rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, Table 6 shows the same set of total survey implementation and technical assistance cost scenarios depicted in Table 5 but for 102, as opposed to 78, countries, which includes all 78 countries of interest plus 24 countries that are either data deprived or vulnerable to data deprivation according to Serajuddin et al. (2015). Raising the number of target countries by 30 percent, using our preferred implementation cost assignments, and assuming a uniform technical assistance provision cost of USD 540,000 per survey with an annual growth rate of 2.5 percent, the total survey implementation and technical assistance costs rise to USD 890 million and USD 334 million, respectively. These estimates are 29 percent higher than the preferred total survey implementation cost estimate of USD 692 million, and 32 percent higher than the preferred total technical assistance cost estimate of USD 253 million.

# **V. Conclusion**

While the evidence base for monitoring poverty and shared prosperity has been improving over time, challenges remain. To address these challenges, the World Bank is committed to support 78 IDA and IDA/IBRD countries as well as India to collect the multi-topic household survey data needed to estimate poverty and shared prosperity every three years between 2016 and 2030. This translates to conducting 390 surveys over a 15-year period. This paper estimates the cost of filling these gaps in household survey data to monitor the World Bank’s poverty and shared prosperity goals. Once collected based on international best practices, the resulting multi-topic data would capture not only the progress towards the Twin Goals but also the profiles of the poor, the multidimensionality of household and individual welfare, linkages between poverty and other socioeconomic outcomes and policies, and a host of SDG indicators.

Our analysis 1) uses unique country-level information on actual survey implementation and technical assistance costs, 2) is anchored in the recommendations of the World Bank Household Survey Strategy for Household Surveys regarding optimal questionnaire and sampling design, and 3) is anchored in the World Bank’s experience in supporting the NSOs in the
data from different countries. While all countries collect household data to measure poverty, there are differences in the methodology and the content included in the survey questionnaire, which contributes to the ‘total error’ of the subsequent international poverty rate obtained for each country. For example, the list of goods included in the surveys is not consistent. Additionally, the steps in the methodology used to construct the consumption aggregate vary from country to country, which then adds to the ‘total error’ of the point estimates of the international extreme poverty rate.

We examine the following three aspects of the construction of consumption aggregates to see how each of them contributes to total error: (i) sampling and survey design, (ii) spatial deflation and inter-temporal deflation; and (iii) construction of the nominal consumption aggregate. The core of the paper compares poverty rates using the national consumption aggregates, which currently form the basis for poverty measurement, to standardized consumption aggregates that attempts to adjust for differences in three aspects described above. This exercise provides an assessment of the extent to which the international poverty rates depend on methodological differences in the construction of welfare aggregates across countries.

<mark>The remaining of this paper is organized as follows. Section 2 lists the data sources and the international poverty rates for the eight South Asian countries. Section 3 explains the differences in the sampling and survey design across the surveys used to estimate poverty in these countries. Section 4 analyzes how spatial deflation accounts for cost-of-living differences and inter-temporal deflation affects international poverty estimates. Section 5 assesses the differences to estimate a national consumption aggregate across countries. Section 6 examines the extent to which the international poverty rates in each country are correlated with other non-monetary dimensions of well-being. Section 7 concludes the paper.</mark>

# **<mark>2. Data and poverty rates</mark>**

National and international poverty rates are estimated using nationally representative householdlevel surveys that collect detailed food and non-food consumption data. Table 2 shows the household surveys that
Figure C.3. Women’s Marriage Age Post the Gujarat Riots of 2002: Borusyak et al. (2021) Estimation Using Neighboring States as Control <!-- Start of picture text --> Marriage Year<br>Data Source: NFHS-4<br>1<br>.5<br>0<br>Diff-in-diff Coefficients -.5<br>-1<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV. The outcome variable is women’s age at marriage. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level. Standard errors were clustered at the state-year level.

