rafmacalaba/datause-encoder-data huggingface dataset:
production. With a specific endowment distribution between the two countries, if the factors are used in a certain way, and if trade is possible, then factor prices can potentially be the same across countries. This is the case when the endowment point – the distribution of endowments between the two economies – is within the set of vectors describing how the factors might be used by the two countries. The prices prevailing would be the prices of the integrated world economy. If the factors are allocated in a way that puts them outside this factor use lens, then the returns on the factors in each country will be different. Fragmentation allows the factor use lens to adjust so that one country can take advantage of lower cost factors in the other, with the factor returns adjusting accordingly.

The framework thus described by Deardorff (1994, 2001a) can now be used to characterize outsourcing as the trade in services. Going back to the H‐O model, the two factors could have been skilled workers and capital, with the two countries being India and the USA. Suppose one of the fragments in the H‐O model were skilled worker‐intensive services, then fragmentation would allow the service‐fragment to be performed in India where labor is relatively more abundant and where wages are relatively lower than in the USA. Fragmentation would therefore allow the USA to use the Indian labor until the wages in the two countries became the same. At that point, the countries would be indifferent between workers from either country. A Hewitt Associates study cited in the New York Times reported that between 2003 and 2005 average wages in the Indian IT/ITES sector grew by 15 percent, higher than any other IT sector in the Asia‐Pacific region (New York Times, 2004), and consistent with the theoretical prediction that wages in the service exporting country would rise as more outsourcing takes place.

Unfortunately, there are several challenges to obtaining clear solutions to the determination of how factor prices and factor uses vary as trade in services (i.e. outsourcing) increases. Deardorff (2001b) has shown that solutions from simple H‐O models of fragmentation do not hold when subject to generalization, since the impact of fragmentation on relative factor prices is sensitive to the factor intensities of the fragments as well as the original production technology. More importantly, when fragmentation fails to cause factor price equalization, there is no guarantee that the factor prices will move closer together, and may in fact move further apart<sup>7</sup> .

> 7 Kohler’s (2001) thorough review of the literature emphasizes the weakness of the H‐O model in determining factor price effects when considering many goods, many factors, or many countries.

7
and the likelihood of teachers being in class teaching (Table 7). This behavior is consistent with teachers in treated schools receiving textbooks. Because the number of teachers in a school is much lower than the number of students, distributing books to teachers will still leave enough books in storage for future “smoothing.” Distributing a few books to teachers but not to students may well be rational behavior on the part of head teachers. Teachers have more organizing and bargaining power within schools, which increases their likelihood of gaining access to “rationed” resources. Also, head-teachers might believe that within a smoothing framework, distributing books to teachers is the most effective way to maximize current learning outcomes of students because a teacher is likely to use information from the textbooks to impact student learning.

# **10. Expectations of schools with regard to government transfers**

Why should expectations of school administrators regarding current and future transfers of textbooks from government be low? One of the challenges that societies face as they move from conflict and fragility towards development is that trust in the state has to be re-built. Lant Pritchett (2009) argues: “In seeking to build legitimacy the state will be under pressure to create confidence in the state, in part by delivering on identifiable ‘quick wins’ but also at the same time to build and transform institutions that are capable of delivering on development.” While a government transfer program for textbooks can be seen as a “quick win,” the system that is expected to deliver these transfers to schools is still far from perfect. There are inefficiencies and uncertainties in the functioning of government systems in Sierra Leone as evidenced by the fact that in 2009, 17 percent of the head teachers and 36 percent of classroom teachers reported not receiving their full pay in the last year.

In addition, relating specifically to the textbook provision program, process data from the impact evaluation reveals that none of the DEOs or Local Councils had a completely clear picture of who was responsible for book pick-up and distribution, official signatories, or monitoring of textbook delivery. Likewise with regard to the actual disbursement of books, knowledge was spotty and written records were rarely found.

Furthermore, only in 20 percent of the cases (out of 325) were textbooks ever delivered directly to the school (in other cases, presumably, textbooks are delivered to a central location in the district, like the DEO office) and in as many as 25 percent of the cases the 18
of government expenditure, it does provide a snapshot of the main concerns among the residents of Europe.

