rafmacalaba/data-use-mentions-extended huggingface dataset:
information that emphasizes community norms favoring vaccine uptake (Palm et al., 2021).

Observational studies provide good grounds for anticipating information on vaccine safety and disease risk to be effective in reducing vaccine hesitancy. In line with the findings from developing countries on attitudes to COVID-19 vaccines outlined above, a common finding in much work on attitudes to vaccinations is that people’s reluctance to be vaccinated stems in part from fear of vaccines or their perceived side effects (Brewer et al., 2017; Kang et al., 2017; Karlsson et al., 2021). Similarly, a common finding in observational studies is that when people perceive an illness to be more threatening, and the vaccine efficacious in reducing that threat, they are more willing to be vaccinated (Karlsson et al., 2021; Palm et al., 2021).

Although observational studies provide evidence that information on the relative risk of vaccines and diseases will increase people’s willingness to be vaccinated, experimental studies have returned mixed results. Positive treatment effects have been found in some studies (for example, Palm et al., 2021), other experiments have failed to find clear evidence of the efficacy of information treatments (Brewer et al., 2017; Dubé et al., 2015), while some studies have found adverse effects, at least in certain groups (Nyhan and Reifler, 2015; Nyhan et al., 2014).

Broader non-vaccine-related work in social science suggests that beliefs about social norms, and in particular beliefs about what most other people are doing, will be an effective means of changing vaccine attitudes. Norms have been found to shape human actions and interactions in many ways (Tankard and Paluck, 2016). And there is some evidence from earlier vaccine work suggesting that norms are important in influencing views, although their potential use as a tool for increasing vaccine uptake remains a comparatively new research area (Brewer et al., 2017; Dubé et al., 2015). One of the few currently
**H1:** _Forcibly displaced women and girls are deprived more often than nondisplaced women and girls._

**H2:** _Forcibly displaced women and girls are deprived and MPI poor more often than non-displaced women and girls._

The second set of hypotheses concern gender gaps:

**H3:** _Gender gaps between men and women are larger for forcibly displaced persons than non-displaced persons._

**H4:** _Gender gaps between boys and girls are larger for forcibly displaced children than non-displaced children._

The final hypothesis examines intrahousehold inequality by displacement status:

**H5:** _Intrahousehold inequalities will be more common for forcibly displaced individuals than non-displaced individuals._

# **5 Findings**

We present the key multidimensional poverty statistics, individual-level deprivations, and intrahousehold inequalities of the MPI poor for forcibly displaced populations and host communities in turn. The final section presents the relationships among gender, displacement, and multidimensional poverty for intrahousehold inequalities.

# **_5.1 Headline MPI results_**

Admasu et al. (2021) found that displaced households were on average MPI poorer in the same set of countries, with large variation in the poverty gaps between the displaced and non-displaced populations. We take these overall findings a step further by zooming into the individual deprivations experienced by men and women, and boys and girls, living in displaced and non-displaced households.

The MPI headcount and intensity by displacement status, by gender, are presented in Table 5. Note that the individual’s poverty status is still determined by the household, as the MPI is a household-level measure. For example, an incidence of 40.1% for male IDPs 14
**Similarly, studies on internal migration in Mexico have mainly focused on rural-urban mobility.** Considering land ownership as a push factor that affects interstate migration in Mexico, King (1978) evaluates migration flows for men and women. Furthermore, Van Wey (2005) studies the effect of land ownership on internal and international out-migration in Mexico following a discrete-time event history analysis. The author finds heterogeneity in the effects of landholding in rural areas, with a negative impact on out-migration for smaller landholders and a positive impact for large landholders.

**Varela Llamas, Ocegueda Hernandez, and Castillo Ponce (2017) analyze the determinants of interstate migration in Mexico from a micro-perspective.** Using data from the National Labor Force Survey (ENOE), the authors find that the probability of migrating for labor-related reasons increases with the education level of the migrant. This study contributes to the literature of interstate migration in Mexico by analyzing a broader set of both macroeconomic and individual factors affecting the propensity to migrate and where to migrate.

