rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8282

# **Abstract**

This paper reviews methods that have been employed to estimate poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross-sectional household surveys, to missing panel household data. The paper focuses on methods that aim to compare trends and dynamic patterns of poverty outcomes over time. It presents the various methods under a common framework, with pedagogical discussion on the intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, the paper provides a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.

This paper is a product of the Development Data 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 authors may be contacted at hdang@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
**Table A.1. Diarrheal disease (DD) incidence per person per year in 2000**

|**World region**|**DD incidence in all**<br>**ages**|**DD incidence in**<br>**0−4 year olds**|**DD incidence in**<br>**5−80+ year olds**|
|---|---|---|---|
|Industrialized<br>countries<sup>_a_</sup>|0.2|0.2–1.7|0.1–0.2|
|Developing countries|0.8–1.3|2.4–5.2|0.4–0.6|
|Global average|0.7|3.7|0.4|

> _a_ In some industrialized countries diarrheal disease incidence is much higher − for example, 0.92 pppy for ‘infectious gastroenteritis’ in Australians of all ages (Hall _et al_ ., 2005) and 0.79 pppy for ‘acute gastroenteritis’ in Americans of all ages (Mead _et al_ ., 1999) − i.e., in the developing-country range shown in the table. _Source:_ Mathers _et al_ . 2002.

Thus it seems perfectly reasonable to accept a maximum additional DALY loss of 10<sup>−4</sup> pppy for wastewater use in agriculture. The required pathogen reductions would then be:

- To protect fieldworkers’ health (restricted irrigation, labor-intensive agriculture): 2 log units, and

- To protect crop consumers’ health (unrestricted irrigation): 4−5 log units.

The implications of these required pathogen reductions are:

- Wastewater treatment processes to achieve a pathogen reduction of only 2 log units are very simple and very low-cost, and

- Reliable post-treatment health-protection control measures (Table 4.2) can easily achieve the remaining 2−3-log unit reduction required for unrestricted irrigation − for example, pathogen dieoff (which can be expected to achieve a reduction of at least 2 log units in warm climates) and produce washing in clean water (1 log unit).

# **A.2  Improved Method for Estimating Annual Infection Risks**

Since the publication of the 2006 WHO Guidelines, an improved method of estimating annual infection risks from QMRA-MC simulations has been developed by Karavarsamis and Hamilton (2010), as follows:

1. Using the appropriate dose-response equation in an appropriate QMRA-Monte Carlo computer program, an estimate of median annual infection risk is determined by a Monte Carlo simulation in which the number of iterations is set equal to the number of days of exposure per year.

2. This is repeated 9,999 times, so that there are 10,000 estimates of median annual infection risk.

The median and 95-percentile values of these 10,000 estimates are then calculated in order to provide a much more robust estimate of median and 95-percentile annual infection risks.

Thus the program determines 10,000 estimates of median annual risk based on what happens in any one <u>year</u> of exposure (i.e., _n_ exposures to a pathogen dose _d_ ), rather than (as in the procedure used in the Guidelines) a much less robust estimate of median annual risk determined from 10,000 estimates of annual risk based on what happens on any one day of exposure. This approach results in similar values for estimates of median annual risks, but much lower estimates for 95-percentile annual risks, and there is less ‘spread’ of results (Table A.2).

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Kishore et al. 2021 Kummu, M, Taka, M and Guillaume, J H. 2018. “Gridded global datasets for gross domestic product and Human Development Index over 1990–2015.” _Scientific Data_ 5(1): 1–15.

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<mark>Mason, N M and Ricker-Gilbert, J. 2013. “Disrupting demand for commercial seed: Input subsidies in Malawi and Zambia.”</mark> _<mark>World Development</mark>_ <mark>45: 75–91.</mark> Mason, N M and Tembo, S T. 2015. _Do Input Subsidy Programs Raise Incomes and Reduce Poverty Among Smallholder Farm Households? Evidence from Zambia_ . Technical report.

Mateo-Sagasta, Javier; Zadeh, S. M.; Turral, H.; Burke, J. 2017. Water pollution from agriculture: a global review. Executive summary. Rome, Italy: FAO; Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Research Program on Water, Land and Ecosystems (WLE) Mideksa, T K. 2010. “Economic and distributional impacts of climate change: The case of Ethiopia.” _Global Environmental Change_ 20(2): 278–286.

