rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6: Descriptive Statistics for the Residuals**

|**Variable**|**Max**|**Min**|**Mean**|**SV**|**CV**|**SD within**|**SD between**|**Number of**<br>**countries**|**Total**<br>**observations**|**Avg. number**<br>**of obs. per**<br>**country**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Log private credit to GDP**|1.44|-1.61|0.00|0.59|0.31|0.18|0.59|155|986|6.36|
|**Log total private credit to GDP**|1.39|-1.67|0.00|0.60|0.30|0.18|0.60|155|985|6.35|
|<br>**Log deposits to GDP**|1.33|-1.31|0.00|0.47|0.24|0.12|0.48|155|981|6.33|
|**Log total bank assets to GDP**|1.44|-1.50|0.00|0.51|0.29|0.15|0.51|154|984|6.39|
|<br>**Log net-interest margin**|1.17|-1.09|0.00|0.43|0.43|0.17|0.40|158|984|6.23|
|**Log interest spread**|1.29|-1.21|0.00|0.42|0.50|0.19|0.39|150|815|5.43|
|**Log bank overhead to total assets**|1.36|-1.43|0.00|0.52|0.44|0.21|0.48|157|993|6.32|
|**Log ATMs per 100,000 people**|1.56|-1.67|0.00|0.68|0.00|0.00|0.68|88|88|1.00|
|**Log of synthetic access**|0.87|-1.05|0.00|0.37|0.00|0.00|0.37|157|157|1.00|
|**Log deposits per 1,000 people**|1.49|-1.77|0.00|0.71|0.00|0.00|0.71|54|54|1.00|
|**Log number of bank branches per 100,000 people**|1.39|-1.36|0.00|0.60|0.00|0.00|0.60|97|97|1.00|
|**Log stock market capitalization to GDP**|2.29|-2.63|0.00|0.85|0.32|0.28|0.87|109|665|6.10|
|**Log stock market value traded**|3.93|-4.22|0.00|1.43|0.49|0.67|1.37|111|670|6.04|
|**Log number of listed firms per mln. inhabitants**|2.72|-2.63|0.00|0.93|0.37|0.35|0.93|117|704|6.02|
|<br>**Log market cap. of 10 largest firms**|0.42|-0.72|0.00|0.24|0.36|0.09|0.24|76|362|4.76|
|<br>**Log of closely held shares**|0.76|-1.17|0.00|0.38|0.33|0.13|0.39|49|264|5.39|
|<br>**Log total public offerings to GDP**|2.46|-4.66|0.00|1.33|0.75|0.83|1.11|45|282|6.27|
|<br>**Log stock price synchronicity**|1.04|-1.09|0.00|0.39|0.96|0.27|0.28|61|292|4.79|
|**Log stock market turnover**|3.10|-3.02|0.00|1.06|0.59|0.57|0.97|112|676|6.04|
|**Log private bonds to GDP**|1.75|-2.41|0.00|0.79|0.23|0.19|0.82|41|242|5.90|
|**Log private bonds turnover**|2.72|-5.31|0.00|1.73|0.68|0.99|1.46|31|55|1.77|
|**Log value new, dom. private bonds to GDP**|4.69|-6.10|0.00|1.86|0.59|1.02|1.74|38|173|4.55|
|**Log public bonds to GDP**|1.19|-1.64|0.00|0.50|0.35|0.18|0.53|48|280|5.83|
|**Log public bonds turnover**|3.75|-7.15|0.00|2.83|0.17|0.49|2.93|31|52|1.68|
|**Long minus short government yield**|7.56|-11.43|0.00|2.19|0.81|1.50|1.86|45|224|4.98|
|**Log assets of mutual funds**|2.24|-1.72|0.00|0.88|0.00|0.00|0.88|99|99|1.00|
|<br>**Log assets of insurance companies**|2.01|-1.90|0.00|0.74|0.00|0.00|0.74|98|98|1.00|
|**Log assets of pension funds**|2.67|-2.86|0.00|1.25|0.00|0.00|1.25|100|100|1.00|
|**Log assets of institutional investors**|2.14|-1.83|0.00|0.89|0.00|0.00|0.89|99|99|1.00|

22
<!-- Start of picture text --> 50<br>0<br>-50<br>-100<br>-150<br>-200<br>Borrowing limit improves<br>-250<br>2019 2021 2023 2025 2027 2029<br>—— Borrowing limit (excl. Glencore amortization)<br>—— Borrowing limit<br><!-- End of picture text -->
consented that the research team access their clinic and hospital records to collect the latter information.