37
we referred to destinations as “destination 1”, “destination 2”, etc., randomizing at the individual level the ordering of which destination was labelled with each number in each game. In a final game, we explicitly tested for home bias by telling participants “now the multinational corporation allows you to continue working in your home country, while also giving an option to move to a destination abroad”. We set the cost of moving to be zero, and the monthly wage to be the same in both destinations. If labelling a destination as “home” had no effect on decisions, we would expect individuals to be invariant between the two destinations, and so choices to be equally split between the two.<sup>4</sup> However, we find that 58.4 percent of the Lisbon sample and 63.8 percent of the Nairobi sample chose the home destination, which t-tests show to be significantly different from 50 percent (p=0.0357 and p<0.0001 respectively). Moreover, we find choices in this game to be strongly correlated with their professed desire to work abroad after graduating in the baseline questionnaire: 32.1 percent of those who say they want to work abroad chose the home destination, versus 87.7 percent of those who say they want to remain in Portugal (p<0.0001). Likewise, 40 percent of those who want to work abroad in the Nairobi sample chose the home destination, versus 75 percent of those who say they want to remain in Kenya (p<0.0001). This shows the importance of not using the “home” label in our main game choices, but also suggests that choices in this last game may be predictive of actual future migration decisions – something we test in section 6 using data from a follow-up survey.

# **4. Testing Classic Theories of Income Maximization and Migrant Self-Selection**

We begin by examining how classic migration theories predict migration behavior in the
We also investigate the variability of seasonal rainfall and how this differs by province. We then compare this with poverty incidence at the province level in order to get a sense of the extent of exposure. In line with results in section 5.1, growing season rainfall from November to March is highly variable both at the national and provincial levels (Figures 4).

Figure 4: Trends in total growing season rainfall (in mm) from 1982 to 2019 in Zambia <!-- Start of picture text --> 1980 1990 2000 2010 2020<br>year<br>Growing season spans Nov year t to March year t+1<br>1100<br>1000<br>900<br>Seasonal rainfall, mm<br>800<br>700<br><!-- End of picture text --> Source: Own calculations based on CHIRPS data.

Rainfall variability is more pronounced at province level, with the Western Province showing high rainfall variability on the lower end of the seasonal rainfall distribution. This is the trend with provinces in agro-ecological zones I and II experiencing much lower average seasonal rainfall than those in the high rainfall zones such as the North-Western and Northern Provinces. Droughts and floods are therefore more likely to affect households in zones I and II. This corroborates our earlier findings in section 5.1 and earlier studies suggesting that droughts are the most common climate shock in Zambia (Braimoh et al., 2018; Table 2). While it is difficult to say whether these changes in rainfall patterns lead to poverty or not, what is clear from the data is that chronic poverty—that is, households that are poor across all the three survey rounds—concentrates in parts of the Southern, Eastern, Muchinga, and NorthWestern Provinces (Figure 3). Except for the Southern Province, these also have the highest proportions of vulnerable households (see Figure 1 and Table S4).

# 5.3. Climate shock impacts on vulnerability and resilience
In addition, as RCTs as a method mature, researchers have turned their attention to consolidating and systematizing the RCT-based evidence, as well as expanding and improving experimental methodology and econometric analysis methods. In short, many emerging areas of secondary research could benefit from improved access, systematic cataloging, and harmonized documentation of RCT data. Here we provide a brief overview of research that reuses RCT data and informed the development of the schema.

