rafmacalaba/data-use-mentions-extended huggingface dataset:
of maternal and child health outcomes, the capacity of primary health care personnel, geographic location, health care budget, and physical infrastructure of facilities. In one district of Sughd region, a pre-pilot of the PBF program was implemented, and it was subsequently removed from the study sample.<sup>4</sup> The research team selected control districts in the two regions. The selection was guided by two goals: 1) selection of districts that are similar in terms of the number of facilities and doctors per capita, and 2) achieving the same number of catchment areas in each region. Two control districts were chosen in the Sughd region and seven in the Khatlon region, as can be seen in the map presented in Figure 1.<sup>5</sup> Additional districts were added to the project after 2015, but the assignment of the control districts was maintained.<sup>6</sup> The Tajikistan Demographic and Health Survey 2012 was used to test whether the parallel trends assumption can be rejected between 2008 to 2012. We created annual indicators for timely antenatal care, receiving at least four antenatal consultations and child vaccination coverage. We failed to reject the parallel trend hypothesis for all indicators. This analysis is presented in Appendix 2. In Appendix Table A2, we present baseline outcome by treatment group and tests for differences between the groups. The tests show that the PBF and control districts were overall similar before the launch of the PBF intervention.

# Data

Large-scale household and facility-based surveys were conducted before the launch of the program and after about three years of implementation. Appendix 3 provides more detailed information on the sampling and survey design. The baseline survey was conducted from November 2014 to July 2015.<sup>7</sup> Facility-based surveys were conducted in 108 rural health centers in the seven PBF district

> 4 Spitamen district was selected as a pre-pilot district. The remaining treated districts are Mastcho, J. Rasulov and Devashtich in Sughd region and Yavan, Farkhor,
There are other aspects of the data that cast further doubt on PILE, at least for Vietnam as a whole. We have seen that for rural Vietnam as a whole, living standards tend to be higher for the landless. Suppose that a change in the share of the rural population that is landless comes about without any change in the distributions of consumption within each of the two groups. In other words, a representative household among farmers is transformed into a representative household among the landless. Rising landlessness must then cause a fall in the poverty rate. To see why, note that _H_ = _H L_ > 0 + ( _H L_ = 0 − _H L_ > 0 ) _LL_ where (as before) _LL_ is the proportion who are landless. Then, under the within-group neutrality assumption, ∂ _H_ / ∂ _LL_ = _H L_ = 0 − _H L_ > 0 . This is negative for rural Vietnam in all years (Table 1) but (again) the MD is an exception; rising landlessness holding within-group distribution constant will be poverty-increasing in the MD.

We can use a difference-in-difference model to relax the within-group neutrality assumption in the above test. While the absence of household panel data spanning the period precludes use of a household fixed-effects specification, we can construct a geographic panel (within the limits of the sample sizes). In particular, we regress the log headcount index for each of the two groups (those with land and those without) for region _i_ at date _t_ , on the landlessness rate allowing for regional and time effects: where π _i_ is a regional effect,<sup>δ</sup> _t_<sup>is a time effect (such as due to macroeconomic or national agro-</sup> climatic conditions) and ε _it_ is a white-noise error term. We estimate a similar regression for the landless, with dependent variable: ln _H itL_ = 0 .<sup>46</sup>

> 46 We estimated this model using both the linear headcount index
that are not triangulated with data monitoring actual use of coping strategies. Each variable is asked separately; none are imputed. Not all variables are included in the surveys of all countries in the sample. The variables of interest that are reported for each country are provided in Annex A.

**We use both definitions to identify enterprise-owning households in the analysis** . The household business definition provides greater precision on the presence of an enterprise in the household (farm or non-farm) and income changes attributable to those enterprises. The household head definition, when limited to respondents who are self-employed, allows us to observe how trends differ by gender and age of a household head who is likely to be the owner of a household enterprise (farm or non-farm). We use a broad definition of household enterprise, which includes both a recognized household enterprise as well as self-employed service providers, namely casual workers who are not in an employer-employee relationship.<sup>13</sup>

**We also use a subset of HFPS data from 27 Sub-Saharan African countries to explore how households fared in the first year of the pandemic.** In this exercise, we are interested in how

> 11 Using the first wave of the HPFS data, heads comprise 75 percent of the respondents in the sample and 79 percent of heads are self-employed or work in a family business.

