rafmacalaba/datause-encoder-data huggingface dataset:
# VI. Summary and Conclusions

# A.  A Summary of Our Suggested Approach

Avoided injuries and deaths are an important component of the benefits of disaster risk reduction projects, especially for projects to reduce losses from earthquakes and storms. To monetize these benefits requires first that the expected number of deaths and injuries avoided by quantified. Estimating the number of deaths and injuries prevented by a project is difficult; however, it should be noted that this is accomplished in the United States using FEMA’s HAZUS model, for earthquakes, storms and floods. It seems possible that similar models could be employed in a developing country context. If avoided deaths and injuries can be estimated by Maximum AIS code (MAIS), then the methods suggested in this paper can be used to produce order-of-magnitude calculations of the value of avoided death and injury associated with a project. These calculations can then be refined if including avoided death and injury among project benefits appears warranted.

Benefits transfer methods will, most likely, have to be used to value lives saved, as few reliable estimates of the VSL exist for middle income countries and none for low income countries. The information available from VSL studies in middle income countries suggests that the ratio of the VSL to per capita income is not constant across countries, as is assumed when the VSL is transferred from country A to country B by multiplying the VSL in A by the ratio of (incomeB/incomeA). Indeed, the ratio of the VSL to income appears to be lower in middle income countries than in high income countries. This may be true for several reasons. First, the elasticity of the VSL with respect to income may, indeed, be greater than 1, especially at low income levels. In addition, other factors that should affect the VSL—attitudes towards risk, the rate at which the future is discounted, and survival probabilities—may vary across countries. If survival probabilities are lower and discount rates higher in low income countries than in high income countries the ratio of VSL to income should fall as income falls.

How should VSLs be transferred from high income to low income countries? We suggest, while awaiting the results of more VSL studies in middle and low income countries, that transfers be made using both an income elasticity of 1, and an income elasticity of 1.5, to provide a range for the VSL. Some support for the 1.5 elasticity is provided by within-country (time series) estimates of the income elasticity of the VSL using data over long time periods. Costa and Kahn (2005), for example, estimate that the income elasticity of the VSL in the US is between 1.4 and 1.7, using data from 19401980.

How should avoided injuries be valued? Although there is considerable variation within MAIS category in medical costs, productivity losses and losses in Quality-Adjusted Life Years (QALYs), the Costs of Illness and Lost QALYs vary substantially by MAIS threatto-life index. Indeed, in the road safety literature (Robinson, 2007; IRAP, 2007), it is common to express injuries in terms of equivalent lives lost, based on the ratio of the 29
the non-lean season by 2.8 percentage points. Consequently, PRIME reduces year-round starvation by 6.6 percentage points.

Does the effect of PRIME differ from that of regular microfinance? Table 11 presents the estimates of the relative impacts of PRIME and regular microfinance on a household welfare set.<sup>24</sup> The impact estimates measure changes in household outcomes due to an intervention such as PRIME, given the availability of regular microfinance. We find that the relative impacts of PRIME differ slightly from its absolute effects, shown in Table 10. Wald test statistics also show that the impacts of PRIME and regular microfinance differ significantly different for three out of six outcomes, unlike the cross-sectional results, which show no significant differences between the two impact estimates for most outcomes (Table 8).

The results suggest that PRIME reduces seasonal starvation by 5.5 percentage points during the lean season, compared to a 3.9 percentage point increase (statistically insignificant) due to regular microfinance programs. Similarly, PRIME has a larger effect (7.2 percentage points) than regular microfinance programs (positive 3.5 percentage points, but statistically insignificant) on mitigating year-round starvation. But regular microfinance has a larger role than PRIME in mitigating food deprivation generally (i.e., when starvation and meal rationing are combined). Since the incidence of meal rationing is much higher than that of occasional starvation, we infer that regular microfinance has a relatively larger effect on reducing meal rationing than reducing the incidence of occasional starvation.<sup>25</sup>

> 24 This is done by implementing the DD method by a p-weighted regression after extending the concept demonstrated in equation (13); that is, assigning a weight of 1 for participants in PRIME and MFIs and an inverse of the probability of non-participation for nonparticipants.