_Combining evidence._ Meta-analysis techniques such as Bayesian hierarchical models (BHM) can increase external validity and generalizability of experimental results. Recent examples include Bandiera et al. (2021), who combine 16 laboratory and field experiments to estimate the impact of performance pay on women, and Meager (2019), who estimates the impact of micro credit on income-generating activities and consumption. When available data is not catalogued, meta-studies run the risk of overlooking less prominent studies.<sup>4</sup> _Assessing estimation and prediction methods._ In seminal work, LaLonde (1986) used experiments as a benchmark to assess the bias in non-experimental estimation methods. The literature that followed (Fraker and Maynard, 1987; Dehejia and Wahba, 1999, 2002; Glazerman et al., 2003, and many more) has matured to the point of being able to draw conclusions about the full distribution of bias (e.g Chaplin et al., 2018). Researchers have also used experimental data for making out-of-sample predictions and evaluating external validity, e.g. by comparing different prediction methods or quantifying site selection bias (e.g. Hotz et al., 2005; Allcott, 2015; Gechter et al., 2019).

_Measurement._ Researchers use existing data to validate methods of measurement for important concept and indicators. Recent work has for example examined sources of measurement error in agricultural data (e.g. Beegle et al., 2012; Rosenzweig and Udry, 2019) and measurement methods for women’s agency (e.g. Donald et al.
concentration curves. We consistently found that the between-group component is smaller for health inequality than consumption inequality. We also found that the proportion of betweengroup inequality in the country is small in comparison with the proportion of between-country inequality in the world. While this application is purely descriptive, it is useful for elucidating the significance of the geographic information in explaining overall inequality. The magnitude of the ratio of between-location health inequality to the overall health inequality was not previously known.

Finally, we evaluated the efficiency gains from geographic targeting. We showed that concentration curves are closely linked to efficiency measures of targeting. We proposed the _adjusted_ concentration curve to allow for the variable transfer across the individuals. We found that commune-level targeting based on the estimates we produced is on average at least two to three times more efficient than stratum-level targeting. This is the best we can do with the CDHS estimates. The budgetary gains for commune-level targeting can be five times as high as those for stratum-level targeting in the short run.

All three of these applications illustrate interesting research questions which can now be answered with the commune-level estimates we derived. Successful application of our methodology in other countries is likely to help improve the efficiency of targeting in these countries. It will also help us to understand the relationship between consumption inequality and health, and inequality in health outcomes.

# **References**

- Alderman, H. (2000) ‘Anthropometry.’ In _Designing Household Survey Questionnaires for Developing Countries: Lessons from Ten Years of LSMS Experience,_ ed. M. Grosh and P. Glewwe (Oxford University Press) pp. 251–272

- Alderman, H., M. Babita, G. Demombynes, N. Makhatha, and B. Ozler<sup> ̈</sup> (2002) ‘How small can you go? combining census and survey data for mapping poverty in South Africa.’ _Journal of African Economies_ 11, 169–200

38
a measure that explicitly focuses on public investment quality or efficiency. To the best of our knowledge, the only index that specifically focuses on public investment quality is the Public Investment Management Index (PIMI) developed by Dabla-Norris et al. (2012).<sup>3</sup> While PIMI is useful for cross-country comparisons and has been extensively used in the literature, its construction required a herculean effort in terms of data collection and relied on qualitative information partly based on “in-country discussion with country officials, review of published and unpublished material, and discussions with relevant stakeholders” (Dabla Norris et al. 2012, p. 244). Thus, the PIMI index is hard to replicate and costly to update.

Consequently, the index is only available for a subset of countries (71 economies) and has not been updated since the original data collection period in 2007-2010. Another potential drawback of the PIMI is its emphasis on public investment procedures rather than outcomes. It is thus a _de jure_ index of public investment quality rather than a _de facto_ index.

In this paper we address these issues by developing a novel index of public investment quality (PIQ) that measures _de facto_ aggregate country-level efficiency of public investment. The index is based on publicly available World Bank data on investment project performance evaluations. Rather than being based on public investment procedures, our index focuses on outcomes as it is based on impartial evaluations of specific investment projects conducted by experts in a consistent appraisal framework. As PIQ is constructed using publicly available data, it can be easily replicated and updated.

Our index covers 120 EMDEs over 2000-2021. It shows significant variation in public investment quality across EMDEs, with lower values of the index in commodity-exporting and low-income countries (LICs). We also find that the gap in public investment quality between low-income and higher-income countries, as well as between commodity-exporting and commodity-importing EMDEs, has widened over time.

