rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 11. Econometrically Predicted Changes in Self-Assessed Food Insecurity over 2006–** 2 **008 (Millions of People)**

||Predicted change in food insecurity|Predicted change in food insecurity|
|---|---|---|
||using regression 2 in table 7|using regression 4 in table 7|
|Total predicted change in|||
|self-reported food insecurity|−104.5|−152.8|
|Change due to|||
|economic growth|79.4|158.8|
|Change due to total|||
|inflation|−25.1|6.0|

_Source_ : Author’s calculations based on the GWP (Gallup 2011) _Note_ : Changes in self-reported food insecurity are estimated by multiplying the coefficients from table 7 by changes in GDP per capita and changes in the total CPI from 2006 to 2008.

## **<<A>>V. CONCLUSIONS**

The innovation of this study is its use of survey-based evidence, rather than simulations, to assess the impact of the food crisis on global and regional food insecurity. We find no evidence that global food insecurity was higher in 2008 than it was in previous years, although it affected many regions, particularly a number of countries in Africa and Latin America. Though qualified by measurement issues, these results are broadly corroborated by recent World Bank estimates of a declining global poverty trend over 2005–08. Our results also cast doubt on the usefulness of simulation approaches in predicting global poverty trends, although the more sophisticated of these approaches are still useful for exploring the mechanisms and distributional impacts of food price impacts in an experimental setting.

Finally, our results raise the question of whether self-assessed indicators might be a useful addition to existing food security metrics. Further research is needed in this regard. As we noted in section 2, self-assessed indicators are susceptible to a number of biases. Nevertheless, these 36
The general findings from the national time-use surveys are also confirmed in smaller case studies: both women and men are involved in fuelwood collection but to varying degrees (Cooke et al, 2008). One of these studies, Köhlin (1998) shows that the labor allocation between men and women, and between sources of fuel, is responsive to the relative shadow wages. Although cultural norms are important for intra-household labor allocation, as indicated by e.g. significant caste dummies in choice of fuel source, it is apparent that the opportunity cost of women‘s time is also important.

Below, we take up the more interesting question of how the relative, and absolute, burden for women changes with changing circumstances, and especially donor-funded interventions.

# **_1.2.3 Technology uptake and electricity connections_**

# **The impact of interventions designed to promote the use of new cooking technology and electricity in homes depends on household decision-making, and in particular, how women’s preferences, opportunity cost of time, and welfare are reflected in those decisions.  Good project design requires context-specific understanding of how these household decisions are made.**

The ultimate impact of many interventions in the energy sector are mediated by household decisions about (i) the type of cooking fuel and technology to employ, and (ii) whether and how to use electricity in the home.<sup>9</sup> This raises the question of whether and how women‘s preferences and welfare are reflected in these household level decisions. It is commonly argued that because women have lower status and lower perceived opportunity cost of time, their preferences and their welfare carry less weight in these household decisions. For example, male household heads who control cash accounts may decide which appliances to buy, without considering the preferences or well-being of the women in the household. Social norms that place a low value on women are often internalized so that, even if women are the decision makers, they afford low priority to new
to academic knowledge. However, we note that the results presented below should be interpreted as the reduced form impacts of PAL transfers, as they combine both the direct and the indirect effect of the program. In particular, there are likely dynamic complementarities among inputs (Heckman and Cunha, 2007). For example, children of PAL recipients might attend better (and more expensive) schools (a higher _Qi_ 2) because transfers increased the family budget, or, they may attend better schools because the returns to school quality are increasing in child health investments ( _HIi_ 1).

