rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 6.** Distribution of test scores of students in government and private schools

# **a) 15-year-olds, 2009, Himachal Pradesh and Tamil Nadu, India: PISA Test**

<!-- Start of picture text --> Reading Math Science<br>100 200 300 400 500 600 100 200 300 400 500 600 100 200 300 400 500 600<br>Standardized score Standardized score Standardized score<br>Government Private aided Private unaided<br>.008<br>.006<br>.004<br>Frequency distribution<br>.002<br>0<br><!-- End of picture text -->

# **b) 8–11 year-olds, 2004–05, India: India Human Development Survey**

<!-- Start of picture text --> Reading Arithmetics Writing<br>Government Private aided Private unaided<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>0 0 0<br>Not read Read letterRead word Read Read story Not recognize Recognize Do subtraction Do division Write with 3 or more Writes with 2 or less<br>paragraph number number mitakes mistakes<br><!-- End of picture text --> 16
# **5.  Estimating the gains from trade:  an illustration**

In this section, we undertake a cost comparison of selected medical treatments to illustrate the potential gains from trade in health care for the US. The biggest challenge in making such a comparison is the difficulty in obtaining the relevant data. We needed data on (a) prices of the surgeries in the US, (b) the magnitude of the demand for the surgeries in the US, (c) prices of the surgeries in other countries, and (d) differences in the quality of services between the US and other countries.

# _Identifying tradable medical procedures_

The first step, however, was to identify the medical procedures that are clear candidates for consumption abroad. It became apparent that it was only for surgeries that the cost savings are likely to be large enough to cover the cost of travel. For minor treatments, e.g., the treatment of hypertension, the cost differential is too small to justify travel. Furthermore, purely diagnostic procedures, such as the interpretation of CT scans, are already being traded electronically cross-border, without requiring the movement of the patient to the foreign country.<sup>21</sup> In collaboration with medical professionals, we developed the following set of criteria:<sup>22</sup>

- The surgery constitutes treatment for a non-acute or non-traumatic condition;

- The patient must be able to travel without significant pain or inconvenience;

- The surgery is fairly simple and commonly performed with insignificant rates of post-operative complications;

- The surgery requires minimal follow-up treatment on site;

- The surgery generates minimal laboratory and pathology reports; and

- The surgery results in minimal post-procedure immobility.

Applying these criteria extremely stringently to the list of the 230 most commonly performed procedures in US community hospitals, published by the Agency for Health care Research and Quality, we selected 15 procedures for price comparison purposes. These procedures are listed in Appendix 1 along with data on patient volume, and summary statistics such as mean charges, mean length of hospital stay, and surgical mortality rates.

# _US prices_

Hospital mean and median “billed charges” were available for each procedure in the Health care Cost and Utilization Project (H-CUP, hereafter) Nationwide Inpatient Sample

> 21 The five radiologists working for Wipro Ltd., and based in India, who interpret 30 CT scans a day for Massachusetts General Hospital, are an example of such cross-border trade in health services that does not require movement of the patient to the foreign country.

> 22  The criteria were developed after consultations with Drs Grizzard & Sorock at the World Bank Health Services Department.

16
Durand (1994) note that the first migrants usually come from the lower middle ranges of the socioeconomic scale. As such it is not clear whether they will tend to be negatively or positively selected on average compared with non-migrant households.

We employ an instrumental variables strategy in order to separate the effect of migration from the impacts of these selection mechanisms. There is substantial evidence that migration networks play an important role in determining migration from Mexico.<sup>8</sup> Following Woodruff and Zenteno (2001) and McKenzie and Rapoport (2004) we use historic state-level migration rates as an instrument for current migration stocks. The U.S. migration rate in 1924 for the state in which the household is located is taken from Foerster (1925). Figure 1 shows the geographical distribution, with the highest rates found in the West-Central states of Michoacán, Jalisco, Zacatecas and Durango, along with a couple of border states, Sonora and Coahuila. The lowest rates are not surprisingly found in the most southern states of Quintana Roo, Chiapas and Yucatán. Nevertheless, it is not simply the border states which have high migration, and one sees reasonable variation in migration rates among states of equal distance from the border. These historic rates can be argued to be the result of the pattern of arrival of railroads into Mexico coupled with U.S. demand conditions arising from restrictions on immigration from Asia at the turn of the century. Massey, Durand and Malone (2002) outline how these conditions led to some states having different initial migration rates than others. These initial migration networks then lowered the cost of further migration. This is argued by Massey, Goldring and Durand (1994, p 1496) to result in “a self-reinforcing process that...over time...becomes increasingly independent of the conditions that originally caused it”.

