rafmacalaba/datause-encoder-data huggingface dataset:
Table 6: EFFECTS ON MATCHING IN TRANSFER NETWORKS, REPORTED BY NONULTRA-POOR HOUSEHOLDS

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_Transfers received by Non-Ultra-Poor from ..._|ultra<br>poor|other<br>poor|middle<br>class|upper<br>class|
|_Panel A1: Pooled_|||||
|Treatment|0.023***|0.012|0.001|-0.008|
||(0.004)|(0.008)|(0.013)|(0.007)|
|_Panel A2: By Wealth Class_|||||
|Treatment|0.039***|0.008|0.003|0.007|
||(0.007)|(0.012)|(0.012)|(0.009)|
|Treatment effect for middle class|0.025|0.013|0.005|-0.001|
||(0.005)|(0.008)|(0.015)|(0.007)|
|Treatment effect for upper class|0.012|0.003|-0.026|-0.040|
||(0.006)|(0.012)|(0.030)|(0.025)|
|Control mean|0.062|0.319|0.691|0.279|
|Mean for other poor|0.085|0.448|0.711|0.272|
|Mean for middle class|0.048|0.224|0.770|0.261|
|Mean for upper class|0.017|0.128|0.385|0.360|
|Observations|32594|32594|32594|32594|
|_Transfers given by Non-Ultra-Poor to ..._|ultra|other|middle|upper|
||poor|poor|class|class|
|_Panel B1: Pooled_|||||
|Treatment|0.025***|-0.011|-0.011|-0.002|
||(0.005)|(0.010)|(0.020)|(0.007)|
|_Panel B2: By Wealth Class_|||||
|Treatment|0.031***|-0.002|-0.012|-0.006|
||(0.007)|(0.016)|(0.019)|(0.009)|
|Treatment effect for middle class|0.027|-0.013|-0.019|-0.001|
||(0.006)|(0.011)|(0.022)|(0.008)|
|Treatment effect for upper class|0.017|-0.027|-0.000|0.006|
||(0.012)|(0.018)|(0.022)|(0.016)|
|Control mean|0.078|0.338|0.600|0.177|
|Mean for other poor|0.084|0.397|0.507|0.119|
|Mean for middle class|0.065|0.268|0.721|0.182|
|Mean for upper class|0.091|0.329|0.584|0.373|
|<br>Observations|32594|32594|32594|32594|

**Source:** Author’s analysis based on original survey data. **Notes:** The table reports ITT estimates based on specification (3) estimated with OLS. The sample includes observations from non-ultra-poor households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the BRAC branch office level (unit of randomization). *** (**) (*) indicates significance at the 1% (5%) (10%) level. In Panel A, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household received any transfers from any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In Panel B, the dependent variables in columns 1, 2, 3 and 4 are dummy variables equal to 1 if the respondent reported that her household gave any transfers to any ultra-poor, other poor, middle class or upper class households (within her community) respectively. In the pooled regressions (Panels A1 and B1) each observation is weighted using sampling weights calculated as the fraction of households surveyed from each wealth class (lower, middle and upper) relative to the number of households from the relevant wealth class in the community census.