# **2.3 Socio-demographic and other characteristics of the study population**

Table 1 reports selected baseline characteristics of the FEATS patient study population. Study participants were mostly in their late 30s. As is the case in the ARV program in general, approximately three in four study participants were women. Two-thirds of patients enrolled into the FEATS study were single, while one in four lived with their partner or spouse. Three-quarters lived in formal dwellings and one in five in informal dwellings. Only in one in five participants had completed secondary school or held some tertiary education, while almost half had some level of secondary education [grade 8-11]. Households generally included 2-4 members, one in three of which represented a child or elderly person. With the exception of age (attrited study participants were marginally, but statistically significantly younger than study participants interviewed in both rounds of follow-up interviews), socio-demographics did not differ statistically significantly across FEATS study participants that stayed in the study and those that were lost to follow-up, thus suggesting the absence of major attrition bias.

# **3. Methods**

Adherence outcomes for study participants were measured as follows: a continuous and dichotomous version of the CASE index of self-reported adherence (Mannheimer et al.. 2006); a continuous, normalized index of self-reported adherence derived from five categorical variables on self-reported adherence using multiple correspondence analysis (MCA adherence index); a selfreported, dichotomous 0/1 variable for a missed clinic or hospital visit; and timeliness for scheduled clinic and hospital visits, which represents a more objective proxy of adherence than self-reported adherence (Blacher et al., 2010; Kunutsor et al., 2010). Delays in clinic and hospital visits, used here to measure timeliness of visits, were calculated by subtracting the scheduled visit dates from the actual visit dates for consecutive clinic and hospital visits. The latter variable was also converted into a dichotomous variable indicating whether the patient arrived three or more days late for the particular visit (=1) or not (=0). Immunologic response to ARV treatment is measured using CD4 counts (cells per microliter). (In the case of timeliness of clinic and hospital 8
Table 1: Decomposition of aggregate convergence

||1975-2018||1975-1995||1995-2018||
|---|---|---|---|---|---|---|
|Sector|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|
|Aggregate|-0.00976**<br>(0.00484)|0.238|-0.00289*<br>(0.00155)|0.088|-0.00695***<br>(0.00234)|0.266|
|1. Agriculture|-0.00255***<br>(0.000493)|0.308|-0.000874***<br>(0.000316)|0.113|-0.00181***<br>(0.000270)|0.331|
|2. Mining|-0.000264|0.025|-2.13e-05|0.000|-0.000388|0.025|
||(0.000212)||(0.000304)||(0.000254)||
|3. Manufacturing|-0.00122***<br>(0.000460)|0.105|-0.000231<br>(0.000223)|0.018|-0.000628*<br>(0.000361)|0.032|
|4. Utilities|-0.000138<br>(9.67e-05)|0.033|1.34e-05<br>(4.18e-05)|0.002|-0.000162**<br>(7.98e-05)|0.043|
|5. Construction|-9.95e-06|0.001|2.63e-05|0.002|-4.48e-05|0.004|
||(5.34e-05)||(8.02e-05)||(7.22e-05)||
|6. Trade services|-0.000300**<br>(0.000118)|0.097|-0.000174<br>(0.000149)|0.022|-0.000251<br>(0.000155)|0.028|
|7. Transport and Finance services|-0.000633***<br>(0.000206)|0.137|-0.000142<br>(0.000203)|0.008|-0.000520**<br>(0.000229)|0.054|
|8. Other services|-0.000399*<br>(0.000236)|0.046|-7.98e-05<br>(0.000156)|0.004|-0.000507**<br>(0.000298)|0.044|
|Total within sectoral effect<br>(1+2+3+4+5+6+7+8)|-0.00551***<br>(0.00139)|0.207|-0.00148<br>(0.000953)|0.039|-0.00431***<br>(0.000995)|0.171|
|Static sectoral effect|-0.00148<br>(0.00162)|0.014|-0.000516<br>(0.00207)|0.001|-0.00277**<br>(0.00138)|0.043|
|Dynamic sectoral effect<br>Observations|-0.00276*<br>(0.00155)|0.050<br>60|-0.000890<br>(0.00179)|0.004<br>60|0.000134<br>(0.00115)|0.000<br>91|

