rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5037

# **Abstract**

HIV/AIDS is drastically changing the demographic landscape in high-prevalence countries in Africa. The prime-age adult population bears the majority of the mortality burden. These “missing” prime-age adults have implications for the socioeconomic well-being of surviving family members. This study uses a 13-year panel from Tanzania to examine the impacts of prime-age mortality on the time use and health outcomes of older adults, with a focus on long-run impacts and gender dimensions. Prime-age deaths are weakly associated with increases in working hours of older women when the deceased adult was co-resident in the household. The association is strongest when the deceased adult was living with the elderly individual at the time of death and for deaths in the distant past, suggesting that shorter-run studies may not capture the full extent of the consequences of adult mortality for survivors. Holding more assets seems to buffer older adults from having to work more after these shocks. Most health indicators are not worse for older adults when a prime-age household member died, although more distant adult deaths are associated with an increased probability of acute illness for the surviving elderly. For deaths of children who were not residing with their parents at baseline, the findings show no impact on hours worked or health outcomes.

This paper—a product of the Poverty and Inequality Team, Development Research Group—is part of a larger effort in the department to understand the socioeconomic impact of HIV/AIDS. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kbeegle@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
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Baumgartner, H. J., & Steiner, V. (2006). _Does More Generous Student Aid Increase Enrolment Rates into Higher Education? Evaluating the German Student Aid Reform of 2001_ (SSRN Scholarly Paper No. ID 892831). Rochester, NY: Social Science Research Network.

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35
Using HBS data presents some challenges, the main one being the absence of data or responses that match exactly with the hypotheses we made in this paper. For instance, the HBS does not collect information related to transaction and search costs. Moreover, the data set that is available to us is a household data set, and does not include any information about local assets and conditions. This will require us to use proxies in some instances without sacrificing the robustness of the analysis and the precision of the results.

# **5.0 The analysis**

The 2007 HBS indicates that distances to the markets have been increasingly affecting rural prices. Figures 3 and 4 below show that in 2000/ 01 maize prices remained constant as household distance to markets increased. But by 2007, the price of maize had become more responsive to distances decreasing the further the households were from the market. This implies that maize growing households that live further away from markets have become more vulnerable. It may also partly explain why the HBS recorded a decline in rural consumption by the bottom ten percent of the rural population.

_Figures 3 and 4: Farm gate price of maize and distance to market_

# **Maize flour**

# **Maize grain**

<!-- Start of picture text --> 500<br>400 2007<br>300<br>200<br>2000/01<br>100<br>0<br>0 2 4 6 8 10 12 14 16 18 20<br>Distance to market (km)<br>Price per kg of maize flour<br><!-- End of picture text --> <!-- Start of picture text --> 250<br>2007<br>200<br>150<br>100<br>2000/01<br>50<br>0<br>0 2 4 6 8 10 12 14 16 18<br>Distance to market (km.)<br>price per kg of maize grain<br><!-- End of picture text --> Source: Poverty monitoring group, 2008.

12
How inertia and limited potentials aect the timing of sectoral abatements in optimal climate policy Adrien Vogt-Schilb<sup>_a,†_</sup> , Guy Meunier<sup>_b_</sup> , Stéphane Hallegatte<sup>_c,d_</sup> <mark>August 1, 2012</mark>

> _†_ Corresponding author (vogt@centre-cired.fr)

> _a_ CIRED, Nogent-sur-Marne, France

> _b_ INRAUR1303 ALISS, Ivry-Sur-Seine, France

- _c_ The World Bank, Sustainable Development Network, Washington D.C., USA _d_ École Nationale de la Météorologie, Météo-France, Toulouse, France

Keywords: climate change mitigation, sectoral policies, optimal policies, optimal timing, inertia, when-exibility, how-exibility, overlapping policies.

JEL classication: L98, O21, O25, Q48, Q54, Q58 1
itself accounts for about one-fourth of the observed variation in volatility across countries, which also indicates that they are key determinants of consumption volatility (Table 1.2).

<!-- Start of picture text --> Graph 1.3: Consumption Volatility and Risk-Coping Financial Mechanism<br>o Canbbean<br>|- OtheOs<br>2 ++<br>14 o o+ ++++|o=<br>+ +~~~<br>6 + 0+0 + 0<br>+ 0 +~~<br>6 +! + +<br>+ +0<br>0<br>0 to 20 30 40 so 60<br>(M I/DP)t/Vointility of Per Capita GDP<br>Source, SIMA database (91 countries included)<br>Graph 1. Consumption Volatility and Risk-Coping Insurance Mechanism<br>o Caribbean<br>+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~+ Othoers<br>0<br>16<br>2 14<br>0~~~~. ~ ~ ~<br>+4<br>n~~I +++ + o<br>2 +<br>0 +~~~~<br>6<br>>~~~~~~~~~~~(nuac rmusG)YoaiiyoPrCpt D<br>Source, SIMA databas(ensranc Prm77nscountriesliy oiPerlapdteG)<br><!-- End of picture text -->

