rafmacalaba/datause-encoder-data huggingface dataset:
INTroGucTION LXXIX

|Infantwortality<br>1905M90|**Age-sex**structuro,1985|NMetrecent<br>contraceptive<br>**prevatlnce**|Country,<br>eonxy,or<br>territory|
|---|---|---|---|
|Bank<br>projection|U.N. 19U revision|-|Surin_|
|U.N.1990<br>revision<br>UNPVSN<br>10/6U (official<br>est.)|**SBkest.**<br>U.N.<br>1968<br>revision|Pop.<br>Ref.Bureau<br>1990<br>U.N.<br>1968|Swaziland<br>Sweden|
|WIPYIR<br>10/P9<br>(official<br>oet.)|U.N.<br>19<br>revision|U.N. 1966|Switzerlnd|
|U.N. 19O revision<br>**Bad**on U-lOC<br>1967|U.N. 19 revision<br>Taiwan<br>Stt. Date<br>Book<br>1967|IfS<br>1976<br>Ross<br>et al. 19|Syrian<br>Arab<br>Rep.<br>Taiwan,<br>Chin.|
|**nkoot.**|**Bnkest.**|Roes<br>at**el.**1966|Tanzania|
|_Based_onOD$1967|U.N. 1966 revision|DNS1967|Thailand|
|U.N. 1990 revieon|Onk**est.**|DNA19U6|Togo<br>|
||||Tok*lau lslands|
|USUOC191|Bnkest.|-|Toa|
|U.N. 1990 revision|U.N. 1966 revision|DSN 1967|Trinided**and**<br>Tobogo|
|_Based_on DM 1906<br>|U.N. 1968 revision<br>|**DhS196**<br> <br>|Tunisia<br>|
|Based on Popultion_w*_HeaLth<br>Survey 1966|U.N.1966 revision|Fertility<br>and Health Survey 1968|Turkey|
||||Turks and Caicos<br>IstIes<br>|
||||Tuvalu|
|U.N. 1990 revision<br>|Sutet.<br>|DhS1986-89|Uganda|
|U.N. 1990 revision|U.N. 1968 revision|-|United Arab<br>E irate|
|Eurostat<br>1969<br> <br>|Eurostat<br>1989|U.N. 1988|United Kingdom|
|UNPYSS<br>10/89(official<br>est.)|U.N. 1968 revision|Ross et *.1 19|United States<br>of<br>America|
|U.N. 1990 revision|U.N. 1968 revision|--|Uruguay|
|_sum_**andPressat**1987<br>|U.N. 196 revision|-|U.S.S.R.|
|**Based on**official<br>1960.64<br>***t.**|But* st.|Pop. Ref. Bureau 1990|Vanuatu|
|U.N.1990revision<br>|U.N. 1966revision|UFS1977|Venzue la|
|**Based on But**study<br>|U.N. 1966 revision|Ross et**al.**1966|VietNam|
|**ased on**UNPVSR 10/69|Based on 1980 census||Virgin lslands<br>(U.S.)|
||||Wake Island|
||||Wallis and<br>Futuna|
|**B*t**est.|Bunk est.||WestBatnk|
||||W.etern<br>Sahara|
# **Box 2: Snapshots of the Environmental Stressors in Pakistan’s KP/FATA Region**

<mark>Among other issues, the participants of the needs assessment in KP/FATA (World Bank 2018) described security issues as the primary, root cause of all their problems. Due to conflict in the region, people report being in a state of fear for their safety. These circumstances have led to decreased business activities. Since most of the businesses of SME entrepreneurs were either destroyed or faced a high drop in sales due to the security crisis, they attributed their financial</mark> difficulties to the ongoing security situation in KP/FATA.

<mark>Security issues have also resulted in unfavorable economic conditions for doing business, thereby impeding the chances of success in any business, including the ability to repay loans. Work‐</mark> related stressors spill over into their personal spaces, affecting the tranquility of family life.