**The studies focused on gender differences in migration patterns in developing countries are limited.** Chort, De Vreyer, and Zuber (2018) find evidence of significant gender differences in internal migration patterns in Senegal, with women being more likely to migrate than men. This pattern may be associated with poorer economies' specific characteristics, which also make women more likely to migrate to rural areas, depending on their education. Recent studies on internal migration in Mexico find the opposite pattern, with men being more likely to migrate internally than women (Varela Llamas et al., 2017). Donato and Kanaiaupini (2000) show that women’s labor force participation and migration are linked to regional employment opportunities for women and family wealth conditions. Moreover, King (2011) shows that marital status is an important factor determining gender differences in internal migration in Mexico (contrary to the trend of labor mobility reason among men).

**Last, this study also relates to the literature on returns
growing debt and economic imbalances, which has contributed to the state’s inability to respond to growing demands and dissatisfaction from citizens (Blas 2023).

**Figure 1. Political stability and economic development in Sub-Saharan Africa (2021)**

<!-- Start of picture text --> 4.6<br>Seychelles<br>South AfricaEquatorial Guinea Mauritius 4.4<br>Gabon Namibia Botswana 4.2<br>Eswatini Cabo Verde 4<br>Nigeria Cote d'IvoireAngola<br>Sudan Cameroon Kenya Mauritania Ghana 3.8<br>Zambia<br>Mali Ethiopia Burkina Faso Chad ZimbabweGuineaUgandaCongo, Rep.MadagascarTogo TanzaniaBeninComorosSenegalLesotho Rwanda Sao Tome and Principe 3.63.4<br>Somalia South Sudan Niger Eritrea Guinea-BissauSierra LeoneMalawiGambia, The 3.2<br>Congo, Dem. Rep. Burundi Mozambique Liberia 3<br>Central African Republic<br>2.8<br>-3.00 -2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00<br>Political Stability<br>Note: The x-axis represents the World Governance Indicator on political stability. Oil rich economies are represented in yellow; non-oil but<br>resource-rich economies in green; non-resource rich countries in blue. The data is the latest available (2021). Source: WGI database for political<br>stability indicator and WDI database for database for GDP per capita.<br>Figure 2. Rule of law and economic development in Sub-Saharan Africa (2021)<br>4.6<br>Seychelles<br>Mauritius 4.4<br>Equatorial Guinea Gabon South Africa Botswana 4.2<br>Eswatini Namibia 4<br>Angola Cote d'Ivoire Ghana Cabo Verde 3.8<br>Nigeria Mauritania Kenya<br>ComorosGuinea-BissauSudan ZimbabweCameroonCongo, Rep.Guinea MaliEthiopiaZambiaTogoSao Tome and PrincipeBeninBurkina FasoTanzaniaLesothoGambia, TheSenegalUga... Rwanda 3
Equations (1) and (2). In order to eliminate potential impacts of abnormal observations on the decomposition effects, we remove one percentile of household per capita income and per capita consumption expenditure in each tail. Summary statistics of variables used in this analysis are presented in Table A2. Table A3 summarizes the decomposition results.

# **Appendix Tables**

# **Table A1: Definition of household income components**

|**_Equation (1) - simple dec_**|**_omposition_**|||
|---|---|---|---|
|**Labor Income**<br>|**Non-labor income**<br>|||
|Wage income from<br>agriculture|Housing rental income|||
|Wage income from non-<br>agriculture|Non-agricultural rental<br>income|||
|Non-agricultural<br>|Remittance|||
|enterprise income||||
|Net income from|Other income|||
|farming||||
|**_Equation (2) - detailed de_**|**_composition_**|||
|**Agriculture income**|**Non-agriculture**<br>**income**|**Capital Income**|**Remittance**|
|Wage income from<br>agriculture|Wage income from non-<br>agriculture|Housing rental<br>income|Remittances|
|Net income from|Non-agricultural|Non-agricultural||
|farming|enterprise income|rental income<br>Other income||