We use the PIQ index to test whether public investment quality affects the relationship between public investment and sovereign risk
Page 4 Bank (EIB), and the Council of Europe Bank. The reforms that remain on the agenda for the education sector involve giving greater means for stakeholders in local schools to hold the government and providers accountable for the quality of education.

10. With the support of the Governance Partnership Facility (GPF) a special survey was designed to examine how parents, community members and teachers interact in school governance structures. The _Albania School Stakeholder Survey and Lab-type Experiment_ was designed and deployed in 180 communities drawn randomly to represent the network of basic (primary) schools in Albania. Along with the survey, a set of controlled lab-type experiments (or “behavioral games”) was also conducted with parents and teachers in order to measure their demand for accountability.

11. This paper presents the findings of the survey and lab-type experiments, and is structured into three parts. The remainder of Part I summarizes the findings from analysis of the survey and lab-type experiments. Parts II and III provide greater technical details for interested readers who are statistically inclined. Appendicies explain the design and sampling strategy used to select schools, parents and other stakeholders for the survey and lab-type experiments, as well as more detailed data on school performance dissagregated by RED.

# **I.c. Summary of Findings from the School Stakeholder Survey and Lab-type Experiments**

12. The School Stakeholder Survey, collected in the autumn of 2009, was designed to provide policy makers with data on the functioning of schools and to inform the design of further reforms in the education sector. The survey was designed to: (i) quantify education resources and take stock of institutions for accountability, including both external (“top down”) and participatory (“bottom up”) structures; (ii) measure stakeholders‟ awareness of the schools‟ institutional framework in general, and participatory accountability institutions in particular; and (iii) gage actual parental involvement with the school. In what follows, we present descriptive statistics drawn from the survey to measure -or
|-0.0050|-0.0052|-0.0040|-0.0050|
|||(1.8)|(2.2)|(1.3)|(1.8)|(1.9)|(2.1)|(1.5)|(1.9)|
|dbuy x dmonth|_νj, δm, _<br>_τjm, _|yes|yes|yes|yes|yes|yes|yes|yes|
|dsel x dmonth<br>|_μk,λkm_|yes|yes|yes|yes|yes|yes|yes|yes|
|adjusted R<sup>2</sup>||0.3335|0.3652|0.3510|0.3618|0.3886|0.4044|0.3986|0.3868|
|number of<br>observations||766|802|639|649|1090|1131|895|867|

The table reports transaction cost estimations on the basis of district maize prices from June 1999 to October 2009. Estimation is by OLS (district fixed-effect estimations are available from the author on request). The transaction cost and fuel price are deflated with the Malawi consumer price index. The sample is restricted to observations below a maximum distance. Absolute _t_ -statistics are given in parentheses (.) below the coefficient. We do not report the coefficients and t-values of the constant term and dummy variables. Columns reflect different assumptions with respect to the minimum per person per day calorie intake (either 2200 or 2300 kcal), excess supply over requirements before districts start to export (either two- or four-month district requirements; see also _φ_ in equation (2a)), and maximum distance, the distance beyond which trade is assumed to be unlikely (either 200 or 300 km).

The transaction cost equations are estimated by OLS. Trade pair fixed-effect estimations (
# **(1) Introduction**