In their words: _<mark>“Due to the fear (of bomb blasts/terrorism) people do not want to invest in the expansion of</mark> their businesses...if the situation worsens, all of their investments will be in loss". (IDI‐005)_ _<mark>“Stress disturbs your daily routine; diet, sleep pattern and you stay awake [the] whole night and</mark> go to [the] office in late hours, this disturbs the whole business cycle.”(IDI‐018)_ _<mark>“(In challenging times) one becomes agitated. When the (work) pressure increases (challenges and stakes are high), the person gives up saying that I cannot do business any more. He feels</mark> that he has an ill fate.”_ _<mark>“</mark>_ <mark>Y</mark> _<mark>ou cannot enjoy your work, you do not get required output. Your customers, your boss, and</mark> your staff remain unhappy.” (IDI‐004)_ _<mark>“Whenever I am reminded of the Army Public School incident, for an hour or two, I am unable to concentrate on work. My attention gets diverted from my main work. It affects my productivity</mark> and creativity. I am unable to do the innovation that I am trying to do.” (IDI‐003)]]_ _<mark>“Due to security issues, trainers from outside the city do not come to train people here.” (IDI‐</mark> 009)_ _<mark>“If someone has made an investment and the market rate of the product drops, we have to bear</mark> losses.” (IDI‐005)_ _<mark>“Due to constant stress, there are frequent issues with the family, such as issues with [my] wife,</mark> with parents and siblings, etc.” (IDI‐005)_ <mark>Source: World Bank Group Rapid Needs Assessment in KP/FATA (2018). Forthcoming.</mark> 18
and poverty reduction in the elaboration of the Cameroon’s IMMPA model, we emphasize the external sector in the real and financial components of the updated financial SAM.

**Figure 1: External Debt and Gross Domestic Investment 1980 – 2001**

<!-- Start of picture text --> 30 120<br>25 100<br>Gross dom. Inv. In % of GDP<br>20 ( left scale ) 80<br>15 60<br>10 40<br>5 External debt to GDP ratio ( %) 20<br>( right scale )<br>0 0<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br>Total external debt to GDP (%)<br>Gross domestic investment to GDP (%)<br><!-- End of picture text --> Source: World Economic Outlook (IMF [2002]).

The sequence of negative growth rates and falling investment reduced the government’s prospects for resource mobilization and contributed to accumulation of fiscal deficits between 1985 and 1995. Expressed as percentage of GDP, the scope of public deficit (including grants) increased from 1.6 to 13%, which is quite high (Beckerman [2000]). The Government responded to the growing deficit by adopting policies to reduce public expenditures, particularly the size of public service wage bill through a number of measures, including direct cuts in civil servants’ salaries, a hiring freeze and public sector downsizing, privatization of public enterprises, and cuts in public expenditures allocated to social services.<sup>11</sup> In the face of the hiring freeze in the public sector and falling aggregate investment, unemployment increased rapidly, contributing to the aggravation of poverty, especially in the absence of effective social safety net programs and the government’s inability to ensure the delivery of essential social services to the poor and vulnerable groups. Urban unemployment rose sharply to reach 8% in 1996. More recent statistics suggest that unemployment increased to about 17% in 2001, and in the two largest cities, Yaoundé and Douala, it increased to 31% and 24%, respectively (Cameroon [2002], Bamou [2002]).

> 11 Real public sector wages suffered a 70 percent cut in 1993; and nominal public sector wages remained frozen in most of the 1990s, despite the impact of the devaluation.

6
# _3.4. Improving the relative terms of access to the U.S. market for other exporters_

U.S. imposition of restrictions on imports from Japan and China sometimes benefits producers in other (unrestricted) exporting nations in addition to, or rather than, competing U.S. producers. This has been especially true for Chinese textiles and apparel, where other developing countries share China’s comparative advantage relative to the United States. In such cases, restrictions on Japan and China have improved the _relative_ terms of U.S. import market access available to other exporters. However, in many cases the United States has created a similar relative advantage for other exporters through a variety of preferential (discriminatory) trade arrangements. Most of these arrangements are permitted under GATT/WTO rules.

