%<br>1.2%<br>3.0%<br>‐1.1%<br>‐8.6% Travel<br>‐12.5%<br>‐3.4%<br>1.5%<br>7.6%<br>4.3%<br>‐1.6% Other Private Services<br>‐3.3%<br>‐8.8%<br>3.0%<br>5.7%<br>7.7%<br>2.0% U.S. Government Misc. Services<br>8.6%<br>5.0%<br>17.2%<br>Q3‐08 Q4‐08 Q1‐09 Q2‐09 Q3‐09 Q4‐09<br><!-- End of picture text --> Source: Own illustration. Data: Bureau of Economic Analysis, International Accounts Product, Monthly imports of services, balance of payments-based data, seasonally adjusted.

36
-5-

goods suffer from upward bias due to the neglect of quality change, an issue that has been discussed extensively in connection with the domestic producer and consumer price indexes. The two issues are related because it is the complexity and rapid change in specifications that lead to the omission of many products from most countries' price iindexes. Computers and computer accessories may be excluded completely, although they have become an increasingly important part of manufactured-goods trade.

To deal with these problems of measurement of manufactured goods prices in international trade, the National Bureau embarked on a program of international price measurement research beginning over 30 years ago. The present BLS export and import price indexes for the United States were to some extent an outgrcwth of those studies. The first phase of the National Bureau's research was summarized in Kravis and Lipsey (1971), and the latest publication is Lipsey, Molinari, and Kravis (1991). This research has produced new measures of the prices relevant to the explanation of trade flows. These are now being broadened with respect to the country coverage of the indexes and by experiments with methods of dealing with quality change. Some of the characteristics of these measures are:

1. Weighting. Two types of indexes of manufactured goods prices are constructed. The one discussed here, which is used, for example, in studying world trends in terms of trade, is a "world" or developed-country index of manufactured-goods prices, using as weights aggregate developed-country exports or developed-country exports to developing countries. Indexes for individual countries are also constructed based on these two sets of weights. A second type of index is intended mainly for studies of competition in international trade. It is calculated for the United States and for the aggregate of many of its major
_-_ _<u>Table 16: trade induced gains and losses (percentages with respect to pre liberalization levels)</u>_

|**Full lib.**<br>**(segmented)**|**CAFTA (N + US)**<br>**(segmented)**|
|---|---|
|**_Meanpercentage difference between simu_**<br><br>|**_lated and initial incom_**<br>|
|**All**<br>2.49|0.98|
|**Poor-Urban**<br>4.45|1.54|
|**Poor-Rural**<br>2.22|0.50|
|**NPoor-Urban**<br>2.13|1.13|
|**NPoor-Rural**<br>0.81|0.59|
|**_Meanpercentage differenceforgainers(%_**|**_)_**|
|**Poor-Urban**<br>5.06|2.10|
|**Poor-Rural**<br>3.67|1.68|
|**NPoor-Urban**<br>2.39|1.31|
|**NPoor-Rural**<br>1.86|1.33|
|**_Meanpercentage differencefor losers(%)_**<br><br>||
|**Poor-Urban**<br>-2.05|-1.90|
|**Poor-Rural**<br>-2.13|-1.83|
|**NPoor-Urban**<br>-1.97|-1.92|
|**NPoor-Rural**<br>-1.77|-1.64|
|**_Percentage of individuals with losses(%)_**||
|**All**<br>15.08|17.94|
|**Poor-Urban**<br>1.99|3.25|
|**Poor-Rural**<br>6.67|8.96|
|**NPoor-Urban**<br>2.06|1.97|
|**NPoor-Rural**<br>4.37|3.77|
|**_Percentage of individuals with losses(% of_**|**_each category)_**|
|**Poor-Urban**<br>8.59|14.06|
|**Poor-Rural**<br>25.00|33.59|
|**NPoor-Urban**<br>5.87|5.61|
|**NPoor-Rural**<br>28.89|24.89|

Note: Poor and non-poor are defined at the initial income level. The results are the case of the "full distribution" approach.

Having identified some variation in the effects of trade liberalization across households, it would be interesting to see how these effects vary with observed household characteristics; identifying strong correlates should help in designing compensatory policies. To do that, the logarithm of the change in per capita incomes<sup>27</sup> under different scenarios considered above is regressed on the age, age squared, gender and education level of household head, the logarithm of household size, the ratios of number of infants (age 0-5) and children (6-12) to household size, urban and regional dummies, the ratio of number of workers to household size, and the ratios of number of agricultural and skilled

> 27 These incomes are computed by separating out auto-consumption and using household-specific price indices. In addition, the simulated incomes are calculated under the full distribution approach.

41