Notes: Regressions <u>(2) and (3) are estimated. The standard errors are reported in parentheses. The constant terms</u> are not reported.<sup>_∗∗∗_</sup> p<0.01,<sup>_∗_</sup> p<0.05,<sup>_∗_</sup> p<0.1 Table 2: Sector-specific convergence

||1975-2018||1975-1995||1995-2018||
|---|---|---|---|---|---|---|
|Sector|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|
|Agriculture|-0.000588|0.015|0.000587|0.005|-0.00127**|0.022|
||(0.000621)||(0.000885)||(0.000623)||
|Mining|-0.00377**|0.093|-0.00272|0.027|-0.00681***|0.123|
||(0.00152)||(0.00165)||(0.00220)||
|Manufacturing|-0.00244***|0.130|-0.00175|0.038|-0.00221***|0.054|
||(0.000777)||(0.00106)||(0.000829)||
|Utilities|-0.00300**|0.087|-0.00251*|0.045|-0.00463***|0.091|
||(0.00132)||(0.00147)||(0.00172)||
|Construction|-0.00172*|0.070|-0.00373*|0.123|3.72e-05|0.000|
||(0.000982)||(0.00208)||(0.00113)||
|Trade services|-0.00226***|0.136|-0.00253**|0.077|-0.00157*|0.031|
||(0.000732)||(0.000976)||(0.000938)||
|Finance and business services|-0.00315***|0.246|-0.00246**|0.095|-0.00309***|0.129|
||(0.000776)||(0.00104)||(0.000810)||
|Other services|-0.00154**|0.096|-0.000230|0.001|-0.00293***|0.125|
||(0.000741)||(0.000730)||(0.00275)||
|Observations||60||60||91|

Notes: Regression (6) is estimated. The standard errors are reported in parentheses. The constant terms are not reported.<sup>_∗∗∗_</sup> p<0.01, _∗_ p<0.05, _∗_ p<0.1 20
one. Next, we compute the monetized losses in 2019 U.S.$ by multiplying our estimates of premature deaths by the country and year-specific VSL estimates. Figure 8 plots the evolution of these drought-induced losses in LAC. The figure reveals that yearly median losses are in the order of $2.4 to $12 billion. An analogous exercise using the Liu et al. (2019) CRF shows median losses of $0.5 to $4.9 billion (Figure A3 in the appendix).

Figure 8: Value of statistical lives lost. Based on the concentration-response function of Deryugina et al. (AER, 2019) for adults 65 or older and extrapolated US VSL from Banzhaf (JBCA, 2022).

<!-- Start of picture text --> 2000 2005 2010 2015 2020<br>Year<br>15000<br>10000<br>Losses (millions $US 2019)<br>5000<br><!-- End of picture text --> _Notes:_ This figure plots the evolution of the losses from lives lost in LAC. Details of the calculation of premature deaths are presented in Section 4.2. The spread of the box plot displayed each year results from the uncertainty in our estimation of excess PM2 _._ 5 concentrations and the value of the observed FHD. Dots represent outliers.

Importantly, these monetized longevity losses are significantly underestimating the total social cost of worsened air quality triggered by droughts. Our counterfactual simulations do not account for the fact that the absence of excess PM2 _._ 5 would not only lead to health improvements that extend life but also increase the quality of life (Murphy and Topel, 2006). Additionally, our loss estimates do not account for non-health costs, such as effects on productivity and cognitive ability (Adhvaryu et al., 2022; Kahn and Li, 2020; Zivin and Neidell, 2012). Recent lit22
data on immigrants’nationality are grouped into Indonesians, Filipinos and the rest of the world, we add our measure of the supply of immigrants for all other countries into a single category, such that effectively we have three source countries: Indonesia, the Philippines and Other.