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In relation to the effect of _new and unexpected opportunities for change_ , we found that receipt of a positive STI test at baseline was marginally associated with reporting change at the 4-month visit, and that receipt of a positive HIV test was significantly associated with unintended change. We did find some support for the contention that the effect of the positive STI test is stronger among women because it could be used as another potential leverage to convince husbands or partners to change. Similarly, being in the high-value cash award group was significantly associated with reporting unanticipated change at the 4-month visit, supporting the idea that this new opportunity revised the perceived ability to change, resulting in unintended behavior change. We find little support for the contention that the cash incentive might provide additional leverage for women in convincing partners to change-- the relationship between being in the cash award group and changing unexpectedly does not differ by gender.

# **Discussion**

Two main findings result from this analysis. First, within the context of the RESPECT trial in Tanzania, perceived risk is an important driver of sexual behavior change. There are three possible reasons why: (a) the counseling sessions may have brought out the fact some participants are engaging in particularly risky behaviors, thus elevating the perceived need for change, and driving the reported behavior change; (b) as part of being enrolled in the study, participants may have on their own connected their reported risky behaviors with risk of infection (perhaps through receipt of STI or HIV test results) and therefore realized that they needed to change and did so; and (c) this group of risk-takers represents the low-hanging fruit. These participants were significantly more likely to report changing simply because they had more to change compared to those already engaging in safe sexual behavior, and the study provided them with an opportunity to engage in that change.

The second main finding is that we see some supporting evidence that exposure to new and unexpected opportunities in the form of receipt of STI test results or being eligible for the cash award was associated with overall change. However, we did not find evidence that the effects of these new opportunities were stronger for women than for men in terms of reported behavior change.

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export destinations:<sup>6</sup> Those indices always lie between 0 and 1, and are equal to 0 whenever there is exactly no overlap between countries _i_ and _j_ import sources / export destinations. Indices are equal to 1 in the case where countries _i_ and _j_ have _exactly_ the same geographical exposure to import / export markets. They also capture unobserved characteristics regarding the trade frictions and trade policies.

It is also interesting to explore the link between the level of inflation and GVC participation. In other words, whether the expansion of GVCs has contributed the synchronized reduction in global inflation observed in figure 1. For this purpose, the estimated equation is the following: Note that equation 4 is country-centric, with the dependent variable being the level of inflation in country _i_ , _πit_ . Accordingly, the the trade intensity indices included in the regression, _Tit_ , are country specific and in relative terms to GDP, as follows: Finally, note that fixed effects included in equation , _FEi_ and _FEt_ , control for the country and time dimensions. And it is not necessary to account for third country

> 6We actually use the indices computed in de Soyres and Gaillard (2019), where they are utilized in an analysis of GDP rather than inflation synchronization.

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some of the potential mechanisms that could be driving our results and 9 concludes.

# **2 Literature Review**

This study fits into the broad literature on the effects of violent conflicts. Several studies have analyzed the effect of ethnic conflict on economic growth (Abadie and Gardeazabal, 2003); human capital formation and demographic changes (Camacho, 2008; Duque, 2017; Brunborg and Urdal, 2005); long-term biases in terms of lending behavior and judicial results (Hjort, 2014; Shayo and Zussman, 2011, 2017); and women’s employment rates, fertility and agency (Goldin and Olivetti, 2013; Zárate-Barrera and Rogall, 2020; Thiede et al., 2020).

Of particular relevance is recent work from the broader social science literature that examines the effects of violent conflict on women’s marriages and changes in fertility (Torrisi, 2023; Sieverding et al., 2020; Torrisi, 2020). While one strand of the literature documents decreasing overall marriage rates during conflict (Khawaja and Randall, 2006; Saxena et al., 2004), another strand finds the opposite effects (Valente, 2011; Williams et al., 2012). Given the mixed findings in the literature where the effects of conflicts on marriage rates and age can go either way depending on the costs associated with marriage and the potential use of marriage as a "consumption-saving tool", the effects are not obvious (Saxena et al., 2004; Fussell and Palloni, 2004; Torrisi, 2022). This paper adds to the literature by examining the effects of intercommunal Hindu-Muslim riots on women’s marital outcomes, especially the age of marriage, using state-of-the-art event study designs.

There are several studies that have focused on the evolution, nature, determinants, and effects of the Hindi-Muslim riots in India (Varshney, 2002; BRASS, 2003; Wilkinson, 2004; Corbridge et al., 2012). While there is a relatively large literature on the determinants of Hindu-Muslim riots (Jha, 2013, 2014; Nellis et al., 2016; van Noort and Goyal, 2020; Bohlken and Sergenti, 2010; Mitra and Ray, 2014; Field et al., 2008) and some work on the effects of these riots on electoral votes and lending (Iyer and Shrivastava, 2018; Ticku, 2015; Fisman et al., 2020), there is limited work on the effect of these riots on women’s marital outcomes. To the best of our knowledge, this paper is the first to empirically examine the effect of Hindu-Muslims riots on women’s marriage market outcomes in India.