_Source: NLSS-I and III_ 30
<!-- Start of picture text --> FIGURE V-3 : PROGRESSIVITY OF TAXES AND TRANSFERS<br>0.70 0.050<br>0.60<br>0.040<br>0.50<br>0.40 0.030<br>Kakwani<br>0.30<br>0.20 0.020 coefficient<br>0.10 0.010<br>0.00<br>0.000<br>-0.10<br>-0.20 -0.010 Marginal<br>contribution<br>Source: World Bank Group and staff estimates based on ENPH 2017UP STAFF ESTIMATES BASED ON ENPH 2017<br>Kakwani Coefficient Subsidies Education Health Redistributive effect<br>Regressive <---------> Progressive Direct taxes and C.S.S. Direct transfers Indirect taxes Unequalizing <---------> Equalizing<br><!-- End of picture text -->

# **_a) Direct taxes and indirect taxes_**

The sum of all direct taxes and contributions to the social security system is progressive since the Kakwani coefficient associated with that value is positive (see Table 1 of Annex A). Furthermore, when each of the direct taxes and social security contributions is examined, it is observed that all direct taxes, as well as contributions, are progressive, as seen in Figure V-4, all the associated Kakwani coefficients are positive. Additionally, given that the marginal contribution coefficients are positive, it is possible to deduce that all direct taxes and all social security contributions help to reduce inequality. The income tax is more progressive and redistributive than the other direct taxes. As far as social security contributions are concerned, the most progressive contribution is made by employers to health care, and the most redistributive is the contribution to health care paid by employees.

<!-- Start of picture text
mpors o oa aove cans ()|.|.|.|.|.|12.0|124|
|of which: Parts|8.0|10.3|11.5|11.8|12.9|13.5|131|
|Finalproducts|9.9|9.5|9.8|11.0|11.7|11.6|123|

Source: Computations based on UN COMTRADE Statistics.

Links to Pan-European consumers and suppliers of parts for further processing in Turkey have expanded since 1995 in most networks. Except for the T&C network, Pan-European markets absorbed over 1995-2003 a growing share of network exports and Pan-European producers accounted for an increasing share of imported inputs (Table 19). In 2004, these trends were reversed, as trade with the rest-of-world expanded slightly faster than with pan-European countries.
corresponding to Finland; the green line plots the values corresponding to Italy, and the red line plots the values corresponding to Serbia.

Figure 1 also demonstrates that different countries implemented their reopening policies at a different pace. We define the start of reopening as the date when the stringency index began declining after reaching the maximum in March-May 2020. Some specific country examples are illustrative of the differences in the reopening process. For instance, Italy and Serbia had similar maximum levels of stringency - Serbia even reaching the stringency index of 100 - but their reopening trajectories were very different. Italy halved the stringency of restrictions in the course of a month and later remained at roughly the same stringency level. Serbia reduced its stringency by three-quarters of its peak value in a month and a half. At the end of June, Serbia had to reimplement restrictions, and its stringency index exceed that of Italy during part of the summer. On the other hand, in Finland, the stringency of restrictions during April-May were lower than those in Italy and Serbia. Furthermore, while Finland started lifting restrictions in spring, the stringency index never dropped below half its maximum value.

Reopening processes are multi-faceted, but in this paper, we focus on three dimensions that we think are most relevant for the economic recovery: i) sequencing, ii) timing, and iii) speed. Differences along these three dimensions are expected to be relevant from an economic point of view.

The first important dimension of the reopening process is the sequence in which different social and economic activities restart their normal operation. For example, school reopening has been and is still hotly debated, given the uncertainty about contagion risk among children (Milani et al., 2020). Countries adopted different school opening policies, even when facing a similar epidemiological situation (World Bank 2020). Denmark was the first country to reopen schools, starting with primary school grades on April 15; Italy and Spain did not reopen schools until the fall term. In between, countries opted for different strategies
is intensive in fixed capital, whereas entrepreneurs that require relatively more working capital, will be financed primarily by informal lenders.

In addition, if the transaction costs of accessing formal finance are large, households that need little credit will tend to rely on informal lenders whereas those with large credit needs will be better off incurring the fixed costs in order to have access to a lower cost of capital.<sup>6</sup> Empirical research on rural credit markets faces the problem that the combination of unobserved heterogeneity and of endogenous matching of agents into borrowing from different lenders can create selection biases on the parameters of interest.<sup>7</sup> .

