rafmacalaba/datause-encoder-data huggingface dataset:
WPS4082

# **Adult Mortality and Consumption Growth in the Age of HIV/AIDS**<sup>*****</sup>

Kathleen Beegle The World Bank Joachim De Weerdt EDI, Tanzania Stefan Dercon Oxford University

# **Abstract**

This paper uses a 13-year panel of individuals in Tanzania to assess how adult mortality shocks affect both short and long-run consumption growth of surviving household members. Using unique data which tracks individuals from 1991 to 2004, we examine consumption growth, controlling for a set of initial community, household and individual characteristics; the effect is identified using the sample of households in 2004 which grew out of baseline households. We find robust evidence that an affected household will see consumption drop 7 percent within the first five years after the adult death. With high growth in the sample over this time period, this creates a 19 percentage point growth gap with the average household. There is some evidence of persistent effects of these shocks for up to 13 years, but these effects are imprecisely estimated and not significantly different from zero. The impact of female adult death is found to be particularly severe.

# World Bank Policy Research Working Paper 4082, December 2006

_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._

> * We thank John Strauss and the referees for their detailed comments, and Jere Behrman, T. Paul Schultz, and seminar participants at the IUSSP seminar on "HIV/AIDS & Poverty" and Population Association of American annual meetings 2006 for very useful comments. For more information, please contact Kathleen Beegle, 1818 H Street NW, Washington DC 20009 USA, 1-202-458-9894, kbeegle@worldbank.org

1
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42
(Figure 9, panel b) confirms the relationship between initial income inequality and subsequent growth.<sup>14</sup>

**Figure 9. Growth in GDP per capita vs Initial Inequality**

Countries that started with lower levels of inequality experienced higher rates of economic growth.

<!-- Start of picture text --> a. Inequality in 1980 vs GDP per capita Growth b. Inequality in 1970 vs GDP per capita Growth in<br>in the Next 30 Years the Next 50 years<br>7 8<br>Taiwan,<br>6<br>China Korea, 6<br>5<br>Rep.<br>4<br>4 Singapore Thailand<br>Malaysia<br>3 Hong Kong Chile 2<br>SAR, China<br>2 Colombia<br>Peru 0<br>1<br>Argentina MexicoBrazil<br>0 -2<br>0 20 40 60 80 20 30 40 50 60 70<br>Initial market Gini Initial market Gini<br>Average annual real GDP per capita growth, percent Average annual real GDP per capita growth, percent<br><!-- End of picture text --> _Sources_ : Authors’ calculations based on data from Standardized World Income Inequality Database (SWIID) and World Bank. _Note_ : Panel a shows a sample of selected Asian and Latin American economies. Panel b shows an extended sample of advanced economies and developing countries. Market Gini is before taxes and transfers.

The empirical relationship between inequality and growth has been investigated formally in a number of cross-country growth studies, following Barro and Sala-i-Martin (1995). Many of these studies find that inequality, typically measured by a Gini coefficient, enters with a negative and statistically significant sign in cross-country growth regressions, indicating that an increase in inequality leads to lower economic growth. In a survey of 23 different empirical studies on inequality and growth, for instance, Bénabou (1996) finds that despite differences in data sets, sample periods, and measures of income distribution, the studies consistently find that initial inequality is negatively associated with growth. In particular, the quantitative effects of inequality are quite robust across studies: a one-standard-deviation decrease inequality raises the annual growth of GDP in the range of 0.5 percentage points to 0.8 percentage points.

Various studies examine different dimensions of the relationship. An early work by Alesina and Rodrik (1994) finds that income and land inequality are statistically significant variables that decrease long-term growth in a sample of 70 advanced and developing countries. Perotti (1996)

> 14 The negative relationship between inequality and growth remains robust even when the analysis controls for the initial level of income, as is standard in growth regressions (see Barro 2000).