As a consequence, by virtue of living in a community with high levels of early 20<sup>th</sup> century migration, a household will have a higher likelihood of having a migrant member than an otherwise identical household living in a community with low initial migration rates. We therefore use the 1924 state migration rate as an instrument for whether a household had a migrant member in 1994 and for household migration prevalence. Table 2 shows the first-stage results for our instrumental variables estimation. Columns 1 and 2 show that the historic

> 8 See Massey, Goldring and Durand (1994), Winters, de Janvry and Sadoulet (2001) and Munshi (2003) among others.

- 8 -
The mortality experience of these cohorts of children during the age interval 7-35 months was compared using life table techniques. The results of this analysis demonstrated a peonounced impact of measles vaccination upon childhood mortality levels (Figure 1). Mortality among the cohort of vaccinated children was substantially lower than among the three control groljps of unva*ccinated children: 39 per 1000 vs. 69-95 per 1000 durAng the age period 7-35 months. This translates into mortality reductions of between 44 to 59 percent among vaccinated children, depending upon the control group chosen. Differences between groups were most marked during ages 7-21 months, the period corresponding to the greatest risk of measles-related mortality.

Two othec studies from Africa merit consideration. A study in Guinea-Bissau compared the subsequent one year mortality experience of two groups of vaccinated children in 1980 and 1981 with two groups of children from the same area and periods who did not attend a measles campaign (Aaby, et al. 1984). Although the numbers in the study were comparatively small, especially the number of unvaccinated children (803 vaccinated and 193 unvaccinated children), the results indicated a markedly lower nortality level during the following year among immunized cnildren-- as much as 72 percent lower among children aged 6-35 months. In a second study, Garenne and Cantrelle (1986) analyzed the impact of two measles vaccination campaigns based on data from the Khombole area of Senegal over the 1965-68 period. The subsequent mortality experience of vaccinated children aged 6 months to 10 years in a set of villages selected for a measles vaccination program was compared with a control group of children from other villages where measles vaccination had not been offered. The findings of the study indicated a reduction in mortality among younger vaccinated children (6 months to 3 years) of 31 percent. The reduction in mortality among older children (3-10 years) was more modest (14 percent).

The two other major studies of mortality impact originate from a measles vaccination program in the Matlab study area of the International Centre for Diarrhoeal Disease Research (ICDDR,B) in rural Bangladesh. Since 1966, the ICDDR,B has maintained a system of continuous demographic surveillance in its rural field site in Matlab, consisting of the bi-weekly registration of vital events and periodic censuses. In 1982, the study area consisted of 149 villages with a total population of approximately 180,000. In 1978, half of the study area was designated as an intervention area for an experimental maternal and child health/ family planning program provided by the ICDDR,B; the other half remained a comparison area, with services limited to the less intensive government program. In early 1982, as part of a carefully phased expansion of services, measles vaccination was introduced into two of the four sub-areas of the intervention area (termed blocks); this service was expanded to 5
_Designing Household Survey Questionnaires for Developing Countries: Lessons from 15 years of Living Standards Measurement Study_ , Ch 22, 249-272, Washington, DC: World Bank.

- Hirshleifer, J (1970), _Investment, Interest and Capital_ , Prentice-Hall Inc., Englewood Cliffs NJ.

- Ilahi, N (2000), “The Intra-household Allocation of Time and Tasks: What Have We Learnt from the Empirical Literature”, Policy Research Report on Gender and Development, Working Paper Series No 13, World Bank, Washington, DC.

