rafmacalaba/datause-encoder-data huggingface dataset:
**Table 5.5.1 : Additional US Data Included in CVD-USA-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In CVD-**|**USA-Master :**||
|**U.**|**CASE_ITA**|Case code associated with the subsidy investigation administered<br>by the International Trade Administration (ITA)|
|**V.**|**CASE_ITC**|Case code associated with the injury investigation administered by<br>the International Trade Commission (ITC)|
|**W.**|**ITC_REPCODE**|First (chronological) ITC case code for multiple country/identical<br>product investigations|
|**X.**|**INIT_DATE_ITA**|Date of initiation of the case at the ITA|
|**Y.**|**INIT_DATE_ITC**|Date of initiation of the case at the ITC|
|**Z.**|**TERM_DATE**|Date of the termination action|
|**AA.**|**P_CVD_DUTY**|The preliminary “all others” rate|
|**AB.**|**F_CVD_DUTY**|The final “all others” rate|
|**AC.**|**INIT_ITA_GP**|Citation to the_Federal Register_government publication for<br>initiation at the ITA|
|**AD.**|**INIT_ITC_GP**|Citation to the_Federal Register_government publication for<br>initiation at the ITC|
|**AE.**|**P_SUB_GP**|Citation to the_Federal Register_government publication for ITA<br>preliminary subsidy determination|
|**AF.**|**P_INJ_GP**|Citation to the_Federal Register_government publication for ITC<br>preliminary injury determination|
|**AG.**|**F_SUB_GP**|Citation to the_Federal Register_government publication for ITA<br>final subsidy determination|
|**AH.**|**F_INJ_GP**|Citation to the_Federal Register_government publication for ITC<br>final injury determination|
|**AI.**|**F_CVD_MEASURE_GP**|Citation to the_Federal Register_government publication for<br>imposition of countervailing measure order|
|**AJ.**|**TERM_GP**|Citation to the_Federal Register_government publication for<br>termination of the case|
|**AK.**|**REVOKE_GP**|Citation to the_Federal Register_government publication for<br>revocation of countervailing duty order|
|**AL.**|**SUSP_GP**|Citation to the_Federal Register_government publication for<br>suspension of the case and acceptance of a suspension agreement|
|**AM.**|**NOTES**|Comments|

# **In CVD-USA-Products :**

|**D.**|**TS_CODE**|Prior to 1989, the US did not subscribe to the HS system and<br>instead used the TSUSA system to classify imports, thus this<br>column reports the pre-1989 cases which referenced the<br>investigated products’ 5-7 digit TSUSA code|
|---|---|---|
|**E.**|**TS_DIGITS**|Number of digits of the TSUSA product code reported in<br>TS_CODE (e.g., 5,7, etc.)|
|**F.**|**CASE_ITC**|Case code associated with the injury investigation administered by<br>the US International Trade Commission (ITC)|
|**G.**|**NOTES**|Comments|

44
# **Template 5  Basic budgetary institutions, national level**

||Variable|Definition|Units|
|---|---|---|---|
||Fiscal: MTEF|Medium Term Expenditure Framework (MTEF)<br>process|1=yes, 0=no|
|TEF|Fiscal: MTEF-Budget|MTEF an integral part of the budgetary process|1=yes, 0=no|
|M|Fiscal: MTEF-Time|MTEF’s time horizon|INTEGER|
||Fiscal: Recent MTEF|Date of the most recent MTEF|YEAR|
||Fiscal-Unitary Budget|Unitary budget, for example, investment and recurrent<br>spending are integrated in a unique budget bill|1=yes, 0=no|
||Fiscal: Development Budget|Separate budget for investments|1=yes, 0=no|
||Fiscal: Development Budget<br>Coordination|Agency coordinating preparation of development<br>budget|0=Min Finance, 1=Min Planning,<br>2=Other|
||Fiscal: External Budget|Separate budget for externally funded budgets|1=yes, 0=no|
|t|Fiscal: External Budget<br>Coordination|Agency coordinating preparation of externally funded<br>budgets|0=Min Finance, 1=Min Planning,<br>2=Other|
|udge|Fiscal: Recurrent Budget|Separate budget for recurrent spending|1=yes, 0=no|
|B|Fiscal: Recurrent Budget<br>Coordination|Agency coordinating preparation of recurrent<br>spending|0=Min Finance, 1=Min Planning,<br>2=Other|
||Fiscal: Sequence Budget<br>Ceiling|Budget framework paper prepared prior to budget<br>preparation to guide sectoral budget proposals|1=yes, 0=no|
||Fiscal: Individual Strategic<br>Budgets|Sector ministries prepare individual strategic<br>expenditure plans (or their equivalent)|1=yes, 0=no|
||Fiscal: Investment Portfolio|There is a project investment portfolio or an<br>equivalent repository of bankable projects specific for<br>individual sectors|1=yes, 0=no|

# **Budgetary cycle diagram, national level (template 6)**

The budget cycle diagram provides a chronological scheme of the government budget cycle, including approvals. In the case of a dual budget system, the separate decision paths for the recurrent and development budgets should be well understood. If easily available, the decision path for SOEs and parastatals should also be understood and documented. This is the main objective of template 6.