# **5.1 Health stock and health inputs**

We use information from the evaluation follow-up survey in order to study whether PAL, either in the in-kind or cash modality, had an effect on proxies for health status and parental health investment of children aged 0 to 6 at the baseline. When looking at the health stock we focus on four indicators: 1) the caregiver reported probability that the child was sick in the four weeks prior to the interview; 2) the child height per age, expressed as a z-score; 3) the child weight per age, expressed as a z-score; 4) whether the child was anemic or not, based on the analysis of a blood sample.<sup>19</sup> The impacts of PAL on these variables are reported in Table 8. For each outcome, we present results for two specifications. In the first one, we present our baseline specification for the whole sample (column 1, 3, 5, 7), in the second one we restrict the sample to those children for which we observe at least one ENLACE test score during the time period between 2007 and 2013 (columns 2, 4, 6, 8). For the probability of being sick, height per age and weight per age, we find effects that are close to zero, both for the in kind and the cash modality, irrespective of whether we consider the full sample
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The state of the CAR’s infrastructure and the price of fuel are highly correlated. After a notable decline in the retail price of diesel and super gasoline during the mid-1990s, these prices have been steadily increasing. In 1998 the price of diesel, at 64 cents per liter, reached its lowest point between 1991 and 2010. The retail price in 2010 was 169 cents per liter—an increase of about 260 percent (figure 4a). Meanwhile, diesel prices in the CAR are the highest in Central Africa (figure 4b). This is due to the high cost—in both money and time—incurred by importing oil from Cameroon (via the Doula-Bangui corridor) and the Democratic Republic of Congo (via the Oubangui River). Higher oil prices raise transportation costs and impact the cost of thermal power production. High power generation costs in turn scale up the price of energy, which can only be afforded by the highest quintiles of the population. Scarcity of fuel is also responsible for constant power outages as thermal production is curtailed, which in turn affects water treatment.

**Figure 4. Diesel and super gasoline retail prices**

<!-- Start of picture text --> a. The CAR’s diesel and super gasoline retail prices b. Central African countries diesel retail prices<br>180 180<br>160 160<br>140 140<br>120 120<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0<br>0<br>Diesel Super Gasoline<br>US cents per liter US cents per liter<br><!-- End of picture text --> _Source:_ GTZ 2008. _Note:_ Diesel retail prices for Central African countries as of mid-November 2008.

The CAR’s infrastructure network would benefit from closer interconnection and collaboration with neighboring countries. The
reforms associated with trade facilitation. But uniform application of multilateral agreements is desirable in order to achieve the ultimate goal – removal of trade facilitation barriers worldwide.

**_Customs Modernization—International and Russian Experience._** In chapter 28, Luc de Wulf summarizes the results for the large study he led for the World Bank in best practices in customs modernization. In chapter 29, Carlos Ferreira summarizes the World Bank-Government of Russia project on customs modernization in Russia. Surveys of traders in Russia have shown that the number of complaints about the Russian customs service has declined subsequent to implementation of this project.

# **Standards and Technical Regulation**

**_International Experience._** In chapter 30, Spencer Henson notes that with the decline in traditional barriers to trade, attention has focused on the potential role of standards as technical barriers to trade.  At the same time, consumers in both industrialized and developing countries are increasingly demanding safer, higher quality and more consistent products that require that systems of standardization are put in place. The question for the world trading system is how to provide for the legitimate rights of a country to use standards to provide for the quality and safety of products without standards becoming a technical barrier to trade. The World Trade Organization (WTO) through the Agreements on Sanitary and Phytosanitary (SPS) measures and on Technical Barriers to Trade (TBT) has played a significant role in this respect. Hensen notes that Members are required to provide scientific evidence when applying SPS measures that differ from international standards.  Further, Members are obliged to achieve consistency in the application of SPS measures, to avoid arbitrary or unjustifiable distinctions in the levels of protection it considers to be appropriate if the distinctions would act to distort trade. Nonetheless, there are now a number of documented cases where developing countries have faced restrictions because of their inability to meet mandatory standards, and lost markets due to their inability to meet voluntary standards.  At the same time, other countries have managed to gain access to high-value markets
to the public good is smaller than the community as a whole and is also a group that has a high level of existing social capital. Finally, the existence of leaders and coalitions can help to increase the provision of the public good, although this also depends on the returns to members of the coalition, and the likelihood of free-riding by non-members (Moxnes and van der Heijden, 2003; Gächter and Renner, 2018; Bosettia et al., 2017). The CBOs in our context can play a leadership role within the community where their actions are highly observable by other community members, potentially shifting beliefs about the equilibrium and thereby encouraging contributions by non-members. We contribute to this literature by providing evidence from an RCT that these types of mechanisms can be effective at overcoming the public goods problem in a denselypopulated urban setting.