41
22

# Table 5: Estimates of impacts of UC and VF on probability of being too ill to work

|||||Y|ears since i|mplementatio|n||
|---|---|---|---|---|---|---|---|---|
||||-3|-2|0|1|2|3|
|UC and VF|UC|coef|0.001|0.002|-0.001|-0.004**|-0.007**|-0.007*|
|effects for full||t|0.39|1.12|-0.60|-2.10|-2.32|-1.75|
|sample|VF|coef|-0.001|0.000|-0.000|-0.000|-0.001|-0.004*|
|||t|-0.44|0.39|-0.00|-0.03|-0.48|-1.89|
|UC effects by age|age (15-29)|coef|-0.000|-0.000*|-0.000|-0.001***|-0.001***|-0.000*|
|group||t|-0.91|-1.79|-0.74|-3.59|-2.98|-1.68|
||age (30-49)|coef|-0.000|-0.000|0.000**|0.000**|0.000**|0.000**|
|||t|-0.39|-0.47|2.55|2.43|2.25|2.07|
||age (50-64)|coef|0.019***|0.009**|0.001|-0.005|-0.012*|-0.012|
|||t|2.68|1.96|0.19|-1.01|-1.73|-1.19|
||age (65+)|coef|-0.004|0.016|-0.022|-0.045**|-0.056*|-0.058|
|||t|-0.16|1.16|-1.10|-2.02|-1.79|-1.47|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Results are from estimates of eqn (1) with the coefficients on UC and VF constrained as described in section III. The regressions include – in addition to the UC and VF dummies – province- and quarter-specific fixed effects, as well as the provincial minimum wage, educational attainment, age, the demographic mix of the respondent’s household, area of residence (rural versus urban). Standard errors are adjusted for clustering at the province level.
_Table 4 Firms Using Computer Regularly by Categories at Country Level (Percentage in terms of All_ _<u>Firms)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Size**||||||||
|Small|57.3|48.8|34.3|60.0|67.4|81.5|58.2|
|Medium|89.7|74.4|59.6|35.3|67.5|96.8|70.5|
|Large|98.6|94.1|68.9|89.6|100.0|100.0|91.9|
|**Age**||||||||
|age<5|68.6|51.7|16.9|58.4|70.5|91.8|59.6|
|age 5-9|69.2|46.8|33.4|53.7|64.1|98.3|60.9|
|age 10-14|67.3|51.6|50.3|73.0|79.6|95.2|69.5|
|age 15-19|71.3|57.6|38.6|73.0|88.3|81.2|68.3|
|age 20-24|70.7|64.1|53.7|53.3|63.6|90.6|66.0|
|age 25-29|78.7|78.9|39.3|51.5|67.7|74.0|65.0|
|age 30-34|62.4|88.7|64.0|32.6|72.5|90.1|68.4|
|age>34|60.7|75.6|41.5|38.7|38.0|81.7|56.0|
|**Trade**||||||||
|No Trader|63.5|51.4|39.9|50.8|67.5|86.5|59.9|
|Importer|93.7|66.6|68.6|100.0|96.6|99.1|87.4|
|Exporter|78.2|65.6|35.5|81.4|67.0|89.1|69.5|
|Two-way|90.8|100.0|55.7|100.0|100.0|89.3|89.3|
|**Foreign Status**||||||||
|Domestic|64.1|50.0|42.3|58.0|64.9|88.0|61.2|
|Foreign|97.0|72.5|26.2|42.8|84.9|88.7|68.7|

Source: Author’s own elaboration from enterprise surveys _On average, only 20% of African firms have developed or purchased any software recently._ Across countries, Kenya and DRC show the highest percentage of firms that have purchased or developed software (over 30%), while only 10% in Tanzania (see Table 5). On average, firms in the services sector tend to invest more in software than those from the manufacturing sector. As expected, the percentage of firms purchasing or developing software increases with firms’ size.

_Table 5 Firms which have developed/purchased any Software by Categories at Country Level (% All_ _<u>Firms)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|11.2|30.5|10.0|14.3|24.4|30.3|20.2|
|**Main Sectors**||||||||
|Manufacturing|9.4|23.5|8.2|12.9|22.8|31.3|18.1|
|Service|12.9|32.8|10.7|14.8|24.9|29.9|21.1|
|**Size**||||||||
|Small|7.0|28.2|7.9|14.0|22.1|22.2|17.1|
|Medium|17.9|37.8|16.6|14.2|24.2|39.7|25.4|
|Large|25.6|61.1|22.0|21.0|58.5|46.1|38.4|