> 25 It is possible that regular microfinance programs, because of their longer-term operation, are more suited to handling the general hardship associated with meal rationing than PRIME which helps alleviate more acute form of hardship (i.e., starvation). We re-ran the same regression with two types of microfinance memberships - those started before PRIME and those after PRIME (that is, after 2006). We find that longer exposure to

37
|Main income source: Hawking|(0.516)<br>0.117<br>(0.662)|
|---|---|
|Main income source: Remittances|-0.418<br>(0.569)|
|Main income source: Safety nets, cash transfers|omitted.<br>omitted.|
|Main income source: Stable business|-0.589**<br>(0.284)|
|Main income source: Public work|-0.764<br>(0.867)|
|Main income source: No income|-0.475<br>(0.871)|
|Observations|392|
|Prob > F|0.000114|

_Table 27: Descriptive statistics of households that have recovered after the flood and those that have not yet recovered two years after the event._

||Did not recover|Recovered|Difference|
|---|---|---|---|
|Real per capita annual expenditure|6,324|5,941|-383.2|
|Age of household head|44.9|45.1|0.2|
|Household size|3.8|3.7|-0.1|
|Percent of employed individuals in||||
|household|60.7%|62.1%|1.4%|
|Male-headed household|60.2%|51.8%|-8.4%|
|Education level of household head||||
|None|11.7%|10.2%|-1.6%|
|Less than primary|5.7%|10.1%|4.4%|
|Primary completed|12.5%|11.4%|-1.1%|
|JSS/JHS completed|27.1%|22.6%|-4.5%|
|Middle school completed|15.9%|17.0%|1.1%|
|SSS/SHS completed or above|27.1%|28.7%|1.6%|
|% of households in low elevation ar-||||
|eas|77.2%|75.1%|-2.1%|
|Tenure situation||||
|Owner|14.1%|24.6%|10.5%<br>**|
|Own dwelling but not land|3.3%|6.2%|2.8%|
|Rent from private person|38.1%|31.1%|-7.0%|
|Rent from relative|8.5%|3.4%|-5.1%|
|Rent free from relative|27.4%|27.2%|-0.2%|
|Other|8.7%|7.6%|-1.1%|
|Dwelling Type||||
|Separate house|0.0%|0.5%|0.5%|
|Semi-detached house|2.5%|6.9%|4.4%<br>**|

50
1985.<sup>15</sup> The 1975 migrant is observed in all three censuses, the 1985 migrant only in the last two censuses. The individual arriving in 1975 is 30 years old in 1980 (therefore 40 in 1990, and 50 in 2000), while the one arriving in 1985 is 30 years old in 1990 (and 40 in 2000). Since our hypothetical individuals have tertiary education, all predictions from this point on will be associated with skilled migrants, justifying the title of the paper.

Finally, our analysis produces the following eight sets of predicted occupational placement indicators for each of the 112 countries that will be used in section 3.2. to assess skilled immigrants’ performance over time and by country: (1)ÔP1980 , ÔP1990 , ÔP2000 , ΔÔP19801990<sup>, ΔÔP</sup> 1990-2000<sup>, for the hypothetical individual who arrived in the 1975; (2) ÔP</sup> 1990<sup>, ÔP</sup> 2000<sup>,</sup> ΔÔP1990-2000 , for the hypothetical individual who arrived in the 1985.<sup>16</sup> Summary statistics including size of cohorts, weighted and not-weighted, and information on sample structure by age, years since in the US and education are presented in Table 2. The first three columns refer to the individuals who arrived during the 1970s and whose performance levels are captured by Censuses 1970, 1980, 1990. Arrivals in the 1980s are summarized in columns 4 and 5. The first row indicates the size of the five samples, while the second row shows the equivalent in the whole US population. In principle, the numbers for each decade should decline across censuses due to return migration or mortality. Counter-intuitively, it can be observed that samples sometimes increase over time. For example, there are 1,668,342 migrants arriving in the 1980s according to 1990 census and they increase to 1,704,575 in 2000 census. The biggest increase in cohort size occurs between Census 1980 and Census 1990 for the 1970s arrivals (22,893 individuals). This may be due to measurement errors, including earlier undercounting of undocumented migrants and methodological differences across censuses. These issues will be detailed below. Despite the upward bias, the relative constancy in the shares of migrants by half-decade of arrival, as well as the stable proportion of the highly educated individuals (4+ years of college) across samples are positive signs that the data capture the same people over time.