Our paper also contributes to the body of literature exploring the role of CBOs in the implementation of development programs, in particular in relation to public goods provision. The 2004 World Development Report (World Bank, 2004) highlighted the need for community-level engagement to solve collective action problems. In general, the evidence on the effectiveness of community-based approaches in the provision and maintenance of public goods is mixed. There are some examples of such approaches having a positive effect on access to water and sanitation (Beath et al., 2013; Guiteras et al., 2015), but evidence on projects that aim to address the maintenance of public infrastructure is scarce. As highlighted by Mansuri and Rao (2004), the success of such projects will depend on the specific context, the time horizon, and the design of the community-based scheme. Moreover, recent evidence from randomized control trials of community-based interventions suggests that the mechanisms for effective collective action will also depend on the context and type of public good in question. Support for grassroots community-level engagement as opposed to involving local authorities is provided by Sheely (2013) for the case of anti-littering in Kenya. In contrast, Jack and

> 3 One exception is
to perform well in a wide variety of situations. The Bottolo-Richardson and horseshoe- _n_ priors are consistent (albeit at a slow rate) and do well on real data, but underperform on the simulated data. The truncation of the Forte _et al._ . prior makes it hard to recommend for situations where _n_ is appreciably larger than _k_ . The benchmark Beta prior with _c_ = 1 does not fare well in prediction for one application, and the benchmark priors with _c_ = 1 and _c_ = 0 _._ 1 do not do well in terms of Bayes factors. The Zellner-Siow and Maruyama-George priors perform quite well, except for the last application (with large _n_ ), where they get very little support from the data. Nevertheless, we feel they do deserve a place in the econometrician’s toolbox, especially if _n_ is relatively small (comparable to _k_ , say). In addition, if one wants to avoid numerical methods to compute Bayes factors between models with different sets of regressors, the Maruyama-George prior should be recommended, although this comes at the (small) cost of making the prior model-specific, which may make it slightly harder to interpret the prior on _g_ .

In our view, the two priors that stand out by not having displayed any truly bad behaviour in our experiments are the benchmark Beta prior with _c_ = 0 _._ 01 and the hyper- _g/n_ prior (with _a_ = 3).<sup>18</sup> Thus, these priors provide an interesting compromise and would be our general recommendations to practitioners.

> 17 Using a much larger value of _g_ 0 ( _g_ 0 _>_ 40) it would be possible, in principle, to compute a Bayes factor, but we can safely assume the Tobias-Li data do not support this prior at all, as it puts no mass whatsoever on areas with large values for the marginal likelihood _ly_ ( _g_ ).

> 18 For the hyper- _g_ prior we find (as do Feldkircher and
such adjustments in the sphere of political economy, what is the least distorting way of establishing higher relative prices ? Is the big-bang approach of large onetime increases more desirable than continuos gradual adjustments that take longer to achieve the same effect ? The results of the model estimated in Section 5 suggested that gradual adjustment is clearly preferred on the grounds that it does not contribute to wide and skewed relative price

> 38 Real appreciation has been indeed taking place systematically during the entire period 1989-1997, in particular since 1994 (Pujol and Griffiths' data) up to date.

37 The utilities index equals 73.6 % in Slovenia, 35% in Hungary, 29.6% in the Czech Rep. and 10.9% in Slovakia
the business-as-usual survey practice of interviewing a single, most-knowledgeable household member to identify asset owners and values within the household.

Overall, a key contribution of this paper is highlighting the importance of disaggregated data collection — across individuals and asset classes — as well as carefully considering respondent selection in understanding and targeting economic inequalities. Data on asset values are also not without their own measurement issues, including missing responses and variation in reported values among joint owners. In the analysis, we construct different specifications of individual-level wealth to account for these sources of measurement error and continue to find that our results are robust to these sensitivity checks. The consistency and significance of our findings across countries point to the need to broaden the scope of surveys and methodological research to better understand longer-run inequalities among men and women stemming from differences in personal asset ownership and wealth.

The paper is structured as follows. Section 2 discusses the LSMS+ methodology and survey data. Section 3 provides descriptive statistics on within- and across-household variation in asset ownership and valuation. Section 4 discusses the findings on inequality measurement using the individual-level data on wealth, as well as implications for how self- versus proxy reporting affects estimates of inequality. Section 5 concludes.

# 2.  LSMS+ Methodology and Survey Data

## 2.1 Background

Since 2016, and as part of the 18<sup>th</sup> replenishment of the International Development Association (IDA18) that committed to launch pilot data collection in at least six IDA countries to “gather direct respondent, 5
this component of RTP3 to have large effects on the labor market or income outcomes of participating households.

Finally, RTP3 followed the decentralized structure of the Government of Vietnam. Although the project was administered and supervised centrally, implementation was carried out by provincial and local government bodies. For instance, while the selection criteria for roads was developed centrally, provincial Departments of Transport (DoTs) were responsible for submitting lists of eligible roads. Provincial DoTs, in turn, invited local government bodies to apply for the project before shortlisting roads based on the eligibility criteria.