We stack the surveys for all years and countries and estimate the following specification of equation (1): In addition to the variables of equation (1), we control for cohort of birth and survey year. We consider twelve 5‐year‐of‐birth groups, starting with those born between 1925 and 1929, and moving on to those born between 1980 and 1984. There is a large literature regarding the identification of age, cohort and time effects (see Schulhofer‐Wohl, 2013; and McKenzie, 2006). A well‐known challenge in this literature is that such effects cannot be identified without making specific assumptions, since they are perfectly collinear. We follow Deaton and Paxson (1994) and implement the normalization of time effects by assuming that trends are only captured by cohort effects, so that the time effects are orthogonal to a time trend.

Table 6 displays the estimates of equation (2) without controlling for cohort effects, and Table 7 displays the estimates including cohort dummy variables. To facilitate the description of the results, Figure 5 shows the estimated coefficients of a specification that introduces age and cohorts in a linear form. The results are consistent with those reported in Table 6 and Table 7. The left panel shows that, consistent with the findings that emerged from using the cross‐sectional data of LITS II, older people are more likely to consider pensions and health care as policy priorities, while being less likely to consider education as a top priority. The results also suggest that fighting crime is another important concern for the older residents of Europe. Finally, inflation, taxation, housing and unemployment seem less of a concern among older individuals.

The right panel of Figure 5 shows that, once we control for year of birth, the estimates become less precise, possibly due to the strong correlation between age and cohort. However, the age patterns regarding preferences for education and pensions do not change and remain statistically significant; that is, individuals are more (less) likely to consider pensions (education) as a policy priority as they become older, and this is not driven by a cohort effect. Fighting crime also becomes a more important policy concern throughout the life cycle of Europeans. It is important to keep in mind that these estimates might be affected by an attenuation bias. However, since most countries in the sample are developed or 13
# **Are Portfolio Flows to Emerging Markets Complementary or Competitive?***

## by

## Sudarshan Gooptu Intemational Economics Department World Bank

### SUMMARY

Increasing portfolio investment flows to emerging markets in the last few years have led to fears about a sudden reversal of these flows and possible "portfolio switching" activities of foreign investors from one emerging market to another in the short run. In an endeavor to address this issue of sustainability of portfolio flows to emerging markets, this paper econometrically examines whether portfolio investment _flows_ (rather than equity _returns,_ that has been the subject of earlier studies) to one part of the developing world are significantly related to those going to another region. This has important implications for policy makers in developing countries who wish to attract portfolio investment from abroad through domestic policy reforms and, in doing so, are trying to ascertain whether these financial flows from abroad are coming from an increasing pool of investible resources from the industrialized world, or whether it comprises of primarily the same fimds (e.g. "Hot Money") chasing different high-yield emerging market securities as conditions in each market change over time. In other words, is there a sort of "adding up" constraint that applies to these flows, making them fiaction as substitutes or not? Or could there even be complementarities among these flows?

This study conducts an econometric analysis of new quarterly World Bank data on gross portfolio investment flows for a sample of eight emerging markets (namely, India, Indonesia, South Korea and Thailand in Asia, and Argentina, Brazil, Chile and Mexico in Latin America) for the period 1989Q1-l993Q2. Results indicate the existence of an inverse relationship between total portfolio flows to Asian emerging stock markets and those to Latin America. This negative relationship holds both for _debt portfolioflows_ (i.e. through bonds, certificates of deposits and commercial paper) and _equity portfolio flows_ (i.e. through closed-end country funds, depository receipts and direct purchases by foreigners in the emerging markets), examined separately. Therefore, not only has there
people is about 2.7 billion over this period; this trajectory implies no progress in reducing the number of poor over the coming decade; the rate of progress in reducing the poverty rate is only sufficient to compensate for population growth. The corresponding projections for the PG index are 14% in 2017 and 13% in 2022. (Again, these incorporate the nonlinearity in how the incomegap ratio evolves over time.) Note that the slower rate of progress against poverty by this new measure reflects in part the success against absolute poverty. Economic growth has generally come with a lower absolute poverty rate but it has also meant that many developing countries have moved into the region in which relative considerations become more important. And, as already noted, the new poverty index is naturally less responsive to economic growth, and puts a higher relative weight on inequality.

In setting more ambitious targets for social inclusion, we have to realize that success against extreme absolute poverty will undoubtedly swell the ranks of the relatively poor. Except in the poorest countries, it can be safely assumed that those who will escape absolute poverty under this trajectory will still be relatively poor by the above definition. And the inclusion line will rise with the overall growth. So success against absolute poverty will add to the challenge of fighting relative poverty.

