rafmacalaba/datause-encoder-data huggingface dataset:
Table 7 Impacts on Psychological Well-Being and Education

||Control<br>Mean (SD)<br>(1)|Level (SE)<br>(2)|p-value<br>(3)|% Control<br>Mean<br>(4)|N<br>(5)|
|---|---|---|---|---|---|
|**PW Psychological Well-being In-**|0_._000|0_._220***|[0_._010]||645|
|**dex (3 items)**|(1_._000)|(0_._085)||||
|PW is Very Happy or Quite Happy|0_._776<br>(0_._330)|0_._042<br>(0_._028)|[0_._130]|5%|645|
|PW Life Satisfaction Rating (1-10)|6_._436<br>(2_._865)|0_._353<br>(0_._238)|[0_._137]|5%|637|
|PW is Depressed (7-CESD _≥_8)|0_._869<br>(0_._338)|_−_0_._063*<br>(0_._033)|[0_._061]|_−_7%|616|
|**School**<br>**Enrollment**<br>**(Children**|0_._562|0_._056*|[0_._061]|10%|2_,_094|
|**Ages 6 to 18 Years)**|(0_._496)|(0_._030)||||

_Notes._ This table reports Ordinary Least Squares estimates of treatment effects. All regressions include 133 randomization PRA controls. Outcomes are listed on the left and primary outcomes are described in detail in Online Appendix Text 1. (***) (**) (*) denotes significance at (1%) (5%) (10%) level. Column (1) reports the mean and the standard deviation (in parentheses) of each outcome for the control group. Column (2) reports the treatment estimates in levels with robust standard errors (in parentheses). Column (3) reports the naive p-values [in brackets]. Column (4) reports the treatment estimates as a percentage of the control mean, except for indices. Column (5) reports the total sample size. Psychological well-being measures include questions on life satisfaction and happiness from the WVS (averages of two data collection rounds); and scores on the CES-D seven-point scale (negatively coded). Indicators were collected across different data collection rounds and, due to attrition between rounds, the sample for psychological well-being outcomes is reduced to households surveyed in all rounds in order to have a complete set of indicators. Each psychological well-being measure is estimated as a score on the variable’s scale, normalized to the unit SD of control HHs, with mean equal to zero. Impacts on the normalized measures can be found in Table A9 of the Online Appendix. The psychological well-being index is an index of the three previous measures, normalized in the same way. School enrolment is estimated for school-age children (ages 6 to 18 years), with the unit of observation being the child, hence all SEs are clustered at the household level. PW = primary woman; SE = standard error; SD = standard deviation; PRA = participatory rural appraisal; WVS = World Values Survey; CES-D = Center for Epidemiological Studies Depression.

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WPS4139 Adult Mortality and Children’s Transition into Marriage<sup>1</sup> Kathleen Beegle kbeegle@worldbank.org Sofya Krutikova <u>sofya.krutikova@economics.ox.ac.uk</u>

## **Abstract**

Adult mortality due to HIV/AIDS and other diseases is posited to affect children through a number of pathways. On top of health and education outcomes, adult mortality can have significant effects on children by influencing demographic outcomes including the timing of marriage. This paper examines marriage outcomes for a sample of children interviewed in Tanzania in the early 1990s and re-interviewed in 2004. We find that while girls who became paternal orphans married at significantly younger ages, orphanhood had little effect on boys. On the other hand, non-parental deaths in the household affect the timing of marriage for boys.

# World Bank Policy Research Working Paper 4139, February 2007

_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._

> 1The views expressed here do not necessarily reflect those of the World Bank or its member countries. We thank Stefan Dercon for valuable feedback and Jere Behrman and seminar participants 2006 Malawi Diffusion and Ideational Change Project Conference “Panel research on HIV/AIDS and other sexually transmitted infections in sub-Saharan Africa” for very useful comments. Please address correspondence to <u>kbeegle@worldbank.org.</u>
## WPS3607

# **Agricultural Trade Reform and the Doha Development Agenda**

### **Kym Anderson and Will Martin**

World Bank 1818 H Street NW Washington DC 20433 <u>kanderson@worldbank.org wmartin1@worldbank.org</u>

#### World Bank Policy Research Working Paper 3607, May 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._ This paper is based on a program of World Bank research on the implications of the Doha Agenda for developing countries. The authors are grateful for the collaboration of all their cocontributors to that project, especially Dominique van der Mensbrugghe and Tom Hertel, and for funding from the UK’s Department for International Development. Forthcoming in the Global Trade Policy 2005 issue of _The World Economy_ , Vol. 28(9), September 2005.
# **_5.2 Prenatal environment: Measures of maternal health, nutrition and care_**

Given extensive evidence about the importance of the prenatal environment for subsequent child nutrition and health outcomes, a set of 10 indicators that are proxies for the likely nutritional status of the mother during pregnancy as well as the health care services that were available to - and used by - mothers during their pregnancy were identified in the NNS. Of these, five are considered necessary evidence for an adequate prenatal environment for the child: normal birthweight (greater than or equal to 2.5kg),<sup>44</sup> maternal height above 145 cm, mother’s BMI > 18.5 and at least four prenatal visits and birth assistance during delivery. Three additional ideal measures in this domain that were not available in the data include whether the child was born preterm, whether the child was born small for gestational age and whether the mother had anemia while she was pregnant.