Examining the impact of communal riots on women’s marital outcomes is important and equally challenging to study as sexual violence against women was a key feature of the Hindu-Muslim riots but abysmally under-reported (Khanna, 2008; Srivastava, 2011; Sen and Jasani, 2021). As pointed out by Srivastava (2011): _“FIRs of women survivors were not lodged. In fact, the police refrained from registering rape as an individual crime. By clubbing rape with complaints of murder, sexual violence was made invisible across the state"_ . Given 4
**Table  2.6 Contribution of urban agriculture in selected cities in Asia and Africa**

|**City and Country**|**Contribution of urban agriculture**|
|---|---|
|**Asia**||
|Shanghai, China|60 percent of the vegetables consumed, produced on 300,000<br>hectares administered by the city<sup>a</sup>|
|Hong Kong, China|50 percent of the vegetables consumed<sup>b</sup>|
|Jakarta, Indonesia|Squatters produce 20 percent of their food consumption<sup>b</sup>|
|Singapore|25 percent of vegetables consumed<sup>b</sup>|
|**Africa**||
|Bamako, Mali|More than 100 percent of horticultural products consumed, including<br>some urban-rural exports<sup>b</sup>|
|Kumasi, Ghana|80-90 percent of consumed perishable vegetables<sup>c</sup>|
|Dakar, Senegal|Nearly 80 percent of the country’s vegetables are produced in the<br>Niayes zone in suburban Dakar, amounting to about 3 percent of<br>country’s land area)<sup>d</sup>|

_Sources_ :<sup>a</sup> Yi-Zhang and Zhangen, 2000;<sup>b</sup> Smit _et al_ ., 1996;<sup>c</sup> Drechsel _et al_ ., 2007;<sup>d</sup> Mbaye and Moustier, 1999.

Main reasons for the involvement in urban agriculture are income and employment generation as well as food security, particularly for the urban poor and unemployed (Smit _et al_ ., 1996). The urban environment also provides relative advantages, including direct access to urban consumers and markets; proximity to institutions that may provide market information, credit and technical advice; and availability of cheap inputs such as urban organic wastes and wastewater for irrigation (Veenhuizen and Danso, 2007). The recent rise in food prices is also likely to have contributed to an increased interest from urban households and local and national governments in expanding urban agriculture.

Together, these four drivers will contribute to increased wastewater use in agriculture, and the need to improve that use, especially in the lower- and middle-income countries.

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manufacturers' suggested retail price. The model simulates excise taxes based on information about the quantities of excisable goods consumed by households (if quantities are not reported, they are recovered from expenditures with <u>assumptions about the average unit value; for example, price for 1 liter of petrol).</u> Notes: (1) Typically, the eligibility for social allowances is defined using incomes reported for the 3, 6 or 12 months preceding the date of application. However, in the survey data, the reference period is 1 month preceding the survey. In simulations we assume that the amount of income reported is received at the same rate throughout the year.

(2) There are also rules in the policy spine not shown above because they do not define programs. Rather, they define objects, such as tax units, income lists, unreported incomes, subsistence minimum level, etc., that are used by the actual tax/benefit rules.

**Table A-2. RUSMOD input database, 2017**

|RUSMOD database|RF_2016_a1|
|---|---|
|Original name<br>Provider|Russian Longitudinal Monitoring Survey (RLMS-HSE)<br>National Research University - Higher School of Economics|
|Year of collection|2016|
|Period of collection|October-December 2016|
|Income reference period|Typically income and expenditure for the month preceding the survey, for some types<br>of expenditures - 3 months preceding the survey|
|Sampling|A three-stage stratified clustered probability sample of dwellings|
|Unit of assessment|Individual and household (people living together and sharing income and<br>expenditures)|
|Coverage|Permanent residents, people living in institutions are excluded|
|Response rate for household grid|80% (60% in Moscow and St-Petersburg)|
|Final sample used in the model||
|Individuals|12,554|
|Households|4,849|
|Population with grossing-up weight*||
|Individuals|143,454,148|
|households**|61,168,334|
|Population according to Census||
|Individuals|146,804,000|
|households**|61,168,333|

# Notes:

* Weights are used in order to adjust the results for sample design and non-response, as well as for deviations from the census. In addition, weights provided with the original data were scaled up to the total population.