What remains to be determined is the industries and regions within Malaysia in which the immigrants choose to work. In order to construct this variable, we use the LFS for the period 1990 to 2002 to calculate the average probability of individuals from a source country and age group to be employed in a certain industry-region where Mrst<sup>acisthenumberofimmigrantsfromasourcecountryinanagegroup,region,</sup> industry and year, and Mt<sup>ac</sup> is the total number of immigrants in Malaysia from a source country and in an age group.

The source country and age-group specific instrument for the immigration flows in a certain, industry, region and year is given by: We have three source countries (Indonesia, Philippines and Other) and seven age groups for a total of twenty-one instruments. The identifying variation comes from the interaction migrate to Malaysia. This is so as to ensure that the magnitudes of the coeffi cients on each instrument are broadly comparable. These propensities are calculated from data provided by the Ministry of Home Affairs of Malaysia and are: Bangladesh 1.96%, Cambodia 1.03%, India 0.11%, Lao PDR 0.01%, Myanmar 2.18%, Sri Lanka 0.16%, Thailand 0.22%, Vietnam 0.78%, Indonesia 5.56% and Philippines 0.38%.

27
diversification, the pension law specifies that the maximum limit on foreign securities holdings, to be determined by the Board of the Central Bank, will be no less than 10%19 and no more than 50% and only instruments traded in the New York Stock Exchange and the London Stock Exchange qualify.

**Compensatory Pensions.** Those affiliates who have contributed to the old pension system will receive a compensation for the rights they acquired from the State. This compensation will be paid monthly upon retirement. For those that have more than 60 contributions (equivalent to five years of work), the monthly compensation will be calculated by the formula:

Years * 0.7 Compensation -------------x last monthly salary 25 where "Years" is the number of years, or fraction thereof, contributed to the old system. For example, for someone who has contributed for 25 years, the above formula implies that, upon retirement, the State will replace 70% of the monthly salary of October 1996 for those employed at the time or the last monthly salary before this date for those that were unemployed at the time. Both the reference salary and the compensatory pension are subject to a ceiling of 20 times the minimum wage. This amount will be indexed in US dollar; thus, there will be an implicit tax on the compensation that is equivalent to the US inflation between October 1996 and the time of retirement. For those who have less than 60 contributions, the compensation will be a lump-sum equal to 100 times the amount given by the above formula up to a payment of 20 times the minimum wage.

> **'9** The 10% limit is the lowest level that the Central Bank can set for the maximum percentage the AFP can invest in foreign securities. The AFP can choose to invest nothing in foreign securities or any amount up to the maximum limit. At the time of writing this paper, the investment regulations have not been issued by the Central Bank The issue is not immediately relevant since the AFPs are initially required to invest a sizable part of their funds in treasury bonds.

9
Policy Research Working Paper 8178

# **Abstract**

Public works programs can be effective safety nets if they help allocate resources toward poor households. By setting wages lower than market rates public works programs identify poor households reasonably well. When these programs are oversubscribed and lack beneficiary selection rules however, discretion by local politicians can influence their distribution and their effectiveness as safety nets. This paper tests this hypothesis using household survey data on a seasonal public works program in Bangladesh. The results show access to local politicians is a significant determinant of participation, and can increase the relative probability of participation by 110 percent. Participation has a positive impact on food and nonfood consumption of poorer participants. The same is not true for less poor participants. The results suggest rather than relying on local politicians, public works aiming to maximize their impact on poverty should rely on an objective and transparent targeting system that ensures participation of larger numbers of poorer households.

