rafmacalaba/data-use-mentions-extended huggingface dataset:
their scores, which in turn will be equivalent to building state capacity, as defined by the indicators in the APAs.

The specific indicators in the APA in the Tanzania project are:

- (i) **Urban planning system:** documentation and indicators of having a General Planning Scheme in place.

- (ii) **Fiduciary or financial management system** : documentation of internal audit reports undertaken by a fully constituted Internal Audit Committee, and scores on a system of public procurement.

- (iii) **Infrastructure management** : documentation and verification of the utilization of financing to deliver physical infrastructure such as roads and sanitation services.

- (iv) **Accountability and oversight by citizens** : verifying public disclosure of information about local budgets and convening of public meetings.

In addition to these four, a fifth area of own source revenue generation (from the property taxes assigned to local governments) was targeted in the original evaluation design:

- (v) **Local property tax system** : increase in own source revenue from the collection of local property taxes. The goal was to enable local governments to gradually move away from dependence on fiscal grants to becoming self-reliant on own source revenue, of which local property taxes are typically the most important. However, in July 2016, three years into the program, the national government recentralized property tax collection, shifting it from local governments to the Tanzanian Revenue Authority (TRA). In response, after 2016 the program decided not to assign any scores to performance in generating own source revenues.

This evaluation draws on data from both the APAs and our independent surveys administered to government officials and households. Each of these sources has value. Changes in the APAs document whether project LGAs improved on the specific indicators that the project targeted. The survey data play three key additional roles. First, many of the survey indicators provide an independent check on data gathered via the APAs.<sup>8</sup> Second,
Bouwer, Laurens M. 2013. “Projections of Future Extreme Weather Losses under Changes in Climate and Exposure.” _Risk Analysis_ 33 (5): 915–30.

- Bryant, Benjamin P., and Robert J. Lempert. 2010. “Thinking inside the Box: A Participatory, Computer‐ Assisted Approach to Scenario Discovery.” _Technological Forecasting and Social Change_ 77 (1): 34–49.

- Bussolo, Maurizio, Rafael De Hoyos, Denis Medvedev, and others. 2008. “Economic Growth and Income Distribution: Linking Macroeconomic Models with Household Survey Data at the Global Level.” In _International Association for Research in Income and Wealth (IARIW) 30th General Conference, Portoroz, Slovenia, August_ , 24–30.

- http://siteresources.worldbank.org/INTPROSPECTS/Resources/334934‐

- 1225141925900/Buss%26DeH%26Med2008LinkingHHSurveyDataIARIW.pdf.

- Caminade, Cyril, Sari Kovats, Joacim Rocklov, Adrian M. Tompkins, Andrew P. Morse, Felipe J. Colón‐ González, Hans Stenlund, Pim Martens, and Simon J. Lloyd. 2014. “Impact of Climate Change on Global Malaria Distribution.” _Proceedings of the National Academy of Sciences_ 111 (9): 3286–91. doi:10.1073/pnas.1302089111.

- Carter, Michael R., Peter D. Little, Tewodaj Mogues, and Workneh Negatu. 2007. “Poverty Traps and Natural Disasters in Ethiopia and Honduras.” _World Development_ 35 (5): 835–56. doi:10.1016/j.worlddev.2006.09.010.

- Dollar, David, Tatjana Kleineberg, and Aart Kraay. 2013. “Growth Still Is Good for the Poor.” _World Bank Policy Research Working Paper_ 6568.

- Dollar, David, and Aart Kraay. 2002. “Growth Is Good for the Poor.” _Journal of Economic Growth_ 7 (3): 195–225. doi:10.1023/A:1020139631000
CAADP Indicator 2.1. _Increased agriculture production and productivity_ contains the following sub-indicators: 2.1.1 _Agriculture value added_ ; 2.1.2 _Agriculture production index_ ; 2.1.3 _Agriculture value added per agricultural worker_ ; 2.1.4 _Agriculture value added per hectare of arable land_ ; 2.1.5 _Yields for the five AU priority commodities_ (cassava, yams, maize, meat, and cow milk).