25
designated as controls, which contaminated the original evaluation design. Nonetheless, a followup survey was carried out in late 2010 and early 2011 in the six districts (Bushenyi, Ibanda, Isingiro, Kamwenge, Kiruhura, and Mbarara) where the SD voucher service had first been launched.

Both baseline and follow-up surveys used a two-stage cluster sample design. First, geographic data obtained from the Uganda Bureau of Statistics (UBOS) were used to identify parishes within six to ten kilometers of 13 facilities that had been contracted to provide services to voucher clients and within three kilometers of a major road. Parishes were then randomly selected from among those within the stipulated distance to the facilities. In the second stage, villages were randomly selected from the sampled parishes. At baseline, 58 parishes were randomly selected for inclusion in the study. There were 231 villages within these parishes with populations ranging from 75 to 1,803 inhabitants, giving a total of 102,260 persons, according to the 2002 census. A total of 94 villages were randomly selected for inclusion in the baseline survey study.

In the follow-up survey, 75 parishes were randomly sampled, and 133 villages from these parishes were selected for inclusion in the study. Of the sampled villages, 68 had been included in the 2008 survey, and 65 were sampled from within five kilometers of the contracted facilities to maximize the possibility of contacting respondents likely to have used the vouchers. This approach was adopted because the 2010 voucher claims data showed a very high concentration of clients in parishes within five kilometers of contracted facilities. Of the 94 baseline and 133 endline villages, none of the baseline and 26 of the endline villages were within five kilometers, and all 94 of the baseline and 68 of the endline villages were within six to ten kilometers.

Besides the population surveys, cross-sectional health facility assessment (HFA) data were collected in 2010 through a number of techniques, including observations of client-provider interactions (CPIs) during consultations, client exit interviews, interviews with SPs, record reviews, facility inventories, and service statistics. The HFA was conducted in 20 contracted (ten offering STI and ten offering safe motherhood services) and ten comparison facilities. The facilities were specifically selected based on the type of services offered and their size in terms of client volumes. This report uses data from CPIs and interviews with SPs. Additional crosssectional data were obtained from (1) voucher sales and claims up to mid-2011, provided by the VMA, (2) semistructured interviews conducted in August 2010 with SPs from contracted and noncontracted facilities to examine workload and the level of job satisfaction among the SPs, and (3) cost data for the voucher program, obtained from the VMA in July 2011.

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The fewest commitments were made for mode 4 (temporary movement of natural persons supplying services), with an average of 3.5 percent. Mode 4 is generally subject to numerous restrictions by most governments due to fears of an influx of too much unskilled labor. Mode 4 is followed by mode 1 (cross border trade) with 50.5 percent. The level of commitments on modes 2 (consumption abroad) and 3 (commercial presence) are the highest and quite similar, in the 6065 percent range. This pattern of commitments matches quite well with what would be expected on standard political economy grounds – modes 1 and 4 are the modes that are of greatest export interest to developing countries, and where there is domestic (EU) interest group-based opposition to liberalization. If the focus is on specific commitments by EU member states, the greatest variance with the EU average commitment is found for mode 3. France, Italy, Greece, Portugal, Finland and Sweden score 10 or more percentage points below the benchmark for the level of bound market access. The standard deviation of the commitments is twice as high for mode 3 commitments as for other modes on market access. This pattern reveals that although modes 1 and 4 are more “sensitive” for all countries, within the EU there were different sensitivities on mode 3. Annex table 2 reports the sectoral breakdown of the summary indicators reported in table 1 – health, transport and cultural services tend to be among sectors where the fewest commitments are made.