> 12 The gender composition of the sample reflects that of data reported for female self-employed household heads in the same 11 countries in the Global Monitoring Database (GMD). Specifically, women are 32 percent of the GMD sample and 27 percent of the HFPS survey. The average age is the same between surveys for women (45 years) while HFPS men are three years older than men in the GMD sample. Women’s households are 0.5 people smaller (4.6) among HFPS female heads than GMD female heads while men’s households are 0.2 people larger in the HFPS, as compared to male heads who are self-employed
We use the regression model listed below to explain the regional poverty rates: AROP� � β� �β�X� � β2country� � β3�X� �country��� β�population density� � β�GDP per capita� � ε� X represents a series of variables including different categories of the CAP funds and normalized agricultural indicators such as agricultural share of area. Population density is a proxy for the urban rural typology.

For each variable used to explain the regional poverty, margins of responses for each country are calculated to show the country‐specific strength of associations between poverty and the specific variable. Tables 2, 3, 8, and 9 show the signs of the results, whereas the zeros indicate insignificant marginal effects at 10% significance level. In Figure 8, the results were standardized by the standard errors to become better comparable between variables.

# **Appendix 3:** Analysis methods comparisons for CAP

||Book Chapter 4|Our replication|Our analysis 1|Our analysis 2|
|---|---|---|---|---|
|Data|||||
|Outcome|NUTS1/2 level<br>apportioned to<br>NUTS3 level:<br>GDP per head,<br>Unemployment rate,<br>Population change|NUTS3 level:<br>GDP per head,<br>Population change,<br>Poverty rate, Share of<br>the country poor<br>NUTS2 level:<br>Unemployment rate|NUTS3 level:<br>Poverty rate,<br>Share of the<br>country poor|NUTS1 and<br>NUTS2 level:<br>Poverty rate|
|CAP|NUTS1/2 level<br>apportioned to<br>NUTS3 level:<br>CAP support per<br>hectare of utilizable<br>agricultural area, CAP<br>support per<
representative household surveys permit proxy reporting for cost reasons, then further research is needed to assess the implications of this survey method on welfare indicators in each country.

# **9 Conclusion**

Labor market policies that address existing inequalities in access to and quality of jobs are needed more than ever, but especially during global crises (UN, 2023). As existing data reveal, a fundamental challenge when drafting such policies is the fact that measurements of female and youth labor market outcomes may be biased compared to measurements of these same outcomes for men. In this paper, we argue that part of the gap in employment and work between women and men as well as between young and older adults can be attributed to measurement bias—that is, standard survey methods might not be sensitive enough to measure these outcomes as accurately for women and youth as they do for men. This paper aims to contribute to the literature on the sensitivity of survey methods by analyzing the data of a randomized survey experiment conducted in urban and peri-urban regions in El Salvador. The study design made it possible to compare employment and work estimates, obtained from a traditional labor module embedded in a household survey, with two variations in the collection of these outcomes: the LOA module and enforced self-reporting.

In addition, this paper illustrates the effectiveness of our novel survey methods in the context of the prevalence of informal employment and gender norms. We find that the LOA module increases women’s reporting of labor outcomes relative to other women who were not exposed to this module, irrespective of the level of peers’ informal employment within their communities. On the other hand, exposure to informality can influence the effectiveness of selfreporting survey methods (compared to proxy reporting) in the sample of men. We further observe that proxy respondents in communities with high levels of youth informal employment tend to underreport the labor indicators of their young household members. Conversely, those exposed to high levels of adult informal employment tend to overreport the labor indicators of their adult household members.
11