- Ilahi, N (1999a), “Children’s Work and Schooling: Does Gender matter? Evidence from the Peru LSMS Panel Data”, World Bank, Latin American and Caribbean Region, Poverty Reduction and Economic Management Unit, Washington, DC.

- Ilahi, N (1999b), “Gender and the Allocation of Adult Time: Evidence from the Peru LSMS Panel Data”, Background paper for Engendering Development, World Bank, DC.

- Ilahi, N and F Grimard (2000), Public Infrastructure and Private Costs: Water Supply and Time Allocation of Women in Rural Pakistan”, _Economic Development and Cultural Change_ , 45-75.

- Jones, C (1986), “Intrahousehold Bargaining in Response to the Introduction of New Crops: A Case Study from North Cameroon.  In JL Moock, ed, _Understanding Africa’s Rural Households and Farming Systems_ ,

- Juster, FT and FP Stafford (1991), “The Allocation of Time: Empirical Findings, Behavioral Models, and Problems of Measurement”, _Journal of Economic Literature_ , 29, 471-522.

- Killingsworth, M and J Heckman (1986), “Female Labor Supply: A Survey, in O Ashenfelter and R Layard, eds., _Handbook of Labor Economics_ , Vol 1, NorthHolland, Amsterdam, 103-204.

- Klevmarken, NA (2002), “Microeconomic Analysis of Time Use Data: Did We Reach the Promised land”, in J Merz and M Ehling, eds, _Time Use – Research, Data and Policy_ , NOMOS Verlagegesellschaft, Baden-Baden, 423-456.

Lokshin, MM (2000), “Effects of Child Care Prices on Women’s Labor Force Participation in Russia”, Policy Research Report on Gender and Development, Working Paper Series No 10, The World Bank.

Lokshin, MM, E Glinskaya and M Garcia (2000), “The Effect of Early Childhood 62
58. In general, housing and real estate property represent an important share of household wealth in total household net worth.<sup>38</sup> Wealth effects from the real estate market and the recovery of the equity market made households feel richer, and hence, consume more. Financial innovations allowed households to refinance their mortgages, and capitalize their wealth gains in the housing markets.

59. Consumption, as well as household debt, increased rapidly during this period. Figure 38 shows that in 2001, U.S. household debt as a percentage of household disposable income was 100 percent. By 2007, it had increased to 132 percent. This 32 percentage point increase in household debt was mainly used to support household consumption. The boom was not only limited to the housing market. The U.S. stock market also increased by more than 50 percent from June 2003 to October 2007.<sup>39</sup> Figure 39 shows the evolution of households‘ net worth relative to disposable personal income. Disposable personal income has been constantly increasing over 1986-2009. However, net worth fell from 1999-2002 to rise again from 20022006. Hence, during 2002-2006 households saw their wealth increase as a consequence of soaring housing and equity markets.

**Figure 38: Household Debt as a Percentage of Personal Disposable Income**

<!-- Start of picture text --> 130%<br>120%<br>110%<br>100%<br>90%<br>80%<br>Sources: Bureau of Economic Analysis Data (2009), Federal Reserve, Flow of Funds Account (2009).<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->

> 38 Data from the Flow of Funds Accounts of the United States of the US Federal Reserve indicate that the share of real estate in total net worth for households and non-profit organizations was less than 30% in 2000, peaked at 40% in 2005 to drop to 37% and 34% in 2008 and 2009 (Q3), respectively.

> 39 World Bank staff estimates (2010).

45
the VSIC two-digit level using the correspondence file between HS 96 and ISIC (Rev. 3) which in turn was downloaded from the U.N. Statistics Division website.

- (c) Impact of a change in exports on wage growth

# (i) Model specification

This section investigates the effects of exports on _absolute_ wages for skilled and unskilled workers running regressions separately by skill levels. I specify a model which relates wage growth rates to changes in the province-specific Exports Index.