Table 2 matches the proposals (offers) EU countries made in the Doha Round as of 20032004 against the prevailing GATS regime that is summarized in table 1. These data cover the most recent proposals to the extent that they were officially published and are not just snapshots of current trade negotiations as debated in the media. We thus implicitly treat any additional proposal as unofficial. The data in table 2 are drawn from an analysis by Langhammer (2005) and reflect the same methodological approach as in table 1, i.e. the Hoekman commitment index. The offer made by the EU substantially increases the average commitment index from 46 to slightly above 58 percent. At the aggregate level the standard deviation would fall from 2 to 1.6, indicating an increase in uniformity at the EU member state level. In terms of the various modes, commitments in mode 4 improve but remain far below that in other modes. Only Finland’s offer on mode 4 is far-reaching, but only for national treatment. On market access it is similar to most other EU countries on mode 4. The relative ranking of the “sensitivity” of the four modes remains the same. In relative terms the smallest expansion in commitment levels occurs for mode 1, presumably reflecting the concerns about outsourcing that emerged during the early years of 6
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urban areas. In rural areas, results show that the average gain in girls’ secondary school enrollment is significant across all subperiods and ranges from 10 to 31 girls per school (see column 2 of table 4).

While overall program effects differ somewhat in size, they are statistically similar across all subperiods, on the basis of an examination of Fisherian confidence intervals (figure 4). The size and statistical significance of the program effects are sustained over the study’s observation period despite a decline in the real value of the annual program benefit of more than 60 percent over the period, from PRs 2,400 in 2004 to PRs 860 in 2015 (figure 5). As a result of this decline in the real value of the benefit, along with an increase in average real household income over the period, the relative benefit level decreased from an amount equivalent to 14 percent of average household consumption spending per adult equivalent in Punjab to 4 percent.<sup>17</sup> As a falsification test, we estimate local randomization-based RD effects near two arbitrarily selected false cutoffs of the program assignment variable, at 30 percent and 50 percent. The results are presented in table 5. While the estimated effects (overall and rural only) are sometimes large, they are almost always statistically insignificant. The only exception is the estimated effect for the subperiod 2003–11 measured near the false cutoff of 30 percent. Significant at the 5 percent level, the effect indicates a decline in girls’ secondary school enrollment in the false RD program district.

Note that, because of data limitations, we are not able to evaluate the extent to which the estimated program effects on girls’ government school secondary enrollment numbers reflect gains in the likelihood of girls’ secondary school participation. If the program mainly induced girls who would have joined secondary schools without the program to choose government schools over private schools, the gains in girls’ secondary school participation

> 17 Based on statistics from the 2004/05 and 2015/16 Pakistan Household Income and Expenditure Surveys.