The approach taken here differs from that of Bell et al. (1997), Kochar (1997) and Conning (1996) and resembles Key (1998) in that we estimate the likelihood of borrowing from each source as dictated by the structure of the model. This way, we are able to assess how important enforcement problems vis `a vis transaction costs are in the overall picture of credit markets.

The data used come from a cross-section survey conducted in Thailand in 1997, an interesting country because despite the growth episode experienced in the 1980s, formal credit is still limited in rural areas.<sup>8</sup><sup>_,_9</sup> The estimation results raise several points. First, there are disparities in the cost of accessing different lenders: while the estimated cost of accessing a formal institution is on average US$30, the cost of accessing an informal source is negligible. Second, the cost of accessing formal finance depends on the characteristics of the household, such as the proximity to the bank or whether the household has a savings account. Third, the model suggests that roughly 85 percent of households that resort to the bank are constrained. Thus, most households receive a lower credit amount because if the bank were to advance to them more capital, they would have the incentive to
survey experience during 1999 and 2000, and country data in Albania, Armenia, Bolivia, Guyana, Macedonia, and five countries in the East Caribbean region. The selection of examples was guided by their usefulness in illustrating the steps in the analytical framework, and not to point out any strengths or weaknesses of any country's public sector.

Section 2 lays out the conceptual framework. Section 3 summarizes the survey methodology. Section 4 describes the approach for measuring the institutional environment using the East Caribbean example as an illustration. The approach for measuring aspects of performance is described in section 5, again using the same example of East Caribbean states. The relationship between performance and institutional environment is described in section 6, using the illustration of the Bolivian public sector. The Bolivian example was also used in Section 7 to show how survey results can be used to identify promising prospects for reform.

Section 8 concludes with an overview of how the analytical framework is being used in practice.

2
# _Alternative per-capita income measures_

Columns (iii) and (iv) of Table 3 repeat the analyses from (respectively) columns (i) and (ii), but with one change. In the first two columns, income is measured _ex post_ , meaning that the income estimates for each year reflect the best available information as of 2009. In many cases these _ex post_ estimates represent a substantial revision from contemporaneous estimates. If graduation decisions at the time were consciously based on the degree to which income was estimated to exceed the threshold, contemporaneous income data – the numbers available to policy makers and Bank officials at the time – may better explain statistically which countries graduate and when. On the other hand, ex post estimates, which incorporate revisions based on information available only later, are presumably more accurate and thus better reflect whether or not a country was really ready to graduate at any given time. Bank and government officials may have been operating on that reality as well as on the then-available official numbers. Therefore, it is unclear which set of figures – contemporaneous or _ex post_ – might better predict actual graduation decisions.

Contemporaneous and ex post estimates are correlated at .96, but for some countries the gap is quite large. Slovakia’s _ex post_ estimates for 1993-2006 are much higher, with a gap of $730 in 1993 growing to $2780 by 2006. For the years 2000-2003, Slovakia’s income was under the IBRD threshold (by about $1200 on average) based on contemporaneous data, but over the threshold (by about $700 on average) based on _ex post_ estimates.

In the last two columns of Table 3, the sample of countries over the threshold is determined using contemporaneous income estimates. The samples are slightly smaller than in the tests using _ex post_ estimates: revisions are in most cases upward rather than downward, so more countries were (believed to be) under the threshold based on contemporaneous data. The results are nearly indistinguishable from those obtained using _ex post_ estimates.
.p. across the region (dynamic point estimates are available in Table A4 and Table A5). As expected, the no-child dynamic estimates in Figure 1 are almost always above the ignore child sample estimates. Still, the confidence intervals typically overlap, and the magnitudes in the differences are very small for all countries except Maldives.