The US Censuses are among the richest databases available. However, as mentioned before, they have caveats. First, it is impossible to follow the exact same individual through

> 15 Because variable “years since migration” is identified by half decades, the 1975 and 1985 arrivals refer to intervals 1975-1979 and 1985-1989 respectively.

> 16 To ensure readability, only the 80 most important sending countries are displayed in graphs and used as basis for the regressions of predicted indices on country-specific variables.  These 80 countries represent 97% of the male immigrants who were in the US as of Census 1990, and meet the criteria by which we constructed this paper’s dataset: employed, not living in group quarters, not in school, having entered the US in the 1970s or the 1980s.

10
# **LIST** OF **Y28L3U**

|Table 1.|Syndicate Bank and the Commercial Banking System,|
|---|---|
||Selected Years 1960-74 . . . . . . . . . . . . . . . . . . . 40|
|Table 2.|Syndicate Bank and the Banking System; Some Indicators,|
||1968 and 1975. . . . . . . . . . . . . . . . . . . . . . . . 41|
|Table 3.|Structure of Costs: Syndicate Bank, Selected Years|
||1950-75. . . . . . . . . . . . . . . . . . . . . . . . . . . 42|
|Table 4.|Cost-Structure: Loans and Advances, 1975. . . . . . . . . . 43|
|Table 5.|Cost-Structure: Deposits of Syndicate Bank, 1975. . . . . . 44|
|Table 6.|Establishment Cost a Percentage of Total Deposits,|
||Selected Years 1950-75 .................<br>.<br>.<br>45|
|Table 7.|Structure of Deposits, Selected Years 1950-75. . . . . . . . 46|
|Table 8.|Growth of Pigmy Deposits: Branch Category-wise, Selected|
||Years 1950-75. . . . . . . . . . . . . . . . . . . . . . . . 47|

iii
# **Eight Questions about Brain Drain**<sup>*****</sup>

[forthcoming in the _Journal of Economic Perspectives_ ] John Gibson, _University of Waikato_ David McKenzie, _World Bank, BREAD, CReAM and IZA._ _Keywords:_ Brain drain, Brain gain, High-skilled Emigration, Development.

> * We thank Albert Bollard, Matthew Groh and Melanie Morten for research assistance; Michael Clemens, Çağlar Özden and Hillel Rapoport and the journal’s editors’ for very helpful comments, and the World Bank and Center for Global Development for funding our surveys underlying some of this article.
- 13 -

Table 4. Beef Exports from Africa to EU Market, 1990–2004

||Annual export volume<br>(Intons)|Average price<br>(Ineuro per kg)|
|---|---|---|
|Angola|7.9|1.57|
|Benin|75.5|0.50|
|Botswana|7,213.9|2.13|
|Burkina Faso|2.3|3.33|
|Burundi|1.3|4.70|
|Chad|24.2|2.13|
|Comoros|32.7|0.90|
|Congo|79.9|0.56|
|Congo, Dem. Rep.|29.4|1.14|
|Eritrea|72.0|2.24|
|Ethiopia|16.6|3.81|
|Gabon|21.7|0.76|
|Ghana|26.0|0.48|
|Guinea|13.7|4.44|
|Liberia|6.3|1.48|
|Madagascar|700.4|1.48|
|Mauritius|86.7|0.97|
|Mayotte|67.7|0.79|
|Namibia|6,401.2|2.50|
|Seychelles|8.7|1.23|
|Sierra Leone|9.1|1.44|
|Swaziland|301.9|2.32|
|Uganda|42.8|1.52|
|Zimbabwe|4,026.3|2.29|

Source: _Intra- and Extra-EU Trade Statistics_ .