18
# **Table S2.E continued**

|VARIABLES|(9)|(10)|(11)|(12)|(13)|(14)|(15)|(16)|
|---|---|---|---|---|---|---|---|---|
||pcf_hk|directimport|sellcredit|subcont|pcf_cont|reput_1|reput_2|reput_3|
|supbuy|-0.152|0.0491|0.0313|0.0767|0.110|-0.0726|0.0131|-0.0891|
||(0.237)|(0.124)|(0.107)|(0.116)|(0.258)|(0.209)|(0.207)|(0.173)|
|samesup|-0.0459|0.0147|0.0554|0.0322|-0.121|0.137|0.00529|-0.113|
||(0.115)|(0.0556)|(0.0588)|(0.0594)|(0.122)|(0.131)|(0.114)|(0.0867)|
|samecus|0.132|0.102|0.0231|0.0498|0.179|0.137|0.0147|-0.0886|
||(0.131)|(0.0628)|(0.0566)|(0.0726)|(0.145)|(0.0989)|(0.139)|(0.102)|
|same_sec|-0.0846**|-0.0578*|0.00718|-0.00606|-0.0531|-0.0331|-0.00254|0.0149|
||(0.0362)|(0.0297)|(0.0134)|(0.0173)|(0.0415)|(0.0473)|(0.0363)|(0.0311)|
|ldist|-0.0272***|-0.0134**|0.00116|0.00511|-0.0101|0.00928|-0.0189***|-0.0162***|
||(0.00821)|(0.00593)|(0.00178)|(0.00699)|(0.00825)|(0.00624)|(0.00637)|(0.00545)|
|ad_fmage|0.00225|-0.000429|0.000481|-0.00206***|-0.00417***|-0.000194|-0.000751|-0.00165|
||(0.00210)|(0.000803)|(0.000557)|(0.000636)|(0.00145)|(0.000405)|(0.00181)|(0.00109)|
|ad_meduc|0.0328|0.0300|0.000648|-0.0205***|0.0373|-0.00390|-0.00794|-0.00937|
||(0.0351)|(0.0199)|(0.00499)|(0.00747)|(0.0279)|(0.00756)|(0.0117)|(0.0110)|
|ad_mexper|-0.000825|-0.00152*|0.000481|-0.00177|0.00456**|0.000127|-0.00130|0.00310|
||(0.00165)|(0.000778)|(0.000642)|(0.00120)|(0.00210)|(0.000931)|(0.00303)|(0.00264)|
|ad_mfemale|-0.00505|0.0457|0.00375|0.0236|0.0402|-0.00813|-0.00607|0.0601|
||(0.0404)|(0.0297)|(0.00949)|(0.0306)|(0.0420)|(0.0130)|(0.0558)|(0.0550)|
|ad_lemp|0.230***|0.132***|0.00247|0.0151|0.138***|0.00234|-0.00421|0.00890|
||(0.0309)|(0.0150)|(0.00472)|(0.0132)|(0.0312)|(0.00657)|(0.0180)|(0.0150)|
|secdist|0.00744|0.00414|-0.00510|0.000705|0.00357|0.00620|-0.00316|-0.00318|
||(0.00607)|(0.00453)|(0.00314)|(0.00335)|(0.00692)|(0.00736)|(0.00650)|(0.00552)|
|seclink|0.186|-0.0398|-0.0823|0.0592|0.0105|-0.198|0.0323|0.0660|
||(0.121)|(0.0623)|(0.0629)|(0.0639)|(0.127)|(0.135)|(0.137)|(0.103)|
|Constant|0.709***|0.222***|0.479***|0.227***|0.862***|0.956***|0.836***|0.722***|
||(0.0722)|(0.0437)|(0.0200)|(0.0457)|(0.0627)|(0.0354)|(0.0778)|(0.0575)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 6
# **IV.D. Regression result: Analysis of salary specified in ads - Do male-specified job ads offer higher salary than female-specified ones?**

Job ads usually come with offered salary; hence, employers in many cases explicitly stipulating for male or female employee provides us with the opportunity to directly observe how employers value and perceive men and women’s productivity. Of all 830,929 ads that come with specified salary, average salary specified per month is 11,841 rupees. Average monthly salary specified by ads with and without gender specification are 10,581 rupees and 12,558 rupees, respectively. It has already been shown that ads for lower-end jobs have higher odds of being gender-targeted. And that could be the reason for this mean salary gap. However, results from our OLS regression analysis with occupation category fixed effect (see Table A2 in appendix) show salary specified in gender-targeted ads on average is less by 6 percent after controlling for within-occupation category variation, job and employer characteristics such as years of experience required, work shift, type of employer, and the year the ad was listed.

Now within the pool of gender-specified ads, salaries specified in female-targeted ads are on average lower by 10 percentage points controlling for all occupation categories and other covariates (see Table 4). We use teaching as the base category for two reasons: firstly, though teaching falls under professional category, salary for teachers is not only the lowest among all professional categories but also comparable to that of all other occupations; and secondly, teaching is one of the least gender targeted occupations. We use the result of OLS estimation of extended model with interaction terms (Model 5B) to predict the log of salaries, the pairwise predictive margins between male and female salary and the contrast between the margins. The predictive margins take care of both sources of discrimination – 1. being a female in general in the job market and 2. being a female within each occupation group. Figure 6 shows the gender gap in ad-specified salary for each occupation group based on predictive margins.