12
Table 10 Robustness: High-risk and severely exposed areas

|Dependent: Change in|grades||||
|---|---|---|---|---|
||Ban|gladesh flood|Ethiopia|Malawi|
|Disaster variable:|Depth|Days Repair cost|Outhome Drought|Drought|
|Sample:|Seve|rely exposed|High risk|High risk|
|Disaster|-0.4125|-0.0260 -0.0009|-0.0118 -1.2152|-2.2863|
||(2.350)|(2.420) (2.510)|(0.310) (1.790)|(29.47)|
|Public work|||-1.1361||
||||(2.160)||
|Food aid|||-0.2049|-1.8825|
||||(0.570)|(13.39)|
|Disaster * GR|0.0003|0.00001 6.76E-07|0.00006||
||(1.950)|(1.330) (0.630)|(3.500)||
|Disaster * GFV|0.0003|0.00001 3.71E-07|0.00002||
||(5.800)|(2.940) (1.280)|(2.380)||
|Disaster * NGO|0.0004|0.00001 6.68E-07|0.00005||
||(2.740)|(0.860) (0.870)|(1.260)||
|Disaster * public work<br>|<br>|<br>|1.6314<br>(3.960)||
|Disaster * food aid|||-0.0264|2.2333|
||||(0.050)|(15.41)|
|Disaster * max educ|0.0227|0.0014 0.00005|0.0030 0.0085|0.1635|
||(1.760)|(1.500) (2.230)|(1.740) (0.550)|(3.520)|
|Disaster * land|0.0012|0.00003 4.12E-07|-0.0001 -0.4038|-0.0094|
||(2.360)|(1.310) (0.410)|(0.370) (1.760)|(0.300)|
|Disaster * hh size|0.0044|0.0010 0.00003|-0.0115 -0.0128|0.0524|
||(0.180)|(0.700) (2.990)|(1.590) (0.270)|(1.040)|
|Disaster * livestock|-5.82E-0|6 1.84E-07 5.64E-09|1.68E-06 0.00008|-0.00002|
||(0.960)|(0.650) (0.160)|(0.990) (1.530)|(1.450)|
|Disaster * female|0.0955|0.0130 -0.00008|0.0338||
||(0.650)|(0.720) (0.600)|(3.610)||
|Disaster * male|||0.5068|0.3377|
||||(0.640)|(0.700)|
|Union FE|yes|yes yes|yes||
|Region FE|||yes|yes|
|Number of observatio|ns 121|121 121|121 168|96|
|Number of unions|17|17 17|17||
|Number of regions|||5|4|
|R squared (within)|0.2926|0.2066 0.1640|0.1974 0.1655|0.3048|

Numbers in parentheses are absolute t values using robust standard errors with union clusters. Age and female (or male) dummies are included in all specification. GR, GFV and NGO are the sum of transfers received in 1998-1999 from respective sources. Severely exposed sample is used in Bangladesh. In Ethiopia and Malawi, high risk sample is defined if the drought probability is greater than 0.40 and 0.50 respectively (see Table 1).

32
Policy ReseaRch WoRking PaPeR 4616

# **Abstract**

Using household level data from Bangladesh, this paper examines the differences in the rates of return to household attributes over the entire welfare distribution. The empirical evidence uncovers substantial differences in returns between an integrated region contiguous to the country’s main growth centers, and a less integrated region cut-off from those centers by major rivers. The evidence suggests that households with better observed and unobserved attributes (such as education and ability) are concentrated in the integrated region where returns are higher. Within each region, mobility of workers seems to equalize returns at the lower half of the distribution. The natural border created by the rivers appears to hinder migration, causing returns differences between the regions to persist. To reduce regional inequality in welfare in Bangladesh, the results highlight the need for improving connectivity between the regions, and for investing in portable assets of the poor (such as human capital).

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to understand the implications of migration and access to market for regional inequality in living standards. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fshilpi@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
This paper contributes to the literature on telecommunications and economic performance in two ways. First, it takes into account how different telecom services—cellular, fixed-line, and the Internet—affect firm performance differently. We find that firms grow faster and are more productive in countries with better Internet access and in regions of countries where firms use the web more intensively. Correlations between firm performance and Internet access are robust to different model specifications. In contrast, we find no evidence that firms perform better in countries with greater access to fixed-line and cellular services.