Trading partners that competed with Japan in the U.S. market and benefited from formal preferential trade agreements with the United States during this period include Israel (1985) and Canada (1987). With the growth of U.S. imports from China, the United States entered into preferential deals with Mexico (NAFTA, 1994), Central American countries and the Dominican Republic (CAFTA-DR, 2004), Bahrain (2006), and Morocco (2006). The United States also offered various groups of developing countries further extensions of major preferential programs. These included the Generalized System of Preferences; for Caribbean nations, the Caribbean Basin Initiative (1983, substantially expanded in 2000 through the U.S.-Caribbean Basin Trade Partnership Act), for Andean countries, the Andean Trade Preference Act (1992, expanded as the Andean Trade Promotion and Drug Eradication Act under the Trade Act of 2002); and for countries in sub-Saharan Africa, the African Growth and Opportunity Act (2000; revised in 2002, 2004, and 2006). While such special preferential arrangements may have been motivated primarily by U.S. foreign-policy considerations rather than as a means to restore the market position of established suppliers to the U.S. market, their result nonetheless is to improve the market access of firms in other countries relative to their rivals in China.<sup>31</sup>

# **4. Treating the symptoms (2): U.S. efforts to improve its exporters’ market access in Japan and China**

The second strategy a country facing a bilateral trade imbalance due to continued export expansion into its market can use to rebalance concessions is to expand its own exporters’ access to the other country’s market. The United States has pursued this approach against Japan, and to a lesser extent more recently against China, via a combination of formal trade disputes initiated under the multilateral auspices of the GATT (1955–1994) and WTO (1995 onward) dispute-settlement systems, as well as its unilateral Section 301 law (1974 onward).

> 31 The USTR website provides a detailed description of U.S. trade preferences for various groups of developing countries: http://www.ustr.gov/trade-topics/trade-development/preference-programs.

14
- 3 **.**

# **1.2. Background: The Literature on Investment in Developing Countries**

Since there are a number of good surveys of the investment literature available,' this section focuses on empirical models that are relevant to developing countries. The relatively few attempts to estimate investment functions for developing economies have tended to use fairly eclectic models that combine features of the flexible accelerator, neoclassical, and structuralist approaches. Few studies have attempted to apply "q" models, which use the ratio of the tnarket valuation of the existing capital stock to its replacement cost, to developing countries since stock market valuations of corporate fixed assets are often non-existent or else are not meaninigful.2 A number of these studies, particul iy the country-specific ones, have provided considerable insights into the factors that influence capital formation in developing countries. For example, Behrman's work on Chile explores the validity of putty-putty versus putty-clay assumptions across a number of d.:fferent economic sectors.<sup>3</sup> His results revealed that investment functions tended to differ across sectors, both in terms of the variables that were relevant and in 'See Serven and Solimano, 1989; Precious, 1987; Bruaker, 1985; Nickell, 1978; Helliwell, 1976; Rowley and Trivedi, 1975; and Meyer and Kuh, 1957. The accelerator and flexible accelerator models are described in Samuelson, 1939; Eisner, 1960; Meyer and Glauber, 1964; Brown, Solow, Ando and Karenken, 1963; and Eisner, 1967. The neoclassical model originates in the work of Jorgenson, 1963; Jorgenson, 1967; Jorgenson and Siebert, 1968; and Hall and Jorgenson, 1971. The "Q" theory of investment and the related adjustment costs literature are presented in Tobin, 1967; ToDin, 1969; Tobin and Brainard, 1977; Hayashi, 1982. Modern versions of Keynes' model based on the supply of capital goods can be found in Haavelmo, 1960; Witte, 1963; and Precious, 1987. The Kaleckian profits model of investment determination is described in Kalecki, 1971. An example of a structuralist model of investment behavior is provided in Taylor, 1987. Disequilibrium models of investment are described in Malinvaud, 1980; Malinvaud, 1982; and Sneessens, 1987. Investment models that focus on financial constraints can be found in Fazzari and Mott, 1984; Fazzari, Hubbard and Peterson, 1988a; and Fazzari, Hubbard and Peterson, 1988b.

> 2 For example, in many developing country stock market shares are not truly traded so that quoted prices do not reflect the market valuation and expectations of future profitability. Dailami uses "q" models to explain private investment in Brazil and Korea. Dai!ami, 1987, 1990. Solimano applies a 'q' model to Chile, but also adds a number of additional explanatory variables. Solimano, 1989.