# **_Underutilization of beef preferences_**

It is common that the EU beef preferential export quotas are underutilized, meaning that the export volume is less than the preferential quota, regardless of their significant potential benefits. According to Manchin (2004), the utilization rate of Cotonou preferences in the agriculture sector averages 62 percent. The largest beneficiary, Botswana, has also failed to meet even half the quota for recent years. Madagascar has not exported beef to the European market since 1999, and Zimbabwe ceased exporting to the EU in 2001.

Table 5. Preferential Quota Utilization Rate, 1990–2004 <u>(In tons, unless otherwise indicated)</u>

||Preferential<br>export quota|Average export<br>volume|Utilization rate<br>(Inpercent)|
|---|---|---|---|
|Botswana|18,916|7,214|38.1|
|Kenya|142|0|0.0|
|Madagascar|7,579|700|9.2|
|Namibia|13,000|6,401|49.2|
|Swaziland|3,363|302|9.0|
|Zimbabwe|9,100|4,026|44.2|

Source: _Intra- and Extra-EU Trade Statistics_ .

There are several common reasons for Africa’s poor export performance in this industry. First, this partly results from accelerated diversification of livestock products in the region.
22 VI. THE DETERMINANTS OF IMPORTS: GRWOTH AND EXCHANGE RATES 39. Trade patterns differed markedly in the two halves of the 1980s. Both their actual evolution and the numbers reflecting it were much influenced by the decade's wild gyrations in exchange rates. Following its depreciation relative to European currencies after 1970, the dollar rose sharply from 1980 to 1985. The Yen tended to move with the dollar during this period, first appreciating less than European currencies (and more reluctantly) from 1970 to 1980, then depreciating less from 1980 to 1985. After 1980, the real exchange rates of most developing countries (not shown in Table 4) also depreciated quite sharply relative to the US dollar and the Yen; relative to the DM, there was modest depreciation for some currencies (Latin America), appreciation for others, notably the dollar and Yen-linked currencies of Asia.

40. In the late 1980s the DM-dolla **r** elationship returned to roughly its 1980 level. This time the Yen appreciated with the European currencies. Korea and Taiwan's exchange rates moved up relative to the dollar, though down relative to the Yen and DM. Most other developing countries' real exchange rates followed the dollar, and some (notably China's and India's) even continued to depreciate relative to it.

> TABLE 3.

NomiNAL AND REAL EXL-HANGE RATES.

||||1970|1975|1980|1985|1990|
|---|---|---|---|---|---|---|---|
||DM<br>100 YEN|per$ per$|3.66<br>3.60|2.46<br>2.97|1.82<br>2.27|2.94<br>2.39|1.62<br>1.45|
||YEN per|DM|98.4|120.7|124.7|81.3|89.38|
||REAL|EFFECTIVE|EXCHANGE<br>(1|RATES RE<br>985=1.00)|LATIVE TO<br>2|THE DOL|LAR.|
|||||1980|1985|<br>1990||
|DM||||1.459|1.000|1.575||
|YEN||||1.331|1.000|1.507||
|DM/YEN||||1.096|1.000|1.045||

41. The appreciation of the dollar during the early 1 980s caused the dollar value of EC GDP production to fall **by** 19 percent from 1980 to 1985, and that of manufactur_1_ **_Source: DnDnSdonALnandG a&Lcsa Yearbook 1992,_ Inteional** Monetary **Fund, Wshlzgton** DC, p. 99. **Basd on relative wholeal** prices; rccalculated to **bo expresd** in **ratio** of US Dollar and DM.
<!-- Start of picture text --> 80<br>© Water<br>50 y ey| . msP<br>20 i<br>10 aNN m Energy<br>—<br>0 m Edu<br>1 3 5 7 9 114 13 15 17 19 21 23 25 27<br><!-- End of picture text -->