Source: Author’s own elaboration from enterprise surveys 15
# CONTENTS

|Prefce<br>.............v|
|---|
|Acknowidgments .vii|
|Introduction.......................ix|
|Format of Tables .ix|
|Overview of Trends and Projecions .xiv<br>|
|World and Regi ns.xiv|
|Population Size and Growth .xiv|
|Fetlity<br>..<br>xix|
|Mortality ..<br>xxvi|
|Age Structurm ..<br>xxvii|
|Long-run Trends ..<br>xxvii|
|World Bank Regions and Income Groups . xxviii|
|Some Country Results ..<br>xxx<br> <br>|
|Conclusion ..<br>xxxvi|
|Appendix: Data and Methods .l.<br>xvi|
|Nature and Sources of Data .l.<br>xvi|
|Population and Age-Sex Structure ..<br>l xvi|
|Mortality Data .l.<br>xvi|
|Fertility Data ..<br>lxvii|
|Migration Data ..<br>lxvii|
|OtherD ata ..<br>lxvii|
|Projection Methodology ..<br>lxvii|
|Projecting Mortality ..<br>lxxxii|
|Future trends in life expectancy.<br>..<br>lxxxii|
|Future trends in infant mortality<br>.<br>lxxxiii|
|Selection of life tables .l.<br>xxxiv|
|Comparison with other projections .<br>lxxxiv|
|Projecting Fertility ...<br>lxxxiv|
|Projecting Migration ..<br>.<br>lxxxvi|
|References ..<br>.<br>lxxxviii|

Detailed Population Projections .......... 1

## World, Geographic Regions, and Icomme Groups 1

|World.|2|
|---|---|
|Less developed countries.|3|
|More developed countries.<br>Geographic Regions|4|
|Africa......................|_5_|
|East Africa**.**East.Afric|6|
|West Africa. .West.Afric|7|
|North Africa**.**North.Afric|8|
|America ...<br>........|9|
|Latin America and the Caribbean..|10|
|Northern America ..<br>.........|11|
|Asia.<br>......................|12|
|East and. Southeast Asiao ..........|13|
|South Asia ......<br>South.As<br>i<br>a|i4|
|Southwest Asia ...............|15|
|Europe ......................|16|
|Oceania .<br>....................|17|
|Income Groups<br>Low-income economies .....<br>.......|18|
|Lower-middle-income economis .19||
|.'<br>Upper-middle-income economis .20||
|Higb-income economies ...|21|
|Nonreporting nonmember economies....|22|

## Income Groups

i
Among EAP countries and those currently classified as upper-middle income, there was a combination of rapid increase in the share of working age population and improvements in education, based on years of schooling. For low-income countries, most of them in the SSA and SAR regions, the improvements in years of schooling can be seen to be still well below the average for other groups of countries. Table 4 shows the average income per capita ($ international dollars, 2005 PPP) across regions and income groups over the 1950-2010 period.

**Table 4 Average per capita GDP**

|**Region/Income groups**|**1950**|**1960**|**1970**|**1980**|**1990**|**2000**|**2010**|
|---|---|---|---|---|---|---|---|
|EAP|282|403|587|952|1,762|3,332|6,355|
|ECA|2,681|2,687|4,041|5,831|4,898|5,552|8,304|
|LAC|2,589|3,415|5,022|6,644|6,480|7,323|8,641|
|MENA|841|2,323|4,272|3,854|4,153|5,002|6,058|
|SAR|622|754|879|1,065|1,382|1,921|3,085|
|SSA|1,266|1,237|1,499|1,516|1,356|1,502|1,838|
|Low income|389|581|697|683|631|679|896|
|Lower-middle income|744|895|1,067|1,308|1,707|2,156|3,152|
|Upper-middle income|755|1,174|1,919|2,549|3,254|4,861|7,896|
|High income: OECD|8,800|1,545|16,453|19,963|25,202|31,424|33,567|
|High income: non- OECD|5,210|6,129|8,647|9,962|11,457|12,363|18,509|
|Total|3,136|3,610|4,856|5,542|6,580|7,955|9,654|

_Source:_ Penn Table (2015).

_Note:_ HIC refers to high-income countries; EAP refers to low and middle-income East Asia and the Pacific; ECA refers to low and middle-income Europe and Central Asia; LAC refers to low and middle-income Latin America and the Caribbean; MNA refers to low and middle-income Middle East and North Africa; SAR refers to low and middle-income South Asia; and SSA refers to low and middle-income Sub-Saharan Africa. Income classifications are based on the official World Bank Group classifications for FY16.