3Behrman, 1972.
**Figure 1. Learning loss depending on the length of school closures**

<!-- Start of picture text --> shortest (p10) short (p25) average (p50) long (p75) longest (p90)<br>0<br>-2<br>-4 full school closures (UNESCO)<br>-6<br>-8 full or partial school closures<br>(UNESCO)<br>-10<br>PISA country average closures<br>-12<br>reported by students<br>-14<br>PISA country average closures<br>-16 reported by principals<br>-18<br>-20<br>Length of school closures in weeks<br>Learning loss in mathematics<br><!-- End of picture text -->

# **Learning loss by gender, immigrant background and achievement level**

The pandemic and school closures could affect students of different backgrounds differently. For example, differences in self-regulation, motivation, home and school support, gender, immigrant background, socioeconomic status, and achievement level could lead to differences in how students learned during the pandemic. We re-estimate the main models for the overall impact of the pandemic and for the effect of the length of school closures using weeks of full closures from UNESCO. To check if students of different background were differently affected, we first interact their characteristics the indicators capturing the departure from the linear time trend in 2022 and with the measures of school closures. To exclude potential differences in pre-pandemic trends, we also estimate separate trends by these characteristics in each country, in addition to controlling for individual and country-average effects of these indicators on achievement as in the previous regressions.

While boys and immigrant students experienced a lower learning loss compared to girls and natives, nevertheless, longer school closures had a more negative effect on both groups (see Table 3). Depending on the closure length measure, each week of school closures was associated with 0.17 or 0.15 points of additional decline in achievement for boys. At around 40 weeks of full school closures, the overall learning loss for boys is the same as for girls and 13
<!-- Start of picture text --> Bond Fund Flows by Region<br>(% of GDP}<br>1<br>EAP ECA ae LAC MINA ee SAR —+—SSA<br>os<br>— ad<br>a o eeeeic2 2 2 = 3 ASEeesfort ce wi22ee22= fm. a “me a88<br>8 a ee ee /, ee eee<br>2-05 S FS SESS EVRSRa S SSS SESBSEBSB RSa<br>-2<br>e eeeN Sr Guceteont ee Ge Am Ge a a a &<br>-L.5<br>Note: Regional aggregates of 33 GEM eligible countries;<br>excluding Kenya, Sri Lanka, Morocco<br>-2 Source: Emerging Portfolio Funds Research, aggregate of<br>bond funds flows by country<br><!-- End of picture text --> <!-- Start of picture text --> 350,000 Trading Volume of Local Instruments Q4 2009<br>300,000<br>250,000<br>8€ 200,000<br>5<br>150,000<br>100,000<br>50,000<br>, (<br>oe ¢ SF ESFEEESE PE ELSEEELE<br>Source:éfFPOEPEP; IP EPISoe EPS ESSé* ANG?x<br> EMTA Q4 2009 Debt Trading Volume Survey<br><!-- End of picture text --> <!-- Start of picture text --> 25,000 Trading Volume of Local Instruments Q4 2009<br>20,000<br>= 15,000<br>=<br>=<br>$10,000<br>5,000<br>: (_\<br>2 o> o @ @ iS 9<br>Nate: ExeludingPEPBe the tap 11EL cuntriesinGOSEELSthesurveyELES LIESBOOSIESN)ISG SE RIS<br>Source: ENTA Q4 2009 Debt Trading Volume Survey<br><!-- End of picture text -->
# **VII. Conclusion**

For effective poverty reduction policies to take place, we need to understand well poverty mobility over time. In the absence of panel data, it has historically been difficult to study poverty mobility in developing countries. However, there are by now at least two rounds of cross-sectional household survey data available in a large majority of developing countries. Our proposed method, which generalizes that initiated by Dang et al. (2014), offers a means to convert these cross-sectional survey data into synthetic panel data.

In particular, by moving away from the bound estimates in Dang et al. (2014) to point estimates, we show that our estimates are quite accurate and do not depend on additional information from ancillary data. Our method would thus seem applicable in most settings where two cross sections are available. We find that estimation results are good not only for the general population but for smaller population groups as well, and are associated with much tighter confidence intervals than even direct, panel-data based estimates in those settings where the sample sizes for the cross sections are large enough. Our estimates are validated against both simulated data and actual panel data spanning different income levels and geographical regions. In addition, our method can be readily extended to settings with three survey rounds or more, although predictions are more accurate for shorter periods.