_∆Ln Wip = β0 + β1 ∆Exportp + β2 ∆Xip + β3 Yip+ eip_ (2) where _∆Ln Wip_ is the monthly wage growth rate during 2002-2004 of an individual _i_ who lives in province _p_ , _∆Exportp_ is the change in Export Index for province _p_ between 2002 and 2004, _∆Xip_ is a vector of differenced control variables, and _Yip_ is a vector of other control variables which may affect wage growth rates. The monthly wage calculations are based on the annual wages earned and the hours worked by panel individuals during the twelve months prior to the survey.<sup>34</sup> Monthly wages rather than hourly wages are employed in the model as they reflect both the compensation per unit of time and hours worked by individuals. Taking account of hours of work is potentially important since the trade-oriented work may involve long hours and the working hours appear to vary substantially in Vietnam across economic sectors. For instance, the panel individuals in the VHLSS reveal some evidence that those who moved from an informal to the formal sector increased their hours of work.<sup>35</sup>

> 34 The wages include bonus/award, social allowances, and trip subsidy. The wages are converted to January 2002 constant prices using the deflators available in the VHLSS.

> 35 According to the panel individuals in the VHLSS, the workers who moved from the informal wage and selfemployed sectors to the formal sector increased their hours worked by 8.3 percent and by 24.3 percent respectively. Unfortunately, the income changes for those who moved from self-employed to formal sector jobs are not reflected in the paper because no 2002 wage information was available for self-employed individuals.

20
for firms in the other regions – 29 percent in Arusha, 30 percent in Zanzibar and 20 percent in Mwanza – suggesting that these regions are poorly served by qualified training providers, both public and private, offering appropriate training in needed skill areas.

Further evidence of shortfalls in the local supply of high-skill workers is that some firms turn to foreign skill sources (e.g. UK, Kenya, Uganda, India, China and Turkey) for university and TVET skills and training. These firms are primarily from Zanzibar, Dar es Salaam and Arusha, a reflection of widespread shortfalls in the local supply of higher-level skills.

|**Table 5. Location of Majo**|**r Skill Sou**<br>|**rces Relati**<br>|**ve to Reg**<br>|**ion of Res**<br>|**pondent**<br>|
|---|---|---|---|---|---|
||Region<br>|of Firm Re<br>Dar es|spondent (<br>|% of total<br>|sources)<br>|
|Location of Skill Source|Arusha|<br>Salaam|Mbeya|Mwanza|Zanzibar|
|**_1. Universities_**||||||
|Same region as Firm|37.5|543|0.0|33.3|37.9|
|Dar es Salaam|25.0|.|33.3|43.3|33.3|
|Rest of Tanzania|25.0|41.1|66.7|23.33|4.6|
|International|12.5|4.6|0.0|0.0|24.2|
|**_2. TVET & Other Tertiary_**||||||
|SameregionasFirm|16.7|714|33.3|50.0|48,9|
|Dar es Salaam|50.0|.|66.7|50.0|42.6|
|Rest of Tanzania|33.3|28.6|0.0|0.0|2.1|
|International|0.0|0.0|0.0|0.0|6.4|
|**_3. Secondary Schools_**||||||
|Same region as Firm|33.3|658|100.0|87.9|77.6|
|Dares Salaam|50.0|.|0.0|9.1|9.0|
|Rest of Tanzania|16.7|34.2|0.0|3.0|10.4|
|International|0.0|0.0|0.0|0.0|3.0|
|**_4. External Training_**||||||
|SameregionasFirm|71.4|794|28.6|60.0|45|
|Dar es Salaam|28.6|.|14.3|20.0|30.00|
|Rest of Tanzania|0.0|5.9|57.2|20.0|5.0|
|International|0.0|14.7|0.0|0.0|20.0|
|Source:TESS,2015.||||||

39
64 current situation in financial markets, the economic situation of the pension funds themselves, and possibly an increased risk aversion of pension fund managers.