# **5. Results**

The results under different specifications, using five-year averages for the 1950-2010 period, suggest that an increase in the share of working age population has a positive effect on per capita GDP growth (Table 5). Three different methods are tested: first-difference, panel fixed-effects, and generalized method of moments (GMM). For each method different specifications - S1, S2, and S3 - are tried, differing according to the inclusion of specific covariates. S2 includes initial per capita GDP as a control variable to capture income convergence across countries, S3 includes initial per capita GDP, log of years of schooling, a set of geographical variables (latitude and a dummy identifying landlocked countries), and a set of institutional variables (dummy variables for 14
**_Table 1: Shadow economy in Europe: % of GDP, 1999-2007_**

||**1999-2001**|**2002-2004**|**2005-2007**|
|---|---|---|---|
|**Austria**|9.8|9.8|9.6|
|**Belgium**|22.3|21.9|21.5|
|**Bulgaria**|36.9|35.5|33.4|
|**Cyprus**|28.7|28.0|27.2|
|**Czech Republic**|19.1|18.6|17.4|
|**Denmark**|18.1|17.9|17.2|
|**Estonia**|32.6|31.5|29.9|
|**Finland**|18.1|17.7|17.2|
|**France**|15.3|15.0|14.8|
|**Germany**|16.1|16.2|15.6|
|**Greece**|28.5|27.5|26.6|
|**Hungary**|25.1|24.3|23.8|
|**Ireland**|16.0|15.9|15.5|
|**Italy**|27.2|26.9|26.9|
|**Latvia**|30.5|29.4|27.8|
|**Lithuania**|33.6|32.2|30.4|
|**Luxembourg**|9.9|9.8|9.6|
|**Malta**|27.3|27.5|26.9|
|**Netherlands**|13.2|13.2|13.1|
|**Norway**|19.1|18.8|18.2|
|**Poland**|27.7|27.5|26.4|
|**Portugal**|22.8|22.9|23.2|
|**Romania**|34.1|32.8|30.9|
|**Slovak Republic**|18.9|18.3|17.2|
|**Slovenia**|27.0|26.4|25.3|
|**Spain**|22.7|22.4|22.3|
|**Sweden**|19.3|18.7|18.2|
|**United Kingdom**|12.7|12.5|12.3|
|**NMS average **|28.3|27.7|26.4|
|**Old Europe average **|18.2|18.0|17.6|

_Source: Schneider et al. (2010), own calculations_

**_Figure 1: Shadow economy in Europe: % of GDP, comparison of old EU members and NMS averages, 1999-2007_**

<!-- Start of picture text --> 31.0<br>29.0<br>27.0<br>25.0<br>23.0<br>21.0<br>19.0<br>17.0<br>15.0<br>1999 2000 2001 2002 2003 2004 2005 2006 2007<br>NMS Old Europe<br><!-- End of picture text --> _Source: Schneider et al. (2010), own calculations_ Figure 2 sheds some light on development of informal economic sectors in particular countries, showing the difference in size of shadow economy between average of 1999-2001 7
Policy Research Working Paper 6768

# **Abstract**

Several studies have explored the relationship between economy-level crime rates or individual-level crime and economic growth. However, few studies have examined the relationship between economic growth and crime against firms. This study uses data for about 12,000 firms in 27 developing countries and finds that economic growth is negatively associated with crime. This relationship is stronger for small and medium firms than large firms. The study also explores several economywide factors and their influence on the growth-crime relationship for small and medium enterprises. The results are robust to various sensitivity checks.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at aislam@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 10 -