Where possible a budget cycle should be obtained directly from the ministry of finance or other reliable source (for example, Public Financial Management [PFM] diagnostic studies). If not available from the ministry or other source, the budgetary cycle diagram can be developed jointly with the ministry of finance (or planning) using template 6 as a framework.

Template 6 identifies and organizes the following activities in the correct chronological order, and identifies the agency responsible for each:

- Budget circular drafting

- Budget circular approval

- Budget call

- Current budget guide distribution

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# **Question 4: Is There a Positive Association Between Skilled and Unskilled Migration?**

Efforts by migrant-sending countries to halt or reduce levels of skilled migration typically assume that high skilled migration is a phenomenon largely unrelated to less-skilled migration – the same countries concerned about outmigration of skilled workers typically welcome the remittances and job opportunities that migration provide for less-skilled workers. Likewise, immigrant-receiving countries may wish to reduce unskilled immigration while increasing skilled immigration. But the empirical evidence suggests that levels of skilled and unskilled migration actually have a strong positive association—and thus that attempts to limit one form of migration are likely to affect the other.

Both skilled and unskilled migrants are likely to determine their migration decisions in part on the basis of the institutional characteristics of their home country, and the presence of common links with potential destination countries. One specific avenue for a positive association between high and low skill migrants is family reunification. In the U.S. New Immigrant Survey, for example, 45.8 percent of spouses who are brought into the country on a family visa by a university-educated migrant do not have a university degree – evidence of a channel through which an increase in the number of high skill migrants may also result in the increase in the number of low skill migrants.

We analyze stocks of migrants to examine how the stocks of both high skill and low skill migrants evolve over time. We use two data sources: the database of Docquier and Marfouk (2005), which provides bilateral stocks of high and low skill migrants aged 25 and over from 198 source countries in 31 destination OECD countries in 1990 and 2000, and the U.S. Integrated Public Use Microdata Series (IPUMS) Census data, which provides similar stocks over the period 1960 to 2000 for the United States (Ruggles et al., 2010).

Figure 1 presents the relationship between the stocks of low skill and high skill migrants, with levels on the left and changes on the right. Each observation is a destination-source country pair in one year: for the OECD panels at the top there are multiple destination countries, whereas for the USA there is only one destination, but more years. The panels on the left plot the log stock of high skill migrants in each source-destination corridor against the log stock of low skill migrants, after removing any year and destination country fixed effects. The panels on the right also remove source country fixed effects, and so illustrate the correlation between the growth 9
# **List of Acronyms**

AIDS Acquired Immune Deficiency Syndrome ANC Antenatal Clinic AIS AIDS Indicator Survey ART Antiretroviral Therapy BAIS Botswana AIDS Impact Survey DHS Demographic and Health Surveys GARPR Global AIDS Response Progress Reporting HH Household HIV Human Immunodeficiency Virus UNAIDS United Nations Joint Program on AIDS UNGASS United Nations General Assembly

# **List of Tables**

**Table 1** . Types of HIV incidence data to be analyzed and comparisons made.......................21

**Table 2** . Summary of HIV incidence trends and ART coverage: Countries with a stabilized trend 2004-2012.........................................................................................22

**Table 3** . Summary of HIV incidence trends and ART coverage: Countries with a modest downward trend 2004-2012 .................................................................. 26

**Table 4.** Summary of HIV incidence trends and ART coverage: Countries with a moderate downward trend 2004-2012.............................................................. 34

**Table 5.** Summary of HIV incidence trends and ART coverage: Countries with a steep downward trend 2004-2012 ................................................................... 39

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impoverishment due to interannual climate variability that affects agricultural productivity, both in recent history as well as in the near future<sup>1</sup> . The next section describes the poverty profile of Tanzania, while section 3 provides details of climate volatility and agricultural variability between 1971 and 2031. Section 4 subsequently analyzes Tanzania’s poverty vulnerability, while section 5 concludes.