It should be noted that our method needs not be restricted only to the analysis of poverty mobility. It can in principle also be employed to study dynamics more generally. Possible application can involve dynamics in the areas of labor (e.g., what is the percentage of those who move from the rural farm sector to the non-farm sector?), health (e.g., what is the percentage of those who have medical insurance in the first period but do not in the second period?) or finance (e.g., what is the percentage of borrowers who do not default on loans in the first period but do in the second period?).

36
<!-- Start of picture text --> 32000<br>6.3% -LS%e<br>_.. 28000 =<br>$ 2.7% 5<br>= 24000 on MB 2 =<br>= # rr =<br>oa ord 4 2<br>wo = = oa<br><!-- End of picture text --> <!-- Start of picture text --> =|oo — og<br>S<br>©<br>"3 16000 . om 2% 13%<br>12000 5 S 3 E<br>& of @ x<br>8000<br>1980 ———» 1990 2005 ——_> 2015<br>13.7% 21.3%<br><!-- End of picture text -->
or all) G20 regulatory reforms. Third, none of the survey respondents provided quantitative evidence to support their perception-based magnitude of the spillover effects.

The findings suggest mixed results from the responses of the three targeted groups (i.e. the supervisory agencies, local banks and global banks). There is considerable variation within countries – between the regulatory authorities and the banks, and across the local banks. There is also considerable variation across countries in the sample. These variations may reflect (at least in part) the different business activities of different banks (both within and across countries).

Most survey questions were presented as multiple choice with the option for respondents to provide additional commentary or examples. Interestingly, while local and global banks regularly exercised this option to support their views, regulators rarely did. Throughout the presentation of the key findings below we include anonymous statements of the respondents to illustrate the issues. The quotations are verbatim and, in a few cases, only minimally edited for clarity, brevity and confidentiality.

# **Overall impacts of the reforms**

# _<u>Regulators</u>_

Considering each of the reforms individually, the majority of the regulators generally believe that they do not generate any spillover impacts at all - positive or negative - on the financial and real sectors in their jurisdiction (Table 3). However, three regulators identified the riskbased capital reforms as having a moderate impact, of which two deemed these impacts to be positive and one negative. Three regulators also assess the measures dealing with systemically important banks as having an impact – one deemed them to be moderately positive, one moderately negative and one regulator did not know.

# **Table 3 – Regulatory respondents: Any spillovers of the individual reforms**

_Regarding the following G20 financial regulatory reforms, do you have a reasonable indication that there are any actual or expected positive or negative cross-border spillover impacts on your jurisdiction due to the adoption of these reforms in other jurisdictions (i.e. effects on financial intermediation, financial stability, and the real economy)?_ 19
For most of the analyses, the “combined” maps are used, which include both coastal and the fluvial/pluvial floods. For instance, the combined maps for the 25‐year return period flood (under current conditions, and low, medium, and high future conditions) are presented in Map 1. A Google Earth image of Ho Chi Minh City with the flood map for a 25‐year return period with high climate change is presented in Map 2.

_Map 1. A visual of what the combined hazard maps (which include coastal and fluvial/pluvial) look like. The map presented here is the worse‐case scenario we simulate, a 200‐year return period flood with high sea level rise._ _Map 2. Google Earth image of the flood maps overlaid with the built environment in Ho Chi Minh City, for the 25‐year return period under high climate change._ 6
<mark>WPS3567</mark>

# **Reducing Child Malnutrition in Tanzania Combined Effects of Income Growth and Program Interventions**

## **Harold Alderman**

The World Bank halderman@worldbank.org

**Hans Hoogeveen** The World Bank jhoogeveen@worldbank.org

## **Mariacristina Rossi**

The World Bank and University of Rome “Tor Vergata”, Italy rossi@economia.uniroma2.it

## World Bank Policy Research Working Paper 3567, April 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

### **__________________**

The authors thank (in alphabetical order): Awudu Abdulai, Kathleen Beegle, Deon Filmer, Philippe Krynen, Valerie Leach, Mildred McLachlan, Meera Shekar and participants to seminars at the World Bank and in Dar es Salaam for useful comments, suggestions and other types of assistance. Financial support by the Italian Trust Fund for Children and Youth is gratefully acknowledged.