|||**Table**<br>|**22 Inve**<br>|**stment**<br>|**s**<br>||||
|---|---|---|---|---|---|---|---|---|
|in M. CHF|199||1998||2000||200|2|
|||in %||in %||in %||in %|
|Liquid assets and|||||||||
|short-term|33,045|9.6%|39,614|9.3%|36,051|7.4%|44,821|10.3%|
|Investments|||||||||
|Investments<br>in<br>employer|<br>44,085|12.8%|46,918|11.0%|30,448|6.3%|11,735|2.7%|
|Bonds|108,084|31.4%|131,720|31.0%|152,936|31.4%|155,193|35.5%|
|Mortgages|26,552|7.7%|26,338|6.2%|24,731|5.1%|22,486|5.1%|
|Shares|66,201|19.2%|108,212|25.5%|160,947|33.0%|107,412|24.6%|
|Real estate|54,347|15.8%|56,346|13.3%|61,242|12.6%|64,610|14.8%|
|Others|12,195|3.5%|15,921|3.7%|20,639|4.2%|30,823|7.1%|
|Total|344,509||425,069||486,994||437,080||
|Net Inv. Income<br>|14,092|1|4,478|1|5,234||11,947||

Source: Bundesamt für Statistik (2004a)

**Figure 22  Investment Positions held by Pension Funds**

<!-- Start of picture text --> 40%<br>35%<br>30%<br>1996<br>25%<br>1998<br>20% 2000<br>15% 2002<br>10%<br>5%<br>0%<br>Liquid Investments Bonds Mortgages Shares Real estate Others<br>assets, in employer<br>short-term<br>Investments<br><!-- End of picture text --> Source: Bundesamt für Statistik (2004a) Figure 23 breaks down the positions held in bonds. Bonds of Swiss debtors make up almost half of the investments in bonds. Another quarter of the investments are effected in foreign bonds in foreign currency. In 2002, foreign bonds in CHF and Swiss mortgages were equally important, although the share of mortgages has decreased, and the share of foreign bonds in CHF has increased. Mortgages in foreign properties are not important at all.
<!-- Start of picture text --> Changes in Diversification Performance During Commodity Boom<br>Confidence Intervals for Resource Poor & Resource Rich<br>.2<br>.1<br>0<br>-.1<br>-.2<br>-.3<br>Resource Poor Resource Rich<br>Share of Top 5 Markets<br>Number of Markets<br>Herfindahl Index - Markets<br>Proportional Change<br><!-- End of picture text --> Figure 11 shows that while the number of markets served increased during the period for resource-poor countries (they have penetrated into eight new markets, on average, which represents, roughly, a 15 percent increase), this is not the case for resource-rich countries, where no significant increase in the number of markets served by their exports is observed.<sup>9</sup> At the same time, while during the period resource-poor countries have not increased dependence on their top 5 destinations, resource-rich countries’ dependence has increased mildly.

Source: Authors’ calculations.

# **Country-Specific Destination - Diversification Statistics**

As in the case of diversification indicators along the product dimension, heterogeneity is revealed when we zoom into country-specific trends. Russia, Ukraine and Uzbekistan decreased the number of markets served during the period, and increased their dependence on their top 5 destinations, which resulted in an increase in the Herfindahl index of concentration. In Uzbekistan, for example, while the number of markets served decreased only moderately during the period, its dependence on the 5 most important export destinations increased dramatically, from 45 percent in 2002/03 to roughly 82 percent in 2010/11. In contrast, other economies, such as Azerbaijan, managed both to widen the number of export destinations and to decrease their dependence on the most important ones. Azerbaijan added seven new export destinations and decreased its dependence on the top 5 export destinations by almost 10 percentage points over the period. While in 2002/3, the 5 most important markets for Azerbaijan accounted for 74 percent of total exports, they accounted for about 64 percent in 2010/11 (Figures 12a & 12b).

Figure 12a: Diversification Indicators for Resource Rich Economies.

Figure 12b: Diversification Indicators for Resource Poor Economies.

> 9 The methodology used to calculate the confidence intervals reported in Figure 12 is discussed in the previous section.

11