**Table 3**

|Category|Dataset|Driver|Definition|Available<br>in NNS<br>dataset?|Required<br>for<br>adequacy<br>in<br>domain?|
|---|---|---|---|---|---|
||NNS Form 1.6|**Low birth weight**|**Child weighted less than**<br>**2.5kg at birth**|**Yes**|**Yes**|
|Birth<br>outcomes|Not in questionnaire|Preterm delivery|Child is born before 37 weeks<br>of completed gestation|No|No|
||Not in questionnaire|Small for gestational age|Child is as smaller than the<br>10th centile for fetal growth.|No|No|
|||**Short maternal stature**|**Mother’s height is less than**<br>**145 cm**|**Yes**|**Yes**|
|Maternal<br>nutrition|NNS Form 2.1|**Mother has low BMI**<br>**(underweight)**|**Mother’s BMI is less than**<br>**18.5**|**Yes**|**Yes**|
||Not in questionnaire|Maternal anemia|NA|No|No|
|||**Use of prenatal services**|**At least four prenatal visits**|**Yes**|**Yes**|
|||Neonatal tetanus|Received tetanus toxoid<br>during prenatal visit|Yes|No|
|Utilization of<br>antenatal<br>l|NNS Form 3.1|Prenatal nutrition<br>counseling|Received nutrition counseling<br>during prenatal visit|Yes|No|
|materna<br>health care||Micronutrient supplements<br>taken during pregnancy|Took iron or other vitamin<br>supplements during pregnancy|Yes|No|
||NNS Form 3.1/NNS<br>Form 4.1|Birth assisted by a skilled<br>health care professional|Birth assisted by skilled<br>practitioner (Doctor, nurse or<br>midwife)|Yes|Yes|

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the host market. On the other hand, if the cost structure is too high or important institutions, such as credit registries, are not in place, foreign banks might be reluctant to go down-market. Theory has also provided conflicting predictions on the relationship between banking market concentration, competitiveness and access to credit for “small” and opaque borrowers, so that it is a-priori not clear whether we expect a positive or negative relationship.<sup>17</sup> To measure banking sector development and structure, we use **Bank Credit to GDP** , the market share of **government-owned banks** , the market share of **foreign-owned banks** and the market share of the **largest five banks** to proxy for the market structure of banking systems, using data from Barth, Caprio and Levine (2006).

# **Bank regulatory policies**

Regulatory policies might impact the business environment in which banks work and thus the ease with which they can overcome transaction costs and risks related to lending to households. We therefore include several indicators capturing different dimensions of the regulatory framework. We use an indicator of **activity restrictions on banks** that indicates to which extent banks cannot extend their business into insurance and capital market activity and non-financial sector subsidiaries. Further, **entry into banking** indicates restrictions on the licensing of new banks. Both indicators are from Barth, Caprio and Levine (2006). Finally, Heritage Foundation’s **banking freedom** indicator is an overall measure of the extent to which government does not interfere into banks’ business by limiting foreign bank entry, restricting banks’ activities and providing deposit insurance. To the extent that household lending depends on innovation and thus a competitive and contestable banking market, we would expect a higher share of household credit in economies with fewer restrictions. Finally, we consider the relationship between the **deductibility of mortgage interest** from income taxation, conjecturing that this will foster the demand for consumer, especially mortgage credit.

> 17 While theory and some empirical work suggest that market power might entice banks to invest in long-term relationships with small and opaque enterprises as they know that they can regain the initial investment in the relationship at a later stage (Petersen and Rajan, 1995; Bonaccorsi di Patti and Dell’Ariccia, 2004), other empirical papers point to the healthy effect of competition on availability of lending to SMEs (Cetorelli and Strahan, 2004; Beck, Demirguc-Kunt and Maksimovic, 2004). See Berger et al. (2004) for an overview.