Economic liberalization in the 1980s and 1990s resulted in increasing inequality in many developing countries (World Development Report 2006). Intra-country spatial inequality also increased in most cases, with an increasing gap in living standards between rural and urban households (Kanbur and Venables (2005)).<sup>2</sup> Using Demographic and Health Survey (DHS) data on 65 countries, Young (2013) finds that rural-urban inequality accounts for 40 percent of the mean country inequality and much of the cross-country variation. To what extent the observed inequality is due to the roots of intergenerational persistence in economic status is an important question for the policymakers. It is thus surprising that there is little research on the rural-urban differences in intergenerational mobility in developing countries. Most of the existing studies focus exclusively on urban areas, and a comparative analysis of rural vs. urban areas remains relatively unexplored.<sup>3</sup> We provide a theoretically grounded empirical analysis of the rural-urban divide in intergenerational educational mobility in Indonesia in the 1990s and 2000s.<sup>4</sup> Inequality in Indonesia increased dramatically over this period: the consumption Gini rose from 29.2 in the 1990s to 38.9 in the 2000s. There are important rural-urban differences: inequality is lower in the rural areas, and the rise in inequality from the 1990s to 2000s has also been less pronounced compared to the urban areas (Kanbur and Jhuang (2014)). A link between higher inequality and lower intergenerational mobility has been the focus of a growing literature on the Great Gatsby Curve (Corak (2013), Fan et al. (2020), Neidhofer et al. (2018)). Does the _higher_ level of inequality in the urban areas imply that there is a “rural bias” where urban children face _lower_ educational (and economic) mobility compared to

> 2In Indonesia, the rural-urban gap in per capita monthly household expenditure increased from 12,991 rupiahs in 1984
|||**Ov**|**erall**|||**Firms**|||**Individua**|**ls**|
|---|---|---|---|---|---|---|---|---|---|---|
|Conditioningvariable|Tot obs|ρ (low)|ρ (high)|p-value|ρ (low)<br>|ρ (high)|p-value|ρ (low)|ρ (high)|p-value|
|**Fiscal freedom**|155|-0.508|-0.118|**0.007**|-0.025|0.024|0.767|-0.570|<br>-0.206|**0.008**|
|**Investment freedom**|154|-0.049|-0.436|**0.011**|-0.033|0.056|0.587|-0.052|<br>-0.513|**0.002**|
|Financial freedom|151|-0.141|-0.362|0.153|-0.092|0.176|0.104|-0.174|<br>-0.450|**0.065**|
|Strength of investor protection index (0 to 10)|156|-0.314|-0.345|0.831|-0.077|0.040|0.474|-0.338|<br>-0.471|0.337|
|Strength of insolvency framework index (0-16)|145|-0.173|-0.337
# (iii)

# Summary

One concern raised in the discussion over regional trade arrangements (RTAs) is whether the discriminatory trade barriers applied in these arrangements encourage high cost imports from member countries at the expense of lower cost goods from non-members. However, evaluations of the actual impact of RTAs have been hampered by a lack of appropriate empirical procedures for assessing their influence on the level and direction of trade.

This study employs a new index for analyzing one aspect of an RTA, - its static trade effects. It examines changes in the regional "orientation" of exports, and shows how this information can be employed in connection with the "revealed" comparative advantage (RCA) index to identify apparent "inefficiencies" in trade patterns. The approach is applied to statistics on Mercosur countries' exports to determine if recent trade is evolving along lines compatible with these countries' comparative advantage. The paper does not comment on the many other possible effects of RTAs such as benefits from political cooperation, enhancing the credibility of reform strategies, or dynamic gains from trade. Nor does it focus directly on changes in trade with non-members which accelerated rapidly due to the 1988-91 liberalization of Mercosur countries trade barriers. Thus, the paper does not address the net welfare effect of trade creation and trade diversion relative to where member countries' trade policies started from in 1988.

The results show the most dynamic (fast growing) products in Mercosur's intra-trade generally are capital intensive goods in which members have not displayed a strong export performance in outside markets. Neither the RCA indices, nor statistics relating to factor proportions, indicate that Mercosur has a comparative advantage in these products. What factors are causing these trade changes? The evidence suggests that Mercosur's own trade barriers are responsible. Most favored nation tariffs on the fast growing products are above the average for all imports and provide Mercosur members with significant preferences. These may also be supplemented by a higher than average incidence of nontariff measures on imports from the rest of the world.

These findings