Lower labor force participation among married women without children persists up to 5 years after marriage in India and Maldives. In contrast, female employment rates appear to revert back to pre-marriage levels by _t_ = 5 in the no-child sample for Bangladesh and Nepal. Still, it is important to recall that the event-study estimates in the no-child sample 12
Restuccia, D., Rogerson, R., 2013. Misallocation and productivity. Rev. Econ. Dyn. 16, 1–10. https://doi.org/10.1016/j.red.2012.11.003 Wollburg, P., Bentze, T., Lu, Y., Udry, C., Gollin, D., 2024a. Crop yields fail to rise in smallholder farming systems in sub-Saharan Africa. Proc. Natl. Acad. Sci. 121, e2312519121.

https://doi.org/10.1073/pnas.2312519121 Wollburg, P., Markhof, Y., Bentze, T., Ponzini, G., 2024b. Substantial impacts of climate shocks in African smallholder agriculture. Nat. Sustain. https://doi.org/10.1038/s41893-024-01411-w World Bank, 2022. Poverty and Inequality Platform (database). World Bank, 2021. World Bank Open Data.

# Acknowledgments

We are grateful to the World Bank LSMS team and the National Statistical Offices for their efforts to collect and publish the LSMS-ISA data and to support this data harmonization effort. We would like to thank, in particular, Alemayehu Ambel, Asmelash Haile Tsegay, Manex Bule Yonis, Wondu Yemanebirhan Kassa (Ethiopia), Heather Moylan, Wilbert Drazi Wondru (Malawi), Marco Tiberti, Ismael Yacoubou Djima (Mali, Niger), Akiko Sagesaka, Ivette Contreras, Gbemisola Oseni, Kevin McGee (Nigeria), Akuffo Amankwah, Amparo Palacios Lopez (Tanzania), Giulia Ponzini (Uganda). We would also like to thank Siobhan Murray for geospatial support, and Gero Carletto and Talip Kilic for their guidance and comments. This work was supported by the World Bank Research Support Budget grant “On agricultural productivity measurement in Sub-Saharan Africa”.

20
for products that are unusually heavy and/or not typically shipped in containers.<sup>7</sup> Kellogg and Sweeney (2023) study the effects of the JA on U.S. regional markets for petroleum and related products. They show that transport cost differentials associated with the JA lead buyers on the U.S. East Coast to substitute toward foreign sources, even as U.S. suppliers in the Western Gulf Coast shift their sales towards exports and away from the U.S. East Coast. Although their analysis does not include PR, the behavior they document for petroleum products on the East Coast is similar to what we observe for all sea-shipped final products purchased in PR. The absence of JA-compliant tankers and liquid vessels in our port-of-call data is also consistent with their results.

Francois et al. (1996) use a computable general equilibrium (CGE) model of the entire U.S. to measure the equivalent variation of removing the JA for the U.S. economy as a whole. These authors calculate that the welfare cost of the JA to the U.S. economy was approximately $3 billion in 1989.<sup>8</sup> These CGE estimates would include a significant burden of the JA operating through higher prices for upstream inputs. We lack a credible, up-to-date input-output table for PR, which limits our ability to do economy-wide general equilibrium calculations. Furthermore, attribution among upstream products is difficult because firms can respond to JA trade costs by locating outside of PR entirely, which means that some industries to which hat calculus might otherwise be applied are altogether missing from the data.<sup>9</sup> Our CV calculations thus consider only distortions to purchases by final demand, and 7Our evidence that the composition of PR’s upstream imports is biased against sea-shipped products from all sources is different than the substitution away from U.S.-sourced products that Olney estimates in his sample of all products. These are different samples
quintiles are expressed in terms of quintiles of _individuals_ in the population. In Table 4 below are shown the quintile boundaries of the asset index.

**Table 4: Quintile of asset index, 1999 Azerbaijan Census.**

|_Percentile_|_Centile value_|_Lower Bound_|**Upper Bound**|
|---|---|---|---|
|||_95% CI_|**95% CI**|
|20|-2.387|-2.389|-2.387|
|40|-1.329|-1.331|-1.327|
|60|0.313|0.306|0.318|
|80|2.184|2.184|2.185|

Note: Total number of present population 7.798.578 according to 1999 Azerbaijan Census.

64