Consider again the World Bank’s current growth projections at country level, and assume again that the mean consumption of all households grows in line with the overall growth rates at country level and there was no increase in inequality. That scenario would get the relative poverty rate down to 41% in 2017.

What poverty target for 2022 for this new measure is consistent with the absolute targets above? One way to address this question is to use the poverty measures across regions and over time in Chen and Ravallion (2012) to estimate the mean relative poverty index when the absolute measure reaches its target holding inequality constant. This gave a mean relative poverty rate of 33% when the absolute rate is 3%, and a combined poverty rate of 42% when the absolute rate is 10%.<sup>36</sup> (The corresponding values for the total PG index are 11% and 13%.) These estimates

> 36 This calculation used a regression of the log relative poverty rate on a cubic function of the log absolute rate and a cubic function of inequality, measured by the population-weighted mean log deviation for the region and year. The predicted relative poverty rate was then calculated for each absolute poverty target, fixing inequality at its overall mean for the developing world in 2008. The data were for the six regions over 30 years (at three-yearly intervals; n=60) in Chen and Ravallion (2012).

17
**Table 8.** Probit Models of Reported Forest-Related Collective Action Behaviors with Household <u>Controls.</u>

||MEETING_DUM|BIOGAS_DUM|ANYTREE_CF|
|---|---|---|---|
|CONTRIBUTION1|-0.002|0.006|0.010|
||(0.72)|(1.85)*|(2.02)**|
|CF_MEM|0.838|-0.379|-0.106|
||(1.75)*|(1.08)|(0.27)|
|CF__OFFICER|0.823|0.194|0.350|
||(2.78)***|(0.82)|(1.18)<br>|
|MONITOR_PAY|0.345|0.270|-<sup>14</sup>|
||(1.29)|(0.56)|-|
|HILL|-0.266|0.314|0.296|
||(0.84)|(0.91)|(0.62)|
|AGE|0.001|-0.004|-0.007|
||(0.16)|(0.35)|(0.42)|
|FEMALE|0.331|-0.139|0.021|
||(1.95)*|(0.58)|(0.09)|
|DALIT|-0.259|-0.129|-0.324|
||(0.88)|(0.40)|(0.88)|
|FAMILY|0.050|0.058|0.052|
||(1.36)|(1.32)|(1.63)|
|MAXEDUC|-0.044|0.117|0.092|
||(1.40)|(2.42)**|(2.31)**|
|EDUC_RESP|0.018|0.007|0.019|
||(0.55)|(0.20)|(0.61)|
|LAND_HA|-0.159|0.118|-0.188|
||(1.39)|(0.79)|(0.75)|
|FOREST_ROP|-0.009|0.007|-0.006|
||(0.17)|(0.19)|(0.11)|
|OXEN|0.025|-0.070|-0.105|
||(0.27)|(0.90)|(0.88)|
|BUFFALO|-0.047|0.137|-0.015|
||(0.72)|(2.01)**|(0.17)|
|CONSTANT|-1.108|-2.851|-2.905|
||(1.30)|(3.43)***|(3.39)***|
|WaldΧ<sup>2</sup>|68.56|124.62|52.47|
|Prob> Χ<sup>2</sup><br>|0.000|0.000|0.000|
|PseudoR<sup>2</sup>|0.126|0.187|0.121|

N=327 in all models; * _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01; robust t-statistics and errors clustered at the forest user group level

> 14 Due to perfect collinearity with ANYTREE_CF the variable MONITOR_PAY was dropped.