Second, rather than focusing on economic growth, we look at how access to telecom services affects firm performance. If telecom services are general-purpose technologies, firm performance should be better in countries with better telecom acces and in _all_ industries—not just telecom-producing industries. Indeed, performance is better for all the firms that we consider: in both high- and low-tech industries; in small, medium-sized, and large firms; and exporters and non-exporters alike. Small firms, however, benefit more than other firms do.

# **Data and empirical methodology**

We use worldwide firm-level data from the World Bank’s Enterprise Surveys (WBES). The surveys have been completed in over 100 developing countries since 2006. Although the surveys include retail and service firms, we exclude them because detailed information on firm performance is only collected for manufacturing firms.<sup>2</sup> To investigate the relationship between firm performance and telecommunications, we estimate the following regressions:

> 2 The Enterprise Survey website (www.enterprisesurveys.org) describes the data.

3
4 payments, more time spent on negotiations, and a heavy regulatory burden go hand in hand with larger bribes for day-to-day operations (See Annex 2).

As in Svensson (2003) and Kuncoro (2006) this paper avoids the pitfalls of aggregate data. The analysis is based on the 2006 Investment Climate Survey (ICS) – conducted in 2005 – which contains data on 361 Mauritanian firms, located in Nouakchott and Nouadhibou and representing the manufacturing, retail, information technology, and other sectors (See Annex 4). The data set includes quantitative information on bribe payments. The paper aims to understand whether Mauritanian firms deem corruption as an obstacle to operate and grow, to identify the profile of firms that are more likely to make informal payments, and to quantify the size of these payments. To the authors’ knowledge, no analysis of corruption at the firm level in Mauritania has been carried out to date, hence the paper makes an initial contribution in this area of study.

The paper is structured as follows. Section 2 provides an overview of the Mauritanian context and explains why it is important to focus on corruption at the firm level. Section 3 presents the descriptive analysis focusing on the perception of corruption by Mauritanian firms, the main characteristics of petty corruption and the financial cost of corruption. Section 4 presents the theoretical framework and specification of the econometric model used, while Section 5 discusses the results of the econometric analysis on bribe propensity (i.e., the probability of paying a bribe) and intensity (i.e., the payments of bribes as a share of the firm’s annual sales). Section 6 concludes.

# **2. The Importance of Reducing Corrupt Practices and Stimulating Competition for Private Sector Development**

Mauritania is at a cross-road. With relatively good natural resource prospects (oil, iron ore, fisheries, copper and gold), the economy is poised to grow rapidly in the future potentially triggering a transition from low to middle-income country.<sup>2</sup> The key challenge confronting policy makers today is to diversify the sources of growth besides

> 2 Mauritania has a population of 2.9 million people and a per capita GDP of US$ 921 (World Bank, 2007). The non-oil (oil) GDP growth rate in 2006 was 4.4 percent (11.7 percent with oil).
the modeled area effect for each small area. The model was estimated using the fayherriot package in Stata (Halbmeier et al., 2019). The third column of Table A3 presents the coefficients from a post-LASSO OLS regression using the sample data, collapsed to the municipal (area) level. We provide more details on these alternative models in Appendixes C and D.

# 5. Main Results

## _5.1 Bias and uncertainty_

Table 2 displays evaluation metrics for overall bias and uncertainty for the two direct survey estimates (the Horvitz-Thompson approximation and AGEB-clustered variance estimates), the household model, the sub-area model, the area-level model, and the 2010 official poverty estimates produced by CONEVAL. For reference, the bottom row shows the mean poverty estimates for the official 2015 poverty estimates used as the benchmark.

The first column shows the simple average, across municipalities, of the estimated poverty rate prior to benchmarking, while the second and third column of Table 2 report two measures of the uncertainty of the point estimates – the mean estimated mean squared error and the median coefficient of variation. Remarkably, for sampled municipalities, the mean estimated poverty rate from the household model, prior to benchmarking, is only 0.1 percentage points lower than the direct estimate (28.1% vs 28.2%). This reflects the success of the ordered quantile normalization transformation at virtually eliminating overall bias in the household model, despite the slight deviations in the household error term from normality shown in Figure 1. It also suggests that any bias due to omitting household characteristics from the model in this context does not systematically bias the municipality estimates, at least on average. The mean poverty rate for the sub-area model matches the direct estimates to one decimal place, while the area-level model is substantially more downward biased.