This paper is a product of the Social Protection and Labor Global Practice Group and the Jobs Cross Cutting Solution Area. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The authors may be contacted at isharif@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **South, East, and Southeast Asia and Oceania**

**AMERICAN SAMOA:** The IMR is the average from vital registration estimates for 1987 and 1988, as published in _PVSR_ 1/90 and 10/90). Life expectancy at birth is derived from Coale-Demeny model life tables selected on the basis of the IMR and the vital registration based CDR

AUSTRALIA: Both the IMR and life expectancy at birth are based on average vital registration data for the period 1985 to 1989 as published in _PVSR._ BANGIADESH: The IMR measured by the vital registration system for 1987 is used. Also considered is the 1989 Bangladesh Fertility Survey, which provides retrospective estimates that are consistent **with** the vital registration system. Life expectancy at birth estimates are from _World Population Prospects 1990,_ and are derived from model life tables selected on the basis of infant and child mortality.

BHUTAN: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ Official estimates of the IMR and Coale-Demeny 'West' model life tables were used.

BRUNEI: The IMR for 1985-89 is the average of vital registration estimates for 1986 and 1988 _(PVSR_ 1/90 and 10/90). Life expectancy at birth is estimated from Coale-Demeny "West" model life tables, using IMR and CDR from vital registration _(PVSR_ 1/90 and 10/90) as inputs.

CHINA: Both the IMR and life expectancy at birth are Bank projections from estimates by Hill (1988) for 1982. The projections are consistent with the results of the 1987 National Population Survey and the 1988 National Family Planning Survey.

FEDERATED STATES OF MICRONESIA: Both the IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1989).

FIJI: Both the IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1989).

**FRENCH POLYNESIA:** Both the IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1989).

GUAM: Both the IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1990).

HONG KONG: The IMR is the average of the official estimates based on the vital registration system for 1987 and 1988, as published in _PVSR_ 10/89 and 10/90. Life expectancy at birth is from _World Population Prospects 1990,_ and is calculated from vital registration data through 1987.

INDIA: The IMR is from _World Population Prospects 1990,_ and is based on sample registration data through 1986. Life expectancy at birth is also from _World Population Prospects 1990,_ and is based on sample registration data through 1987, adjusted for incompleteness of registration of all deaths.

INDONESIA: The IMR is derived from an indirect estimate from the 1987 National Contraceptive Prevalence Survey (DHS) for 1982-87, adjusted upwards to account for exclusion of certain high mortality areas. Life expectancy at birth is estimated from Coale-Demeny 'North' model life tables, selected on the basis of 1980 census analysis and the estimated IMR.

17
**Table 2. Correlations between the Self-Reported Food Security Indicator and Other Indicators of Income, Poverty, Hunger, and Malnutrition, Circa 2005**

|**Alternative poverty/hunger indicator**||**Self-reported**|
|---|---|---|
|**(source)**||**hunger**|
|GDP per capita, purchasing power parity, log|Correlation|−0.71***|
|(World Bank)|No. of obs.|44|
|Household income per capita, USD, log|Correlation|−0.68***|
|(World Bank Povcal)|No. of obs.|59|
|Prevalence of hunger|Correlation|0.58***|
|(FAO)|No. of obs.|62|
|Prevalence of poverty, USD 1/day|Correlation|0.77***|
|(World Bank Povcal)|No. of obs.|58|
|Prevalence of poverty, USD 2/day|Correlation|0.67***|
|(World Bank Povcal)|No. of obs.|49|
|Prevalence of low-BMI women, excluding outliers|Correlation|0.73***|
|(DHS & WHO)|No. of obs.|17|
|Prevalence of underweight preschoolers, log|Correlation|0.55***|
|(DHS & WHO)|No. of obs.|45|
|Prevalence of stunted preschoolers, log|Correlation|0.48***|
|(DHS & WHO)|No. of obs.|45|

_Source_ : Dependent variable is from the GWP (Gallup 2011) . The sources of the independent variables are as follows: _World Bank_ , World Bank (2010b) _WDI_ ; _World Bank Povcal_ , World Bank (2010a); _FAO_ ; Food and Agriculture Organization (2011); _DHS_ ; Demographic Health Surveys (2010); _WHO_ , World Health Organization (2010).