# 1. POVERTY PROFILE OF TANZANIA

Following the approach of Hertel et al (2004), the population as a whole can be divided into seven distinct strata, reflecting the pattern of household earnings specialization: Agricultural self‐employed (more than 95 percent of income from farming), Non‐Agricultural (more than 95 percent of income from non‐agricultural self‐employment), Urban Labor (more than 95 percent of income from wage labor), Rural Labor (more than 95 percent of income from wage labor), Transfer dependent (more than 95 percent of income from transfer payments), Urban Diverse, and Rural Diverse. As determined by the Household Budget Survey 2000/01, there were 12.3 million Tanzanians living below the national poverty line in 2001 (NBS, 2002)<sup>2</sup> .

Table 1 reports some key estimates of the structure of poverty in Tanzania, based on Tanzania’s national poverty line and the 2001 household survey (NBS, 2002). The rows in this table correspond to the seven strata and are therefore exhaustive of the Tanzanian population. The first column reports the poverty headcount rate in each stratum. This shows that the overall poverty headcount in Tanzania was about 36 percent. The estimated headcount rate was highest in the agriculture‐specialized stratum (68 percent), followed by the transfer‐dependent households (56 percent), the rural diversified stratum (51 percent) and then rural labor, urban diversified, non‐agriculture self‐employed and urban labor. Based on these figures, it is not surprising that the agriculture, transfer and rural diversified households all account for a larger share of the total poor in Tanzania (column II) than in the total population (column III). Taken together, the agricultural specialized and rural diversified households account for about 60 percent of total poverty in Tanzania.

Table 1: Socioeconomic Distribution of Tanzania by Earnings Based Stratum (in percent)

|Stratum|Stratum Poverty<br>Rate|Share in<br>Total Poverty|Share in<br>Total Population|
|---|---|---|---|
||I|II|III|
|Agriculture|68.79|29.95|15.54|
|Rural Labor|24.15|0.74|1.09|
|Rural Diversified|51.43|30.34|21.05|
|Non‐Agriculture|23.71|10.02|15.08|
|Urban Labor|12.24|3.40|9.91|
|Urban Diversified|23.24|23.44|35.99|
|Transfers|56.01|2.11|1.35|
|National|35.68|100.00|100.00|

Source: Authors’ estimates based on data from NBS (2002) 1 Henceforth referred to as poverty vulnerability.

2 The national poverty line is the basic needs poverty line defined in the Household Budget Survey 2000/01 (NBS, 2002), and is TShs 7253 (2001) without correcting for Purchasing Power Parity.

3
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# **Figure 4: Trends in service coverage and financial protection**

<!-- Start of picture text --> Argentina Brazil Chile China<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Colombia Costa Rica Ethiopia Georgia<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Ghana Guatemala India Indonesia<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Jamaica Kenya Kyrgyz Republic Mexico<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Nigeria Peru Philippines South Africa<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>Thailand Tunisia Turkey Vietnam<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>MDG Interventions Non-MDG Interventions Absence of catastrophic payments Absence of impoverishing payments UHC index<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br>100 100 100 100<br>80 80 80 80<br>60 60 60 60<br>40 40 40 40<br>20 20 20 20<br>0 0 0 0<br><!-- End of picture text --> Source: authors’ calculations using data described in text. Notes: MDG interventions include ANC, immunization, SBA, and treatment of childhood ARI and diarrhea. Non-MDG interventions include screening for breast and cervical cancer, and IP admissions. Financial protection reflects the narrow interpretation of impoverishment, i.e. households crossing the poverty line. Data are three-year averages, 1990-92, 1992-94, etc.