Table 4: OLS estimation of (log of) specified salary within gender-targeted pool of ads

|Natural log of monthly salary|Model 5|A: Basic|Model 5B: Extend|ed with interactions|
|---|---|---|---|---|
||Coef.|Std. Err|Coef.|Std. Err|
|Experience|0.03|0.00***|0.03|0.00***|
|Work shift (base: Not mentioned)|||||
|Full|-0.05|0.00***|-0.06|0.00***|
|Part-time|-0.22|0.00***|-0.22|0.00***|
|Day time|-0.04|0.00***|-0.05|0.00***|
|Night shift|0.03|0.01**|0.00|0.01|
|Employer Type (base: SME & staffi|ng company)||||
|Unknown|0.00|0.00*|0.00|0.00|
|Household|-0.14|0.00***|-0.09|0.00***|
|HR enterprise|0.12|0.00***|0.11|0.00***|
|Period Dummy|||||
|Year 2012|0.17|0.00***|0.16|0.00***|
|Year 2013|0.21|0.00***|0.20|0.00***|
|Year 2014|0.26|0.00***|0.25|0.00***|
|Year2015|0.24|0.00***|0.23|0.00***|
|Year2016|0.09|0.00***|0.09|0.00***|
|Year 2017|0.32|0.01***|0.30|0.01***|
|Occupation categories (base=Teachi|ng)||||
|Professional|0.38|0.01***|0.28|0.01***|

13
**<u>Table 8.</u>** <u>Effects of gender role attitudes and personality traits on profits (</u> _<u>pooled</u>_ <u>sample)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Norms|Norms+<br>Controls|Norms+<br>Controls+<br>Personality|_Norms+_<br>_Controls+_<br>_Impulsive_|_Norms+_<br>_Controls+_<br>_Work_<br>_Central_|_Norms+_<br>_Controls+_<br>_Tenacity_|_Norms+_<br>_Controls+_<br>_Polychroni_<br>_city_|_Norms+_<br>_Controls+_<br>_Locus of_<br>_Control_|_Norms+_<br>_Control+_<br>_Achieveme_<br>_nt_|_Norms+_<br>_Control+_<br>_Power_<br>_Motivation_|_Norms+_<br>_Controls+_<br>_Organized_|_Norms+_<br>_Controls+_<br>_Optimism_|
|Traditional|-0.011***|-0.003|-0.007**|-0.008|-0.002|-0.014|-0.021|-0.011|-0.004|0.015|0.007|0.009|
||(-2.73)|(-0.77)|(-2.00)|(-0.47)|(-0.12)|(-0.69)|(-1.54)|(-0.72)|(-0.18)|(1.04)|(0.38)|(0.27)|
|Impulsive|||0.023|-0.073|||||||||
||||(0.25)|(-0.24)|||||||||
|Work centrality|||-0.000||0.101||||||||
||||(-0.00)||(0.50)||||||||
|Tenacity|||0.016|||-0.025|||||||
||||(0.18)|||(-0.10)|||||||
|Polychronicity|||-0.030||||-0.421*||||||
||||(-0.49)||||(-1.80)||||||
|Locus of control|||-0.088*|||||-0.270|||||
||||(-1.69)|||||(-1.14)|||||
|Achievement|||0.134||||||0.217||||
||||(1.25)||||||(0.64)||||
|Power motivation|||0.044|||||||0.375*|||
||||(0.64)|||||||(1.71)|||
|Organized|||0.141**||||||||0.251||
||||(2.25)||||||||(1.25)||
|Optimism|||-0.318***|||||||||-0.151|
||||(-3.28)|||||||||(-0.35)|
|Traditional*Impulsive||||0.002|||||||||
|||||(0.32)|||||||||
|Traditional*Work centrality|||||-0.000||||||||
||||||(-0.01)||||||||
|Traditional*Tenacity||||||0.003|||||||
|||||||(0.59)|||||||
|Traditional*Polychronicity|||||||0.009||||||
||||||||(1.52)||||||
|Traditional*locus control||||||||0.002|||||
|||||||||(0.43)|||||

38