- <u>Table 1. Quasi fiscal deficits in electricity sector (% of GDP)</u>

||2000|2001|2002|2003|
|---|---|---|---|---|
|**Albania**|**10.5**|**7.4**|**6.1**|**4.2**|
|Armenia|1.4|2.2|1.0|1.0|
|Azerbaijan|11.4|10.1|8.1|6.4|
|Belarus|2.5|2.2|0.8|0.0|
|**Bosnia**|**5.4**|**5.1**|**3.9**|**1.4**|
|**Bulgaria**|**9.5**|**8.1**|**7.0**|**3.8**|
|**Croatia**|**2.1**|**2.1**|**1.8**|**0.9**|
|Georgia|12.2|6.9|6.5|6.0|
|Kazakhstan|3.3|2.9|2.4|1.3|
|Kyrgyz Rep.|18.6|25.2|19.0|9.2|
|**Macedonia**|**5.0**|**3.6**|**3.5**|**5.6**|
|Moldova|10.8|7.7|3.2|2.7|
|Poland|0.3|1.4|1.1|0.8|
|**Romania**|**3.8**|**3.7**|**2.5**|**1.3**|
|Russia|5.4|3.6|3.1|1.0|
|**Serbia & Montenegro**|**22.5**|**16.5**|**8.9**|**8.7**|
|Tajikistan|28.2|25.0|23.0|16.5|
|Turkey|1.8|2.1|1.1|0.6|
|Ukraine|9.1|6.8|5.6|4.0|
|Uzbekistan|8.6|10.2|13.1|12.1|

Source: World Bank (2006b).

Second, an indirect cost of underpricing is inefficient resource allocation in the economy. Underpricing must of necessity induce users to over-consume energy and act as a disincentive to improving energy efficiency because firms are likely to keep using old equipment and machinery, rather than investing in costly energy efficient technologies. Overconsumption would in turn deteriorate the financial viability problem, making it more difficult to maintain the quality of utility services.

# **III. AN OVERVIEW OF ENERGY SUPPLY AND DEMAND IN SOUTH EASTERN EUROPE**

# **_Supply_**

The supply capacity of electricity varies markedly across SEE countries, as partly documented by IEA (2008). In terms of installed capacity per capita, Albania has only onethird as much generation capacity as more advanced countries in Europe (Figure 3). Croatia and Macedonia are also potentially deficient in domestic electricity supply capacity. Apparently, these inadequacies can threaten domestic energy supply and trigger off massive load shedding, when an external energy shock occurs. The vulnerability may increase particularly when countries are largely dependent on hydrology for energy. In Albania, for instance, three hydropower plants account for over 90 percent of domestic electricity
resulting impacts on exports and poverty in Tanzania. We consider these impacts within the context of five historical case-study years in which we apply the historical productivity shocks under two different trade regimes to examine the sensitivity of the economic impacts of climate shocks to export restrictions. Finally, we use a suite of global climate model simulations to quantify the potential for changes in precipitation extremes in Tanzania and in key trading partners as greenhouse gas concentrations increase over the course of the 21<sup>st</sup> century.

# **2. TANZANIAN MAIZE TRADE IN THE CONTEXT OF INTERNATIONAL PRODUCTION VOLATILITY**

Tanzanian trade policy emphasizes integration into the regional and multilateral trading systems, as reflected in the National Trade Policy (Tanzania, 2003), which sets the goal of guiding the country from a supply-constrained economy to one with competitive, export-led growth. Export growth has been credited with an important role in raising the national growth rate from two percent per annum in 1990-95 to six percent in 2000-03 (Integrated Framework, 2005). The National Trade Policy, however, provides only a weak and superficial treatment of agriculture-enhancing policies, even though crop exports accounted for about 23% of total export value in 2001 (Dimaranan, 2006). There also appears to be little coordination of trade, agriculture, and poverty reduction strategies, despite the importance of sustaining export growth indicated in the National Strategy for Growth and Reduction of Poverty (Tanzania, 2005).

Focusing on maize, Tanzania’s trade policies have a history of rapid changes and great uncertainty, as reviewed by Chapoto and Jayne (2010). For example, the government lifted a longstanding ban on maize exports around the same time that the East African Community was established (1999). However, in 2003, the Ministry of Agriculture and Food Security imposed an export ban on maize by withdrawing export permits already issued to traders and suspending the issuance of new permits. In 2006, this ban was lifted for a month, and then re-imposed, before being lifted again in late 2010.