35
UNHS (UBOS 2006). Results are presented for the poverty headcount ratio and the poverty gap in section 5.

# **4. Net Benefit Ratios by Region and Rural/Urban**

Who are the net buyers and net sellers of major food staples in Uganda? Figures 3-7 present the results of this analysis. Each graph shows how the NBR (on the vertical axis) varies with household consumption per adult equivalent (AE) on the horizontal axis. The vertical reference line at approximately 21,000 Ugandan shillings per month is the national poverty line: households to the left are below the poverty line and those to the right are above the poverty line. There is also a horizontal reference line at zero: households below zero are net buyers and those above zero are net sellers. The value on the vertical axis is the average value of net purchases or sales for that commodity, expressed as a proportion of household consumption. To facilitate comparison of results across regions and food items, the axis scales are identical for all of the nonparametric regression plots in Figures 3-7. The top graph in each figure shows results separately for rural households, urban households, and households in internally displaced persons (IDP) camps.<sup>2</sup> The next graphs show results by region, in urban and rural zones, respectively. Both disaggregations are relevant as net benefit ratios are significantly different for most crops by location and income For most crops, the poor are more vulnerable to food price increases, primarily because net food purchases are a larger share in total consumption. Maize (Figure 3) is the staple food for which purchases account for the highest share of total calories on average (Benson et al. 2008). Among rural households (dashed line in the top graph of Figure 3), the poorest spend an average of approximately 7% of their total budget, including autoconsumption, on net purchases of maize.3 Thus a doubling of maize prices would be equivalent to a real income loss of 7%. Poor urban households (dotted line) have a greater dependence on net purchases of maize, with average net maize purchases reaching as much as 12% of total consumption for the poorest urban households, and about 5% for urban residents living near the poverty line. The importance of net maize purchases in total

> 2 The UNHS 2005–06 sample includes 300 households residing in IDP camps. All of them are in the Northern Region, and 290 of them are in rural areas.

> 3 Note that the nonparametric regression lines represent the weighted mean of the NBR at different levels of total consumption, and that there is often considerable dispersion around this mean, especially at the tails of the regression lines.
- EIU (Economist Intelligence Unit). 2007. _Sub-Saharan Africa: Regional Overview_ . London: EIU.

- Eifert, Benn, Alan H. Gelb, and Vijaya Ramachandran. 2005. “Business Environment and Comparative Advantage in Africa: Evidence from the Investment Climate Data.” Working Paper 56, Center for Global Development, Washington, DC.

- Gelb, Alan, Ali A.G. Ali, Tesfaye Dinka, Ibrahim Elbadwi, Charles Saludo, and Gene Tidrick. 2000. _Can Africa Claim the 21st Century?_ Washington, DC: World Bank.

- Go, Delfin S., Vijdan Korman, John Page, and Xiao Ye. 2008. “Assessing the Macroeconomic Framework for Scaling Up Foreign Aid.” In _Africa at a Turning Point? Growth, Aid, and External Shocks,_ ed. Delfin S. Go and John Page. Africa Development Essays Series, The World Bank. (forthcoming).

- Hausmann, R., L. Pritchett, and D. Rodrik (2005), Growth Accelerations, _Journal of Economic Growth_ , 10, 303-329.

- Hnatkovska and Loayza 2004. “Volatility and Growth.” Policy Research Working Paper 3184, World Bank, Washington, DC.

- IMF (International Monetary Fund). 2007a. “Sub-Saharan Africa Regional Economic Outlook: Fall 2007.” SM/07/319, IMF, Washington, DC.

- ———. 2007b. _World Economic Outlook: Spillovers and Cycles in the Global Economy_ . Washington, DC: IMF _._

- ———. Various years. _World Economic Outlook_ . Washington, DC: IMF.

- Lofgren, Hans and Carolina Diaz-Bonilla. 2008. “Foreign Aid, Taxes, and Government Productivity: Alternative Scenarios for Ethiopia’s Millennium Development Goal Strategy.” In _Africa at a Turning Point? Growth, Aid, and External Shocks,_ ed. Delfin S. Go and John Page. Africa Development Essays Series, The World Bank. (forthcoming).

- Kaufmann, Daniel, Aart Kraay, and Pablo Zoido. 1999. “Governance Matters.” Policy Research Working Paper 2196, World Bank, Washington, DC.

- Ndulu, Benno J., Lopamudra Chakroborti, Lebohang Lijane, Vijaya Ramachandran, and Jerome Wolgin. 2007. _Challenges of Africa Growth: Opportunities, Constraints, and Strategic Directions_ . Washington, DC: World Bank.

53
2 _Estimates and PAijections of Mortality by Cause_

# Method

This section covers categories of causes of death; the nature and quality of the base data; the estimation and projection of mortality; the equations used to disaggregate mortality by major cause; and procedur; - for subdividing major causes into specific categories.