<!-- Start of picture text --> 100%<br>_<br>90% ps ee_ —_ ~~ jj © Water<br>80% — ~~. ~ N i © Urban<br>70% Vw © Transport<br>60% mTMSP<br>50% SDV<br>40% § Health<br>30% mFPD<br>20% HEnv<br>10% @ Energy<br>0% m Edu<br>13 5 7 9 141 13 15 17 19 21 23 25 27<br><!-- End of picture text -->
# **Results**

Including all the asset variables of the 1999 Census dataset that are related to household economics (presented in Table 1 above) results in an asset index with 15 components. Thus when we perform the PCA the dimension of the variable will be 15, and the proportion of variance explained from the first PC will be compared with the total variance with 15 variables. If a smaller number of variables are used, the proportion of the variance explained from the first PC will be higher, but this PC will be based on a small number of variables with supposedly smaller variance, but also with less information about the household. In this paper we apply the PCA to the full correlation matrix of all 15 variables. Table 2 presents the result of the variance of the principal components analysis.

**Table 2: Total variance explained by each component.**

|_Component_|_Eigenvalues_|_Differences_|_Proportion _|_Cumulative_|
|---|---|---|---|---|
|1|4.767|2.881|0.3178|0.3178|
|2|1.885|0.653|0.1257|0.4436|
|3|1.231|0.105|0.0821|0.5257|
|4|1.125|0.143|0.0751|0.6008|
|5|0.982|0.180|0.0655|0.6662|
|6|0.801|0.040|0.0534|0.7197|
|7|0.760|0.115|0.0507|0.7704|
|8|0.645|0.088|0.0430|0.8134|
|9|0.556|0.064|0.0371|0.8505|
|10|0.491|0.036|0.0328|0.8833|
|11|0.455|0.023|0.0303|0.9136|
|12|0.431|0.059|0.0288|0.9424|
|13|0.371|0.097|0.0248|0.9672|
|14|0.274|0.057|0.0183|0.9855|
|15|0.217|.|0.0145|1.0000|

The principal components is a technique for extracting from a large number of variables those few orthogonal linear combinations of the variables that best capture the common information. The first principal component is a linear index of variables with the largest amount of information common to all of variables. In the present analysis, the first principal component explains more than 30 percent of the variation of the original variables and each subsequent 61
Leuven, E., H. Oosterbeek and H. van Ophem (2004). Explaining International Differences in Male Skill Wage Differentials by Differences in Demand and Supply of Skills. _Economic Journal_ 114: 466-486.

McIntosh, S. and A. Vignoles (2001). Measuring and Assessing the Impact of Basic Skills on Labour market Outcomes. _Oxford Economic Papers_ 53: 453-481.

Mincer, J. (1974). _Schooling, Experience and Earnings_ , Columbia University Press: New York.

Murnane, R., Willett, B. Duhaldebord, Y. and J. Tyler (2000). How Important Are the Cognitive

- Skills of Teenagers in Predicting Subsequent Earnings? _Journal of Policy Analysis and Management_ 19 (4): 547-568.

- __________ , Willett, B. and F. Levy (1995). The Growing Importance of Cognitive Skills in Wage Determination. _The Review of Economics and Statistics_ 77 (2): 251-266.

Murray, T, I. Kirsch and L. Jenkins (1998). Adult Literacy in OECD Countries: Technical Report on

- the First International Adult Literacy Survey. No. NCES 98-053, Washington D.C.: U.S. Department of Education, National Center of Education Statistics.

Nordin, M. (2008). Ability and Rates of Return to Schooling – Making Use of the Swedish Enlistment Battery Test. _Journal of Population Economics_ 21:(3): 703-717.

- Patrinos, H. and C. Sakellariou (2011). Quality of Schooling Returns to Schooling and the 1981 Vouchers Reform in Chile. _World Development_ 39 (12): 2245-2256.

- Psacharopoulos, G. and E. Velez (1992). Schooling, Ability and Earnings in Colombia, 1988. _Economic Development and Cultural Change_ 40 (3): 629-643.

Rouse, C. (1997). Further Estimates of the Economic Return to Schooling from a New Sample of Twins. Unpublished discussion paper, Princeton University.