49
18

# 5. **Estimating the US' Share of Potential Rent**

To get a more accurate picture of rent sharing, we should<sup>focus on the</sup> amount of rent obtained by the United States as a<sup>proportion of the total</sup> potential rent generated by the MFA quotas rather than<sup>as a proportion of the US</sup> price. Recall our definition of the potential<sup>rent which is the difference</sup> between the demand price (i.e., the price in the<sup>United States) and the supply</sup> price (i.e, the Hong Kong price exclusive of the license<sup>price.) In the absence</sup> of rent sharing, the license price exactly makes<sup>up the difference. However, if</sup> rent sharing exists, then the license price falls<sup>short of the potential rent,</sup> with the remainder going to the US importers. In<sup>this section, we look at how the</sup> potential rent is divided between the Hong Kong<sup>exporters (in the form of the</sup> license price) and the US importers (in the form of rent.) As described in Morkre (1979, 1984) and Krishna,<sup>Erzan, and Tan (1991),</sup> quota licenses in Hong Kong are transferable to a certain<sup>extent. However, there</sup> is no systematic record of the transactions and we<sup>owe a great deal to Carl</sup> Hamilton at the University of Stockholm's Institute<sup>for International Economic</sup> Studies and Peter Ngan of the Federation of Hong Kong<sup>Garment Manufacturers, who</sup> provided us with monthly license prices for<sup>many MFA categories in several</sup> years.1<sup>9</sup> In order to get an idea of the license price<sup>for each apparel group, we</sup> had to aggregate our monthly license price data<sup>into the ten apparel groups and</sup> then average them over time.0 As can be seen from<sup>Table 3, the average license</sup> price ranges from $0.12 per piece for underwear<sup>to $9.10 per piece for skirts.</sup> Table 3 also gives the potential rent for each<sup>apparel group in each year,</sup> with the potential rent measured as license price<sup>plus marginal component of rent</sup> sharing. The rent estimates are obtained from Table<sup>2. As can be seen from Table</sup> 3, even if we assign the adjusted intercepts to represent<sup>quality differences and</sup> consider only the multiplicative component<sup>of rent sharing, then the US' share</sup> of the total potential rent is very substantial,<sup>ranging from 47 per cent for</sup> skirts in 1986 to 94 per cent for playsuits in<sup>1988.</sup>
even though there is no simple geographic pattern of malnutrition. At the commune-level, the correlation between the estimates of prevalence of stunting and underweight is 0.33. Red areas are the communes where the prevalence of stunting and underweight is both over 45 percent, which is approximately the national average for both stunting and underweight. Green areas have on the other hand prevalence of stunting and underweight both less than 45 percent. In terms of the number of communes, the green and red communes account for about 65 percent of all the communes.

The prevalence of both stunting and underweight is high in the most densely populated parts of Cambodia surrounding Phnom Penh, the provinces of Kandal, Prey Veng, Svay Rieng, Kampong Cham, and Kampong Chhnang as shown red in Figure 3(b). While these provinces are geographically close to each other, the causes of malnutrition may be quite different. Inspection of possible causes elucidates this point. For example, the prevalence of diarrhea for children under five is 29.7 percent in Kandal whereas it is only 3.1 percent in Prey Veng. Similarly, the prevalence of fever, which is a primary manifestation of malaria and other acute infections in children, is 46.8 percent in Kandal and 4.0 percent in Prey Veng (National Institute of Statistics et al., 2001). On the other hand, the poverty rates in Kandal and Prey Veng are estimated at 18.4 percent and 53.1 percent (Fujii, 2004). This suggests that malnutrition in Prey Veng is likely to be driven mainly by poverty, or more specifically lack of caloric intake, whereas infectious diseases seem important causes of malnutrition in Kandal.

Figure 3(b) also shows some noticeable differences between stunting and underweight. For example, most areas of Kampong Speu shown in pink have high levels of stunting but not underweight. This suggests that the nutritional status has improved recently, which is perhaps partly because of the recent improvement in road access in this province. Low levels of stunting and high levels of underweight as shown orange in Figure 3 reflect recent aggravation of nutritional status. Possible causes of aggravation include increased incidence 17
- 37 -

# 6. **Implications for Policy and Concluding Remarks**

6.1 This paper explored the strength of female secondary enrollment relative to, and/or<sup>in</sup> combination with some health and family planning program efforts to reduce fertility and<sup>infant mortality</sup> in the developing world. The relative strength of these strategies seems to vary at<sup>different levels of</sup> socio-economic development, suggesting the need for careful consideration of priorities especially<sup>in</sup> countries with low female secondary enrollments. The equation with 1975 enrollment<sup>rates suggests that</sup> effect of family planning services should be evaluated with reference to specific levels<sup>of female</sup> enrollments. The elasticity of the total fertility rate with respect to female secondary enrollments<sup>is</sup> consistently higher than for family planning services, ceteris paribus. Thus expansion in female<sup>education</sup> reduces fertility substantially and even more when family planning services accompany it. For<sup>example,</sup> a doubling of female secondary enrollments in 1975 from the mean of 19 percent to 38<sup>percent would</sup> have reduced the fertility rate from 5.3 to 3.9, whereas a doubling of family planning services<sup>in 1982</sup> from the mean of 25 to 50 would have reduced the total fertility rate from _5.5_ to 5.0. In countries where the female secondary education base is low, this research suggests that the expansion of female<sup>secondary</sup> education may be the best single policy lever for achieving substantial reductions in fertility. However, couples who desire smaller families certainly ought to have good family planning options at hand, and improvements in family planning may encourage more couples to opt for contraception.

6.2 With respect to infant mortality, public programs with reduction in the ratio of population per physician coverage do reduce infant deaths independently of female education. However, the elasticities of the IMR with respect to female secondary enrollments are always greater than with reduction in the ratio of population per physician coverage or GDP per capita. Our simulations show that the reduction in IMR from expansion of female enrollments is striking. For example, in a typical poor country with a GDP per capita of $300, female secondary enrollment of 10 percent, and a physician for