15
- 10 -

# II. THE INTERNATIONAL OCEAN TRANSPORT INDUSTRY AT PRESENT

4. Towards the end of 1992 the world merchant fleet had a total carrying capacity of about 657 million deadweigh: tons (dwt), and almost 69 million dwt were on order with shipyards in 40 different countries (see Table I below). During 1992 an estimated total of 4.1 billion tons of seaborne cargo was carried by the world's merchant fleet which produced about 17.5 trillion ton-miles in the process. About 40 percent of the ocean transport activities were in the oil trades, including crude and petroleum products; another 40 percent was serving the key dry bulk trades, including iron ore, coal, and grain, as well as the minor bulks, such as agricultural and forestry products. Only one-fifth of the registered tonnage was e-a ged in the general cargo trades, which also comprises the sea carriage of containers.

Table 1: THE WORLD MERCHANT FLEET AND NEWBUILDINGS ON ORDER Status as of end 1992

|Category<br>|Orde<br>|r Book<br>|**|**<br>Existi|ng Fleet|
|---|---|---|---|---|
|l|dwt (mn)|# of units|dwt (mn)|# of units|
|General Cargo Ships<br>(incl. Reefer tonnage)|2,922|475|92,982|16.094|
|Container Vessels<br>(all kind)|4,918|175|29,994|1,281|
|Tankers<br>(Oil,Chenical, Gas,<br>and Product categories)|41,565|672|285,380|7,977|
|Dry Bulk Ships<br>(incl. Combination<br>|18,547|266|240,161|5,027|
|Carriers )|||||
|RoRo Vessels|0,720|87|8,146|1,024|
|_WORLD TOTAL_|_68,672_|_1,675_|_65,663_|_31,403_|

Source: Fairplay. _'Newbuilding Supplement"._ November 1992. London Institute of Shipping Economics and Logistics. _'Statstical Tables'._ Various issues. Bremen _5._ The consequence of this market structure with its preponderance of liquid and dry bulk cargoes was that the majority of vessels deployed during 1992 was operating either on a charter or tramp basis, which is common practice in the bulk trades. In the general cargo trades the conference-based liner segment was much reduced in comparison with previous years. This phenomenon confirms the earlier recognizable trend toward _independent action_ which has been increasingly adopted by different carriers, particularly in the container sector. Under this type of arrangement carriers negotiate (freight) service contracts directly with shippers. By doing so, they by-pass existing conference rules for specific trade routes. Such service contracts often span longer periods, i.e. they are usually not limited to one shipment, and are widely considered to be more advantageous to shipper and carrier alike than conference regulated ocean transport.
essentially the same cells.<sup>11</sup> The proportion of fully built-up cells ( _yit_ = 1) among built-up cells increased a lot between 1975 and 1990 – from 2% to 6.6%, but does not show any trend after that. We revisit this point more in detail below. We check whether Zipf’s Law applies to built-up area at this micro level. To do this, we sort partially built-up cells by the proportion of cell area that is built up – from largest to lowest. We then plot the log of the rank to the log of the built-up area. Here cells with a built-up proportion close to 1 represent ’cities’ while those with a built-up proportion close to 0 represent the smallest possible town. Results are shown in Appendix Figures A1 (in levels) and A2 (in logs). We find absolutely no evidence of a Zipf’s Law similar to what has been discussed for cities (e.g., Gabaix 1999, Ashrad et al. 2018).

In Maps 1a to 1d, we illustrate what the data looks like for a randomly chosen location in Ghana – the rural town of Yeji. Buildup is color coded, with white meaning no evidence of any building in the cell and black meaning high density buildup – i.e., 90% and above. Intermediate shades of blue and grey correspond to different levels of partial buildup. We see that in 1975 there is very little evidence of buildup – most of Map 1a is empty. But the small settlement of Yeji can be seen in the upper middle of the map. Over time, this initial settlement grows and expands geographically. It also spawns small satellite settlements at more or less random but fairly equally spaced intervals. New settlements also appear farther away from the initial settlement, often in the proximity of a road. But the initial settlement remains the most densely built-up area of the map throughout the 40 years period covered by the data, suggesting some form of preferential attachment/first-mover advantage. Maps 1e and 1f show the same general area as a Google Earth photograph and Google map print, respectively. It is immediately clear that the data contain a wealth of detail about buildup that is hard to see at this level of magnification, but it comes to view under magnification. The picture also reveals the presence of the Volta lake nearby. Some of the roads shown in Map1f correspond to 1975 roads. However, one road running to the west from Yeji seems no longer in use, even though its outline is still visible from the air. Similar map sequences for other parts of Ghana show a similar evolution, at least visually (Maps 2a to f show the evolution of buildup in the coastal town of Takoradi). The rest of the paper is devoted to analyzing these patterns in a formal way.