** Estimated by dividing the population by the average household size. Source: RUSMOD for 2017 policy year and 2016 RLMS-HSE data

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**Table** 7 **PRODUCTIVITY** EQUATIONS - **II** _Kn(QI) s,-.r(X,,)' 4ln(L/K)eP2nMlK)ep3lnneK_

||Eq. :<br>l=<br>[matj,<br>1 <sup>,1ev</sup>1<sup>,]</sup>|Eq.2:<br>**Xi,**=[mat,, levi,.1, liqi,1]|Eq.3:<br>X1j=dtitb),,mktbtbf,,lev,,liqI,.,]|
|---|---|---|---|
|rmat2.1|.052(1.03)|.174(3.57)||
|dfitb,l|||.157(3.70)|
|mktbtb.,|||.033(0.84)|
|lev-,|-.129(-1.81)|-.224(-3.92)|-.055(-1.55)|
|liq-,||-.236(-4.92)|-.125(-3.04)|
|In K|.050(1.52)|.069(2.46)|.120(4.16)|
|In (L/K)|.127(2.70)|.127(3.36)|.202(5.60)|
|In (M/K)|.914(29.92)|.932(36.88)|.889(33.15)|
|constant|-.014(-3.32)|-.017(-3.89)|-.019(-4.34)|
|waldl(d.f)|5922.40(5)|6243.62(6)|5082.13(7)|
|wald2(d.f.)|42.99(8)|62.57(8)|68.89(9)|
|2nd order<br>serial corr.<br>(p value)|-3.07<br>(.002)|-3.22<br>(.001)|-3.11<br>(.002)|
|Sargan(d.f.)<br>|142.18(130)|171.7(156)|172.28(182)|
|**R**<sup>**2**</sup>|_ :|||
|obs.|8739|8739|8739|
|period|1982-89|1982-89|1982-89|

**Notes: (I) sh** = **a + A).,;(ii) waldl is for the slope coefficients; (iii) wald2 is for the time dummies; (iv) t statistics in parentheses are based on robust standard errors;** **_(vi)_ minimum lag length of instruments used is 3 due to the existence of second order serial correlation.**

The production function results provide no evidence to support the view that short term debt is superior. With a non-competitive market for short term debt in which criteria other than profit maximization determine the behaviour of financial intermediaries, and with an administered interest rate structure there is perhaps little reason for firms to prefer short term debt. With an inverted yield
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<!-- Start of picture text --> Correlation between sectoral productivity and change in employment share<br>> Ethiopia<br>=<br>><br>5 1970-1980 1980-1990<br>=] Bola: 17/2 Bela: 213,<br>BO) ety th ee ts<br>a. MN fire cs] tra<br>=[2] maony / (coospen<br>>z 77Gy,<br>8~1 -.05 i) .05 -A -.05 0 05<br><!-- End of picture text --> <!-- Start of picture text --> 2]<br>oO<br>a 1990-2000 2000-2010<br>Bo Faae7ew fe | Fadeloe ‘ae<br>9 Conte high f Gon Conflict high Pra<br>3 TM ya a] con<br>oe° . re fn + CupZ Ont<br>i=) o a “”Oo G Hire Onen<br>fof3s” VY= (oa<br>5 “A -.05 0 .05 = -.05 i) .05<br>os<br>z Change in employment share<br><!-- End of picture text -->
An Assessment Of Reform Options For The Public Service Pension Fund In Uganda

**Figure 37: Comparison of replacement rates* for teacher switchers– Reform II.2 with Reform I.2**

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**Notes** : * Average pension as percentage of average group wage **Source** : WB calculations based on PROST

With the similar cost to the government, the two-pillar approach may be more beneficial for lower income members through the redistributive defined benefit component and for all income groups by reducing investment risks for plan members.

# **CONCLUSIONS AND POLICY RECOMMENDATIONS**

This paper analyzes the future liabilities that the Ugandan Public Service Pensions Fund might accumulate under the provisions of CAP 286, unless it is reformed over a period of 70 years. The results for the baseline (no-reform) scenario clearly show that the current public sector scheme is not sustainable and needs reforming. The system already imposes a heavy burden on government finances and the society at large, and, if no changes are made to it in the near future, the fiscal pressure will continue to grow exponentially thus increasing the risk of system’s default on its promises.

These conclusions are, of course, very well known to Ugandan policy makers. It is less known, however, how generous the PSPF scheme is and how much it costs to the tax payer. Section IV provides a quantitative assessment of these issues. Under specific economic, demographic and recruitment assumptions discussed in APPENDIX A and agreed upon with Ugandan officials and policy makers, the system is analyzed in terms of replacement rates, government expenditure, contributions required to rebalance the scheme, and the implicit pension debt.

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