There are four other striking findings in the table. The first is the difference in estimated uncertainty between the Horvitz-Thompson and standard clustered estimates of variance. This reflects the high degree of correlation in per capita income across AGEBs within municipalities. The standard clustered estimates do not take these into account and therefore severely 21
boLe 3 FiXeo etfect estimacion using siA-year averages and tevets or traae OootIcY

||cl)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|TR1|0.20*-<br>(.08)|||||
|BLACK|-|-0.02"<br>(.004)||||
|DOLLAR||.|0.05**<br>(.02)|||
|TRADE<br>SHARES||-||-0.02<br>(.02)||
|INDIRECT|||-|-|5.t6'<br>(3.61)|
|LAND|-0.13<br>(.17)|0.01<br>(.11)|-0.23<br>(.13)|0.12<br>(.11)|-0.09<br>(.19)|
|LABOR<br>FORCE|0.36<br>(.58)|0.83<br>(.28)|0.60<br>(.32)|0.67<br>(.28)|0.68<br>(.52)|
|EDUCATION<br>(0-3<br>Years)|0.17<br>(.16)|0.14<br>(.06)|0.09<br>(.10)|0.06<br>(.06)|0.04<br>(.11)|
|E3DUCATION<br>(3-9<br>Years)|0.07<br>(.09)|0.04<br>(.04)|0.03<br>(.06)|0.00<br>(.04)|0.06<br>(.08)|
|CAPITAL STOCK|0.54<br>(.07)|0.42<br>(.04)|0.51<br>(.05)|0.44<br>(.04)|0.36<br>(.07)|
|;-Value<br>1/|1.1|1.6|1.6|1.4|1.3|
|N|67|237|125|265|69|
|R-Square|.76|.71|.75|.85|.76|

Tabte 9 Fixed effect estimation using six-year averages and changes in trade policy

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|TR 1|0.20<br>(.24)||||||
|BLACK||-0.02<br>(.02)|-||||
|COLLAR|||0.04".<br>(.01)||||
|TRADE<br>SHARES|-|||0.23"<br>(.10)|||
|INDIRECT|-|-|-|-|16.75**<br>(5.27)||
|Movement towards<br>international<br>Prices|-|-|-|-|-|0.05<br>(.03)|
|F-Value<br>1/|0.7|1.4|1.4|1.5|1.6|0.7|
|R-Square|.72|.67|.74|.65|.80|.69|

Note: Stanoard errors in 0."" indicates significant at 5 percent level; * indicates significant at 10 percent Level. 1/ F-Value for test of Nutt hypothesis that country effects are 0.

30
First, do mothers (or care-givers) recognize fever in their children? A recent exchange in _The Lancet_ set off by Einterz and Bates comes to no firm conclusion (Einterz and Bates 1997, Dunyo, Doram, and Nkrumah 1997, Verhoef and others 1998, Koefoed and others 1998), and this paper will certainly not resolve the issue. Table 2 summarizes the main results pertaining to children to come out of the exchange. While the studies are not entirely comparable (e.g., differences in study methodology and differences in overall percentage of population with fever) they are consistent in finding that among children with measured fever (actual temperature of 37.5°C or higher) parents (or care-givers) tend to report fever accurately most of the time, in the cases here, between 78 and 98 percent of the time. On the other hand, among children without fever, anywhere from almost none to almost all of the children are reported to have a fever. It is somewhat reassuring that the study that most resembles the DHS-the community survey in Ghana-has reasonably high sensitivity (78 percent) and rarely found reports of fever when it was not present (0.8 percent). Note that the findings are consistent with a bias towards reporting fever, even when it isn't true. Since such a bias would tend to reduce the variation in reported fever, this would tend to dampen any subsequent study of the correlation of fever with other factors.

**Table 2. Summary of results on sensitivity and specificity of reported and measured fever in three studies**

||_Number_<br>_of_<br>_children_|_Overall_<br>_percentage_<br>_withmeasured_<br>_fever_|_Percentagewith_<br>_measuredfever_<br>_whoarereported_<br>_tohavefever_<br>_("sensitivity")_|_Percentagewithout_<br>_measuredfeverwho_<br>_arereportedtohave_<br>_fever(100-_<br>_"specificity')_|
|---|---|---|---|---|
|Patients, districthospitalin<br>northernCameroon,children<br>under5|494|34|92|56|
|Two communitiesinsouthern<br>Ghana,childrenunder5|1714|3.5|78|0.8|
|HealthCenter in theoutskirtsof|||||
|Bissau,Guinea-Bissau,|||||
|"children"withsymptoms<br>compatiblewithmalaria|203|81|98|97|

_Source:_ Einterz and Bates (1997), Dunyo, Doram, and Nkrumah (1997), Verhoef and others (1998), Koefoed and others (1998).

An additional complication is the fact that individuals of different socioeconomic backgrounds might report a true episode of fever differently, and that a subsequent analysis of the correlation of fever with these socioeconomic variables would be biased in the direction of the selective reporting. While the issue of self-selective reporting is frequently cited as a cause for worry, there has been little systematic investigation of how severe a problem this might be. Studies typically conclude that more easily observed symptoms are less likely to suffer from self-selection (for examples of studies addressing this issue see Butler and others 1987 for an example from the United States and _5_