> 11A small difference arises due to the fact that _yitn_ 1<sup>and</sup><sup>_y_</sup> _it_<sup>_n_2arenormalizedbythesizeoftheneighborhood.This</sup> gives a bit more weight to small neighborhoods in the averages of _yit_<sup>_n_1and</sup><sup>_y_</sup> _it_<sup>_n_2relativeto</sup><sup>_yit_.</sup>

12
**<u>Figure 2.2: Growth rate of the Quality of Life, 1991-2000</u>**

<!-- Start of picture text --> 40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>Source: Calculations based on IPLANCE data.<br>36.9%<br>35.2%<br>33.7%<br>31.3%<br>30.1%<br>29.2%29.0%<br>27.7%27.6%<br>27.0%<br>21.5%<br>19.6%<br>17.4%<br>16.8%16.3%16.3%<br>15.0%14.7%14.7%14.6%<br>13.5%13.3%13.1%13.0%12.5%12.4%<br>10.6%<br>9.1%<br>8.4%<br>7.7%<br>6.9%<br>6.0%<br>4.7%<br>Alagoas NordesteTocantins PernambucoBahia RG Norte SergipeRoraimaMinas Gerais Mato GrossoBrasil Norte Santa CatarinaSul AcreC Oeste RG do SulMS AmazonasRio de Janeiro SudesteDistrito Federal<br><!-- End of picture text -->

# **_Poverty Trends_**

In the last two decades Paraíba has made creditable headway in reducing poverty. During 1981-91, the state’s extreme poverty, measured by the P0, fell 19 percentage points. There is little room for complacency, however, because extreme poverty is still very high at almost 40 percent.<sup>4</sup> This means that over a 1.3 million people still live in extreme poverty, which means that they do not have sufficient income to buy a minimum basket of food. This is almost twice the poverty rate for Brazil as a whole (22.4 percent). Appendix A shows P0, P1, and P2 for Brazil, the Northeast, and the state of Paraíba.

Poverty did not fall monotonically over the last two decades. The P0 increased from the beginning of the 1980s, fell briefly after implementation of the inflation-beating Cruzado Plan of 1986, increased when the plan collapsed, and increased even more during the crisis of 1990. The P0 embarked on a downward path only after the most recent inflationbeating Real Plan of 1994 was implemented and the minimum wage increased. There was a sharp a decline in poverty to a level lower than at any point during the previous decade. After the Real Plan was implemented, the P0 fell 7 and 12 percentage points in

> 3 The data applied for the analysis are both money-metric and non-money-metric welfare indicators constructed from available data. The data applied are from three sources: (a) the _Desenvolvimento Humano no Brasil_ (DHB) (1998), a coproduction of IPEA, IBGE, FJP, and PNUD constructed from the census figures for 1970, 1980, and 1991; and (b) _Pesquisa Nacional por Amostra de Domicílios_ (PNAD) for the years 1981, 1985, 1988, 1990, 1993, 1995, 1997, 1998, and 1999, (no PNAD dataset for the Census year 2000 exists); and (c) Census 2000.

6
Cardres. Research based on these national surveys has been published within China (Tao and Jian, 1993; Wang, 1993; China Statistical Bureau, 1993; and Yie, 1997). These surveys provide a rich data source for analyzing and assessing Chinese women's social and economic status as well as for identifying key determinants of women's well-being.

But these surveys are not specially designed for the purposes of program and policy evaluations. However, the 1996 household survey collected for the purpose of evaluating the socalled _Population Control and Community Development Project,_ which has recently been made publicly available, is unique in this respect. The program, initiated by the Central Population Planning Committee<sup>3</sup> , was implemented in villages across 34 county towns in rural China in 1993. In 1996, three years after program implementation, a household survey was conducted for 57 villages, with 38 treatment villages and 19 control villages. Detailed socio-economic information on women and households was collected in the survey, and the questionnaire was specifically designed to collect information on women's economic and social participation within villages.

The availability of such a survey provides us with an unique opportunity to formally evaluate the impact of gender-based public interventions in the Chinese context. To our knowledge, no rigorous program evaluation has been carried out using this data. Using this data, in this paper we attempt to evaluate the economic and social impact of the program on the rural communities. We address the following questions. First, can such a community program be transformed into human and social capital, which increases women's (or their family's) productivity, narrowly measured by their income-generating capacity? Second, does the program generate external benefits at the village level in the form of influencing the incomes of nonparticipants as well as participants? Thirdly, how important is the program's ability to increase the level of participation within villages for its ability to generate income impacts? Finally, are 5
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<mark>Sen, A. (1976). Poverty: an ordinal approach to measurement.</mark> _<mark>Econometrica: Journal of the Econometric Society</mark>_ <mark>, 219-231.</mark> Sen, A. (1985). “A sociological approach to the measurement of poverty: A reply to Professor Peter Townsend.” _Oxford Economic Papers_ 37 (4): 669–76.

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