|**Applications**|**Accounting**<br>**Approach**|**HCPM**|
|---|---|---|
|Access!<br>Interconnection|Leads to application<br>of wholesale rates<br>instead of<br>incremental cost<br>based rates.|Flexibility limited only<br>by computing time that<br>the user can tolerate.|
|Universal<br>Service|Impossible to<br>separate costs of<br>subsidized services<br>from unsubsidized<br>services.|Flexible and unbiased.|
|Price Cap<br>Review|Will understate<br>efficiency gains by<br>over-weighting<br>historical data.|Greater optimization<br>allows substitution<br>among inputs, though<br>not perfect.|
|Poverty<br>Programs|Leads to "cost-plus"<br>type arrangement for<br>low-income<br>infrastructure<br>deployment.|Can lead to balanced,<br>efficient social<br>program.|
|Unbundled<br>Network<br>Elements|Accounting data not<br>disaggregated in<br>relevant way.|Flexible and unbiased.|

# **Policy Objectives**

The new regulators of infrastructure services in developing countries have a number of underlying objectives to meet. Among these objectives are the need to set independent benchmarks for cost-based regulation. Such independence is desirable from the perspectives of avoiding the pitfalls of cost-plus reward structures and of avoiding the undue influence of any one industry group on policy decisions. Benchmarks developed using the HCPM approach avoid both pitfalls.

Another objective is maintaining control of information used in policy applications. The HCPM approach guarantees that all data input to the model are generated in a public process in which all parties are invited to participate.
<!-- Start of picture text --> Figure 9 - Which income levels have more systems to address recourse?<br>24<br>21<br>19<br>29<br>57<br>27<br>30<br>11<br>5<br>Low income Middle income High income<br>Financial ombudsman General ombudsman<br>Mediation service<br>Source: Financial Access Database<br>100<br>80<br>60<br>40<br>% of countries<br>20<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Figure 10 - How many complaints per thousand adults do countries receive?<br>Gambia 0.11<br>Botswana 0.11<br>Belgium 0.11<br>Norway 0.13<br>Kuwait 0.14<br>Malaysia 0.14<br>Armenia 0.15<br>Dominican Republic 0.17<br>Sweden 0.17<br>Chile 0.17<br>Azerbaijan 0.17<br>Puerto Rico 0.17<br>Spain 0.19<br>Slovak Republic 0.20<br>Venezuela 0.25<br>Denmark 0.29<br>Netherlands 0.50<br>Switzerland 0.50<br>New Zealand 0.56<br>France 0.57<br>Singapore 0.57<br>Israel 0.58<br>Australia 1.11<br>Colombia 1.44<br>Namibia 1.56<br>Mexico 1.76<br>Korea 1.83<br>United Kingdom 2.52<br>Portugal 2.76<br>0 1 2 3<br>Number of complaints per thousand adults<br>
Table 31. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Using U.S. O*Net database. All workers. 2011-2015

||||||No|n-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||M|anual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.000412||0.000407|0.000333|0.000478||0.000475|0.000403|-0.000488||-0.000481|-0.000370|
||(0.000134)***||(0.000134)**|*(0.000127)***|(0.000151)***||(0.000151)***|(0.000148)***|(0.000202)**||(0.000201)*|* (0.000216)*|
|Share of hhlds with computer||-0.0733|-0.0727|-0.0814||-0.0615|-0.0606|-0.0690||0.118|0.117|0.130|
|||(0.0565)|(0.0569)|(0.0573)|
the scheme. However, the numbers likely to be involved were certainly too small for a simple random sample of households to pick up enough RSE households in a cost-effective way – at most 5% of households would be likely to participate in the program. This meant we needed to know RSE status before surveying. Survey design was then complicated by the fact that approvals to recruit workers and recruitment took place on a rolling basis. For example, the first employer to recruit workers under the RSE initially contracted 20 Tongans workers in July 2007, the next employer in Tonga contracted 6 workers in August, and the next 35 in September. In Vanuatu, a grower co-operative (Seasonal Solutions) contracted 232 workers in one go, with smaller employers also recruiting at staggered intervals. Once workers were selected for recruitment, there was often only two or three weeks before they left for New Zealand, which left a very short window of time to interview them and their household for baseline, or to at least interview their household within a week or two of their

> 7 Tuvalu and Kiribati both had fewer than 100 workers per season participate in the RSE, offering insufficient sample size for rigorous impact assessment. Samoa was the other main kick-start country. A one-off ex-post survey of households was conducted there.