## _Grouping causes of death_

The categories for major causes of death shown in Table 1 are identical to those used by Hakulinen and others to permit the application of their equations. These categories require some explanation. Bronchitis, emphysema, and asthma are included among the infectious and parasitic diseases because of the important infectious bronchitis component, which was not separable from the others because of the way the Table 1. Classificatlon of causes of death and corresponding International Classificatlon of Diseases, Injuries, and Causes of Death (lCD) categories

|_Cau_|_si. of death category_|_ICD-8 (List A)_|_ICD-9 (Basic_<br>_tabulation list)_|
|---|---|---|---|
|_Maj_|_or CAUses_|||
|1|Infectious and parasitic diseases|A1-44, A99, A90-93|01-07, 320-323|
|2|Neoplasms|A45-61|08-17|
|3|<br>Circulatory system and certain degenerative<br>diseases|A80-88, A64, A98, A102,<br>A105-106|25-30, 181, 341,<br>347, 350|
|4|Complications of pregnancy|A112-118|<br>38-39, 41|
|5|<br>Perinatal conditions|A131-135|45|
|6|Injury and poisoning|AE138-150|E47-E56|
|7|<br>Other causes|Other codes|Other codes|
|_Spec_|_fc causes_|||
|<br>1.1|<br>Diarrhea|Al-5|01|
|1.2|Tuberculosis|A6-10|02|
|1.3|Acute respiratory infection|A15-17, A89-92|033-035, 320-322|
|<br>1.4|<br>Measles|<br>A25|<br>042|
|1.5|Chronic obstructive pulmonary disease (COPD)|A93|323-325|
|<br>1.6|<br>Polio|A22-23|040, 078|
|1.7|Yellow fever, dengue, and encephalitis|A26-27|<br>044-045|
|<br>1.8|<br>Malaria|A31|052|
|1.9|Schistosomiasis and filariasis|A39, A41|072, 074|
|1.10|Intestinal parasltes|A42-43|075076|
|<br>3.1|<br>Ischernc heart disease|A83|27|
|3.2|Cerebrovascular disease|A85|29|
|3.3|Other cardiovascular diseases|A80, A82, A84, A86-88|25, 26, 28, 30|
|3.4|Diabetes|<br>A64|<br>181|
|3.5|Certain degenerative diseases (nephrltis, cirrhosis of<br>the liver, ulcers of the stomach and duodenum)|A98, A102, A105, A106|341,347, 350|
|7.1|<br>Mental disorders|A69|210**-**212|
|7.2|Oral health diseases|A97|330|
|7.3|Mlcronutrient disorders|A62-63, A67|180, 193, 200|
|7.4|Malnutrition|A65|190-192|

Note: The specific diseases listed under infectious and parasitic diseases (1.1-1.10) and under other causes (7.1-7.4) are not meant to be comprehensive.
|**Table 1: Summary Statistics**<br>Observations<br>2,707,562|
|---|
|Share of men<br>47.8%|
|Average age<br>36.0|
|Share of married individuals<br>46.7%|
|Average years of education<br>9.6|
|_Size of locality (share of individuals)_|
|More than 100,000 inhabitants<br>60.4%|
|Between 15,000-99,999 inhabitan<br>11.7%|
|Between 2,500-14,999 inhabitant<br>12.0%|
|Less than 2,500 inhabitants<br>15.9%|
|Weekly hours<br>43.1|
|Hourly wage<br>32.5|
|_Labor force composition_|
|Employed<br>61.8%|
|Unemployed<br>2.7%|
|Out of labor force<br>35.5%|
|_Employed status_|
|Employee<br>69.8%|
|Employer<br>5.0%|
|Self-employed<br>20.0%|
|Unpaid worker<br>5.1%|
|_Type of displacement_|
|Plant Closing<br>8.4%|
|Being Fired<br>5.5%|
|Quitting<br>25.2%|
|Closed Own Business<br>2.7%|
|Retired<br>2.6%|
|Other<br>2.2%|
|Non displaced workers<br>53.3%|
|_Measures of informality status_|
|Formal Employment<br>47.5%|
|With access to health<br>41.5%|
|With a secondary job<br>4.2%|

Source: Own calculations based on ENOE 2005-2019 There are marked differences in current labor market outcomes between individuals who experience a plant closing and those who experience other types of job separation. We construct a transition matrix that relates the reason for job separation with the individual’s current labor market status. Through the matrix, we can identify both voluntary and involuntary separation. Involuntary separations are captured in the top two rows of table 2 —the “plant closing” and “fired” categories. The bottom two rows of table 2 captured voluntary separations—the “quit” and “closed own 8