22
# **Figure 9 Stock Markets**

# **Number of Firms Listed**

<!-- Start of picture text --> 2,55625564,413 5,863 4,730<br>2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>China India East Asia Eastern Europe Latin America G7<br>Market Capitalization to GDP<br>160%<br>120%<br>80%<br>40%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>Turnover Ratio<br>240%<br>200%<br>160%<br>120%<br>80%<br>40%<br>0%<br>China India East Asia Eastern Europe Latin America G7<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br><!-- End of picture text --> East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. Source: Standard and Poor's Global Stock Markets Factbook and World Bank World Development Indicators.
# **_Annex :  Computer Code for the Extended Model_**

'INDOGSS02.PRG Illustrates how to set up a CGE model based on an aggregate social accounting matrix for Indonesia in billions of 2002 rupiahs (Data Source: LDB on Line World Bank). The analytical structure of the model originates from Devarajan Shantayanan, Lewis Jeffrey D. and Robinson Sherman. 1990. Policy Lessons from Trade-Focused, Two-Sector Models. Journal of Policy Modeling 12(4):625-657.

'B. Essama-Nssah, PRMPR, The World Bank Group, September 14, 2003 WORKFILE INDOGSS02 U 5 '==========Set Up the Social Accounting Matrix==================================== 'Accounts: 1. ACT Activity; 2. COM Commodity; 3. HHD Household; 4.GOV Government; 5.SVI Capital Account; 6. ROW World; 7. TOT Total MATRIX(7,7) IDMACSAM 'ID is country Code for Indonesia 'Define the Columns of IDMACSAM FOR %AC ACT COM HHD GOV SVI ROW TOT VECTOR(7) V{%AC} NEXT 'Fill in IDMACSAM's Columns with Base Year Data VACT(3)=1538826 VACT(4)=71186 VACT(7)=@SUM(VACT) VCOM(1)=1040070 VCOM(4)=12005 VCOM(6)=447626 VCOM(7)=@SUM(VCOM) VHHD(2)=1042148 VHHD(4)=110845 VHHD(5)=405079 VHHD(7)=@SUM(VHHD) VGOV(2)=132219 VGOV(5)=61817 VGOV(7)=@SUM(VGOV) VSVI(2)=325334 VSVI(7)=@SUM(VSVI) VROW(1)=569942 VROW(3)=19246 VROW(5)=-141562 VROW(7)=@SUM(VROW) 'Load Vectors in IDMACSAM !COL=1 FOR %AC ACT COM HHD GOV SVI ROW COLPLACE(IDMACSAM,V{%AC},!COL) DELETE V{%AC} 29
**<mark>Through the Looking Glass: Can Classroom Observation and Coaching Improve Teacher Performance in Brazil?</mark>**

Barbara Bruns*, Leandro Costa**, and Nina Cunha***<sup>1</sup> JEL: I21, I28, H43, C52, C93.

Keywords: classroom observation, teacher feedback, coaching, instructional time, student engagement, student learning, and randomized control trial.

> * Center for Global Development

> ** World Bank

> *** Stanford University
27 spending categories; in fact, at the $1.25-a-day poverty line, these categories dominate the immiserizing spending category in Brazil and China. The fraction of nonspenders varies across the BRICS countries, with less than 30 percent of households recording no health spending in India, but over 70 percent of households recording no spending in South Africa. The ranking of the BRICS countries other than India on the financial protection index depends on the poverty line (Brazil and China slip down the ranking as the poverty line is raised) and on whether we put all nonspenders in the top category (the basic index) or only the nonspenders who receive care (the adjusted index); moving from the unadjusted to adjusted index makes a smaller difference to the scores of Brazil and Russia than it does to the scores of China, India and South Africa. Across the 51 countries, we see low adjusted financial protection index scores in Asia, and relatively high scores in the World Bank’s Europe and Central Asia and Latin America and Caribbean regions. Moving from the unadjusted to adjusted index makes relatively little difference in the European countries, but a sizable difference in countries like India and Ethiopia. This is important because computing the adjusted index will likely be impossible in household expenditure surveys, because of the lack of data on utilization.