# 3. Data

As previously mentioned, we relied on a mixed methods analysis to provide evidence on whether men and women benefit equally from rural road improvements, and to explore reasons why they do or do not. Specifically, we used quantitative data to rigorously evaluate the gender-specific impacts of rural road improvements. Then, we collected qualitative data to understand the mechanisms behind these impacts. For the quantitative analysis, we combined administrative data on the rollout of the RTP3 activities with existing household-level data. We describe all three types of data in greater detail below.

## _Household data_

For household data, we turned to the Vietnam Access to Resources Household Survey (VARHS) implemented by UNU-WIDER in collaboration with the Central Institute for Economic Management (CIEM). The VARHS consists of two questionnaires—a commune and a household questionnaire. The former covers information on the commune, recent economic shocks, infrastructure, access to services, and development programs at the commune level. The household questionnaire, which we used in our analysis, includes modules on household members, production and employment, use of inputs, income, and measures of social and political connectedness.

The VARHS was implemented in 2002, 2006, and every two years since 2008 in 12 provinces in Vietnam. From 2008, the survey included a panel component. We used data from the 2008, 2010, 2012, and 2014 rounds of the survey. As per this data, 99.5 percent of RTP3 road improvements
.

The effect on take-up is mainly driven by relatively light touch services, meaning that firms either went to a SEBRAE-PR service center and received materials on best business practices, or accessed material through SEBRAE’s website. Receiving the information sheet increased participation in group training sessions or one-on-one consulting only for firms who implemented below the median number of business practices at baseline, possibly because these firms realized they needed more hands-on help with improving their business practices.

Given the relatively small absolute effect on take-up in the short-run (7 percentage points) and the lack of a medium-run effect, we do not expect the information sheet to affect business performance through take-up of SEBRAE services. However, we implemented a follow-up survey about one year after the intervention to check whether firms obtained business training from other sources or whether they improved business practices on their own. We do not find any effects on either the use of services from other sources or on a business practices index. Accordingly, the follow-up survey data shows no 3
Data Source (0.12), and Data Infrastructure (0.3), and a relatively high Data Services (0.56) and Data Products (0.61) components. Senegal is in the middle range in terms of the SPI distribution and CV spectrum (SPI = 0.56), with uneven performance of individual components. The components of SPI increase from a low of 0.4 for Data Infrastructure and Data Sources components to 0.67 for Data Products, and 0.81 for Data Services.

That pattern of variance of four SPI components, or pillars, may be explained by weak donor coordination in statistical capacity building and donors’ preferences for certain areas of the NSS. It is also likely that resource-constrained countries cannot effectively allocate resources to cover all dimensions of the SPI. The high variance in SPI components among low-capacity countries hinders their progress because components of SPI complement one another in the production and dissemination of statistical information. For example, a weak capacity to collect survey and census data adversely affects the presence of key indicators in international databases.

Figure 3 reinforces the argument that donor preferences could be driving capacity improvements in low-performing countries. Data Infrastructure and Data Products are the largest contributors to the overall SPI scores for this group; until recently, they were donors’ preferred statistical tool, as they contribute to understanding social rather than economic performance. Low scores on Data Services and Data Sources, which are more developed for economic than social statistics, reinforce this hypothesis.

# **4. Assessing the Demand for Data**

We assume that in the long run, a country’s statistical capacity is determined by the interaction between the supply of and demand for data. If the demand for official statistics exceeds the supply, data users will pressure the government to increase investment in the NSS. If the NSS supplies more data than are consumed, the government will eventually reallocate resources to more urgent priorities. The steady-state equilibrium of the statical capacity is reached when the demand for
, plus expenditures on health and education; and assets poverty, defined as the inability to acquire the latter plus expenditures on clothing, housing, and transportation. The municipalities’ inequality levels were computed through an array of well-known indicators such as the Gini coefficient.

This exercise yielded a robust, novel municipality-level dataset with income-based indicators that are comparable both over time and across 2,361 municipalities on which it was possible to compute reliable estimates on each data point over time. These municipalities represent 96 percent of Mexico’s current municipalities and cover approximately 98 percent of the country’s population. Summary statistics derived from

> 7Some of these variables are derived from the census datasets, while others are taken from administrative records collected in the National System of Municipal Information (SNIM for its acronym in Spanish) and the National Institute of Statistics and Geography (INEGI, for its acronym in Spanish).

> 8A detailed description of the small area estimation methodology used on each data point is available from the authors upon request.

9