> 8 <u>http://www.stats.govt.nz/Census/2006CensusHomePage/QuickStats/quickstats-about-a-subject/culture-andidentity.aspx</u> (accessed August 13, 2010).

7
While different groups in different contexts are likely to benefit from different interventions, and in some sense, this is especially true for behaviorally informed policies, it is still the case that the fundamental insights, and many of the corresponding barriers, are universal. All humans care about what others think of them, even if the specific reference class (peers, religious leaders, or other categories) varies by age and culture. One implication is that most of the biases discussed, as well as the impacts of targeted programs, do not differ greatly by gender or income, for example. That said, women may be especially receptive to interventions based on aspirations (for themselves or their children), while men are especially unlikely to seek health care and may need additional support in that domain.

Finally, it is worth facing the fact that all interventions, behavioral or otherwise, involve making tradeoffs and considering opportunity costs; this is particularly true in resource- and capacity-constrained settings across much of the world. Fortunately, many behavioral ‘nudges’ can be implemented essentially for free, making them even more relevant in such contexts. This still requires the capacity to take the initiative and to know what to do, for which there are existing sources of external support (such as the World Bank’s eMBeD team) but also a growing number of internal units (see OECD <u>Interactive BI Unit Map). Behavioral</u> organizations exist at either the national or sub-national level in Brazil, Chile, Colombia, India, Lebanon, Peru, South Africa, Tanzania, Tunisia, and Zambia – among others. It should be reiterated that all these policy approaches can be viewed as complements to, not substitutes for, traditional approaches (Bryan et al. 2017). Behavioral measures are often used to build on or leverage existing frameworks, which can therefore ease their adoption from both a financial and political perspective.

# **References**

<mark>Abdou, C. M., Fingerhut,
metric is based on the premise that a person faces lower employment and income risk if she can work at home, and higher risk if she is required to be physically present in the workplace to carry out the required tasks. Recent literature on the impact of COVID-19 on labor markets highlights the feasibility of home-based work (telework) as a key determinant to overcome governments’ mobility restrictions and associated income and/or unemployment risks. The ability to telework also limits the exposure to health risks among workers that face supply shocks. Dingel and Neiman (2020) use the Occupational Information Network (O*NET) surveys to calculate the amenability of different

> 9 <u>https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020XC0330(03)</u>

> 10 This result is slightly higher than Fasani and Mazza (2020a) who estimate that 33% of workers in the EU work in essential sectors. The disparities emerge as we include the occupations related to “Agriculture, Forestry and Fishery Laborers” (ISCO-08 code 921), as well as an approximation of essential occupations at the 2-digit level for Bulgaria, Poland and Slovenia as the EU-LFS does not include 3-digit occupations for those countries.

10
# **1. Introduction**

Various reforms in the water sector—including at the river basin level—have been implemented in recent years in many countries.<sup>1</sup> This has come following the increased awareness and concerns of both policy makers and water users regarding the state of water. One of the major components of these reforms at the basin level is the decentralization of management to _the lowest appropriate level_ , as widely advocated in the Dublin Principles (ICWE 1992, p 4) and in the World Bank’s Water Resources Policy Paper (1993, p 18), and the Global Water Partnership Working Paper (GWP, 2000). The lowest appropriate level usually implies the involvement of different stakeholders in the basin, including water users, in order to achieve a more sustainable management of the basin’s water resources.<sup>2</sup> Early approaches to river basin management took an essentially scientific and engineering perspective to optimize water resources development and water allocation. Over time, while some efforts succeeded, many were “disappointing,” as Barrow (1998) concluded. This, and the growing scope of river basin management—to include, for example, ecosystem preservation—led to the call for decentralization to the lowest appropriate level as a means for harnessing local initiatives and creating coalitions for development. In fact, of the several documented reforms of river basin management, both decentralization and performance are concepts that are hard to evaluate. A review of the literature on decentralization in the water sector (Mody, 2004) suggests that not many analyses of decentralization of river basin management functions have been undertaken yet, and that this topic is also relatively recent. Recent works have focused mainly on surveys of efforts at the national level to implement Integrated Water Resources Management (GWP, 2004).