Table 1 reports data on maize production, imports and exports by year (USDA, 2011). These data show that both imports and exports tend to be very small as a share of domestic consumption and production. Exports are reported in nine of the 22 years considered in Table 1. By contrast, imports are recorded in 20 of the 22 years. Given that export bans have been in place for much of this period, it is hardly surprising that producers and marketing institutions have not pursued long run expansion of exports, assuming instead that production will be consumed domestically.

4
The Developing World is Poorer than we Thought, But no Less Successful in the Fight Against Poverty Shaohua Chen and Martin Ravallion<sup>1</sup> _Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_

> 1 **_This the August 2009 revised version of the working paper under the same title first issued in August 2008. In response to comments received, the revised version includes new sensitivity tests and a fully explanation of the differences with past estimates. The main results are unchanged._** A great many colleagues at the World Bank have helped us in obtaining the necessary data for this paper and answered our many questions. An important acknowledgement goes to the staff of over 100 governmental statistics offices who collected the primary household and price survey data. Our thanks go to and Prem Sangraula, Yan Bai and Xiaoyang Li for their invaluable help in setting up the data sets we have used here. The Bank’s Development Data Group has helped us with our many questions concerning the 2005 ICP and other data issues; we are particularly grateful to Yuri Dikhanov, Olivier Dupriez and Qinghua Zhao for their help. We have also benefited from the comments of Francois Bourguignon, Gaurav Datt, Angus Deaton, Massoud Karshenas, Aart Kraay, Peter Lanjouw, Rinku Murgai, Ana Revenga, Luis Serven, Merrell Tuck, Dominique van de Walle and Kavita Watsa. These are our views and should not be attributed to the World Bank or any affiliated organization. Addresses: <u>schen@worldbank.org</u> and <u>mravallion@worldbank.org.</u>
# **6.1 Overall impact on formalization**

As discussed in section 3.4, our main measure of formalization is registration of the business with the chamber of commerce at GUFE (i.e. the registration was found in GUFE data). We think that this definition of formalization is preferable over others that use follow-up survey data because administrative data included information on the whole study population, whereas survey data only have information on those who were surveyed. Moreover, survey data are subject to declaration bias. However, the correlation between survey data and administrative data was high (0.7), and we show similar results using the survey data as well.

Table 3 presents the results on formalization two years after the program started. The impact of the program on the formalization rate was 9.6 percentage points in group 1, 13 percentage points in group 2, and 16.3 percentage points in group 3. All these effects are statistically significant at one percent level. The effects in treatment groups 2 and 3 are higher than in treatment group 1 (although the test is only statistically significant for group 3), and the effect in treatment group 3 is significantly higher than in group 2: both sets of additional incentives included in package B (counseling, trainings and bank services) and in package C (tax mediation) seemed to be valued by informal businesses as incentives to register.

The formalization rate in the control group was only 2.3 percent. Therefore, in the absence of the program, only a few businesses would have formalized. Alternative measures of formalization that combine survey and administrative data show consistent results on businesses surveyed during the follow-up survey. Impact rates in groups 2 and 3 are always significantly higher than in group 1 (for group 2 the test is only significant for declared formalization), and impact rates in group 3 higher than in group 2.

Figure 1 presents trajectories of impacts in time with formalization rates by group in the months following the first visit received by the CGA.<sup>10</sup> It shows that most businesses that choose to formalize because of the program did it relatively quickly after the first visit. For all treatment groups, most of the impact arises during the first month following the first visit. Then for groups 2 and 3, some businesses took more time to formalize but we don’t see any significant additional impact five months after the first visit.

> 10 For the control group, the date of the first visit was set at the mode of the first visit date in the other groups (i.e. three months).

18
# **The association between remarriage and HIV infection: Evidence from national HIV surveys in Africa**

# **Damien de Walque**

The World Bank Development Research Group **Rachel Kline** The World Bank Development Research Group

# **Keywords**

Africa, Remarriage, HIV/AIDS, Widowed, Divorced, Marital Status

> * We thank Adam Wagstaff for useful discussions and suggestions. 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. Working papers describe research in progress by the author and are published to elicit comments and to further debate.