_Note_ : *, **, and *** indicate significance at the 10 percent, 5 percent, and 1 percent levels, respectively. All variables are measured in 2005 or the nearest available year. _Log_ indicates that variable is expressed in logarithms to account for a nonlinear relationship. _Excluding outliers_ refers to the exclusion of six countries with the highest prevalence of low-BMI women in the sample, all above 20 percent: India, Bangladesh, Ethiopia, Cambodia, Nepal, and Madagascar. Without this exclusion, the correlation is statistically insignificant. Samples vary in size because of the paucity of some of the poverty and malnutrition indicators.

In table 3, we also show that the GWP food insecurity indicator is significantly explained by “relative food prices,” which is measured as the ratio of the purchasing power parity for food items to the exchange rate (both measured in 2005). This index can be interpreted as the extent to which a country’s food basket is expensive or cheap relative to the costs of importing food 12
**<u>Table 5: Cobb Douglas Production function estimates</u>**

||**Log of output (in USD)**||
|---|---|---|
||**Pooled OLS**<br>**Rayon FE**|**Farm FE**|
|Log(Area)|0.212***<br>0.236***|0.341***|
||(0.016)<br>(0.015)|(0.013)|
|Log(Area)*post 2006|-0.048***<br>-0.020|-0.050***|
||(0.018)<br>(0.015)|(0.015)|
|Log(Labor)|0.222***<br>0.189***|0.168***|
||(0.009)<br>(0.007)|(0.006)|
|Log(Labor)*post 2006|-0.137***<br>-0.104***|-0.051***|
||(0.010)<br>(0.009)|(0.008)|
|Log(Seed)|0.154***<br>0.163***|0.139***|
||(0.009)<br>(0.007)|(0.006)|
|Log(Seed)*post 2006|0.091***<br>0.069***|0.049***|
||(0.014)<br>(0.013)|(0.010)|
|Log(Fertilizer)|0.117***<br>0.121***|0.073***|
||(0.005)<br>(0.004)|(0.003)|
|Log(Fertilizer)*post 2006|0.012**<br>0.010**|0.015***|
||(0.006)<br>(0.005)|(0.004)|
|Log(Capital depreciation)|0.072***<br>0.066***|0.066***|
||(0.006)<br>(0.004)|(0.004)|
|Log(Capital depreciation)*post|0.041***<br>0.014**|-0.004|
|2006|(0.007)<br>(0.006)|(0.006)|
|Log(Fuel and energy)|0.319***<br>0.240***|0.162***|
||(0.012)<br>(0.009)|(0.007)|
|Log(Fuel and energy)*post 2006|-0.104***<br>-0.061***<br>(0.014)<br>(0.012)|0.006<br>(0.011)|
|Log(Other – agricultural services)|0.076***<br>0.048***<br>(0.004)<br>(0.003)|0.043***<br>(0.002)|
|Log(Other – agricultural|0.029***<br>0.049***|0.042***|
|services)*post 2006|(0.005)<br>(0.004)|(0.004)|
|Observations|89,736<br>89,736|89,736|
|R-squared|0.860<br>0.828|0.607|
|Sum of coefficients period 1|1.171***<br>1.062***|0.991|
|Sum of coefficients period 2|1.056***<br>1.019***|0.999|
|No of rayons/farms|480|16,191|
|Joint test of interaction(F-value)|80.41***<br>60.15***|32.68***|

Note: standard deviations in brackets; Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; Deviation from 1 is tested for the sum of coefficients 22
<!-- Start of picture text --> » 200<br>=<br>a» 150<br>F<br>= 100<br>=<br>o 50<br>i<br>—<br>S 0<br>=<br>4 -50<br>ey)<br>F= -100<br>= @ ites nS (s) “~ nO Bees<br>” RS x» we <e » RNS Vr<br>Oy S ANG<br>AS 5<br>@ maintenance norm Omaintenance and rehabilitation norm<br><!-- End of picture text -->
<!-- Start of picture text --> All<br>Subject Products Non-Subject Products<br>filing filing<br>-25 -20 -15 -10 -5 0 -25 -20 -15 -10 -5 0<br>months months<br>subject countries all countries subject countries all countries<br>.2 .2<br>.1 .1<br>0 0<br>-.1 -.1<br><!-- End of picture text --> Figure 1: Subject and all sources imports: HS-10 vs HS-4 17