The fact that there are only a few documented works of the experiences regarding the process and impact of decentralization in river basins around the world points to a major gap in
Table 3 where we show, for example, that the estimated number of children requiring Alternative 1 (i.e. additional behavioral management intervention) with one sibling is 1.92, Alternative 2 (-2.44 (negative sign signifying a reduction in children requiring intervention)), Alternative 3 (-1.44), and Alternative 4 (-1.44).

We then applied the outcome data with the cost data described above into the enhanced CBA APP. Since the provision of PIP did not affect the RBRI score of some of the subgroups (non-statistically significant effect), no benefits could be attributed to those groups. As such, the economic benefits for these groups were not estimated (see Table 3 column 3, row 5). For other groups, however, we found positive and significant benefits (resulting in a positive avoided cost), and also negative and significant benefits (resulting in additional future treatment cost). The results presented in Table 3 show the number of children under each category (i.e. by number of siblings) multiplied by the corresponding per participant costs of the management programs to generate an estimate of the costs, benefits, and net benefits of PIP (i.e. the avoided costs of behavioral management). As discussed above, PIP was most beneficial for those PIP participants that had 1 or 2 siblings. Table 3 reveals the net benefits to PIP participants with 1 or 2 siblings of $ $18,234.39 and $74,049.29, respectively.

**Table 3. Estimated CBA results according to the no. of siblings**

|No. of<br>siblings|Costs<br>(AUD)|Benefits<br>(AUD)|Net benefit<br>(AUD)|Estimated<br>no. of<br>children<br>requiring<br>Alternative<br>1|Estimated<br>no. of<br>children<
# With a Little Help: Shocks, Agricultural Income, and Welfare in Uganda

Ruth Hill and Carolina Mejía-Mantilla<sup>1</sup> World Bank _Keywords_ : weather shocks, prices, welfare, vulnerability. JEL Classification: I31, I32, I38, Q12

> 1 Ruth Hill is a Senior Economist in the Africa Region of the Poverty and Equity Global Practice of the World Bank; Carolina Mejía-Mantilla is Economist in the Africa Region of the Poverty and Equity Global Practice of the World Bank. We thank Johanna Fajardo for excellent research assistance throughout this project, Siobhan Murray for constructing the WRSI data used in the analysis, and Alvin Etang Ndip for assistance with the price data. We thank Pablo Fajnzylber, Markus Goldstein, Obert Pimhidzai, Kathleen Beegle, Hans Hoogeveen and Luc Christiaensen for providing comments on earlier drafts. We also thank the participants of seminars held at EPRC, Kampala, UNICEF, Kampala and the Uganda Poverty Assessment writers’ workshop.
the self-reported data will help to have a better idea of the kind of reforms that are needed in India’s monitoring system. Even if firms over-report compliance in self-reporting, it still is important information about the behavior of firms regarding environmental regulations.

# **5.2 Water and Air Pollution**

Next, the paper explores whether there is a higher rate of compliance with air or water environmental regulations. This is captured by including a variable equal to one when the measures are on air emissions data and omitting the water information variable. Table 4 shows that firms seem to comply more with air emissions than with water effluents when the information is self-reported, but inspections find that firms tend to be more in compliance with water regulations.

14
, binary variables for whether the firm was formally registered when it began operations, whether the firm has a written business strategy with clear key performance indicators, whether the top manager is female, whether annual financial statements are checked and certified by an external auditor, whether the firm is a young firm (under five years old), and the years of experience of the top manager (in log).

# **A gravity model of trade combining bilateral data on trade flows and the Enterprise Surveys**

France, Spain, Italy and Germany are among the top five export destinations of Morocco and Tunisia, But Egypt, Jordan, Lebanon and the West Bank and Gaza are more exposed to countries that are 13