18
**Quarterly**|**Q3 2021**|**Monthly**|**October 2021**|**Monthly**|**November**<br>**2021**|

Source: Gatti et al., (2022). Information obtained from country statistical office websites; government data portal websites; central bank websites and Ministry of Planning/Economy/Finance websites Notes: N/a means information is not readily available. All data accessed as of January 2022. This table is not exhaustive, it has only surveyed country Statistical Office Websites, Government Data Portal Websites, Central Bank Websites and Ministry of Planning/Economy/Finance websites. Third-party websites were excluded from this survey. The table has been updated and verified by World Bank country economists. Mexico was selected as a comparator because it is an uppermiddle-income economy with a well-functioning data ecosystem.

On top of the lack of published monthly unemployment data in the MENA region, there are also challenges regarding the definitions employed. For examples, countries around the world usually follow the ILO’s definitions of employment and unemployment, which are consistent with definitions adopted by other developed countries, such as the United States (see Table 4).

**Table 4 Definitions of Employment and Unemployment from the U.S.  BLS, the French INSEE and the ILO**

|**_Definitions_**|**Employment**|**Unemployment**|**Source**|
|---|---|---|---|
|**United States**<br>**-Bureau of**<br>**Labor**<br>**Statistics**<br>**(BLS)**|Someone, aged 16 or over, who has either (1) worked at<br>least 1 hour as a paid employee or (2) in their own<br
40 Jones (1984) probes the distribution of contraceptive methods provided by private physicians or clinics in a subset of developing countries from the World Fertility Survey. An erratic pattern emerged from this data. In the Dominican Republic, three quarters of all sterilizations and one fifth IUD insertions vere performed by private physicians. Private clinics provided relatively more injectables than other methods in Peru, Costa Rica, and Panama while they covered a significant proportion of pill users in Haiti and Malaysia.

Other Health Providers. One of the major research questions in the area of private physicians has been to what extent do the quality of services suffer when non-physicians handle family planning tasks that have traditionally been in the hands of physicians. This question may be emphasized because of its practicality--when physicians are in short supply and many couples do not have access to their services, what are the consequences of allowing more abundantly available health personnel to take over some of their responsibilities.

Traditional midwives (i.e., unlicensed midwives or birth attendants) are major health care providers in the developing world. They are particularly dominant in the delivery of babies. Many projects have attempted to broaden the role played by midvives so that they either refer couples to clinics or dispense contraceptives directly to clients. A recent review of these projects (SimpsonsHebert et. al, 1980) concludes that the results are mixed.

Traditional midwives seem to be more effective providers of family planning services when they distribute contraceptives compared to when they only do referrals. A Nicaraguan program allowed midwives to purchase contraceptives and other medical supplies at subsidized prices and then sell them to their clients. A study in the program area indicated that use of oral contraceptives had douibled after two years. Further, there is evidence that the incentives for trained midwives to actively recruit family planners depends upon the economic returns to

**al.fp:Pauily Planning** Paper **(whol.I**
the model assigns walking speeds to off‐road travel and vehicle speeds to on‐road travel. The routing algorithm therefore prefers road network routes when available.

# Calculation for on-road travel

Road network type, condition and geographic zone (Plain, Rolling, Hill, Mountain) determine the generic average vehicle speeds for each roadway type. Slope information from the DEM is attached to each element of road network data in order to categorize the speed of the vehicle by a combination of slope angle, road surface and road type.

Strategic Road Network (SRN) and District Road Core Network (DRCN) speeds (model Classes 1 and 2) are derived from the Road Classes II and III (half of the design speed) of the Nepal Department of Roads’ “Nepal‐ Road‐Standard‐ 2070”. Class 4 speeds were determined based on reported walking path travel times from consulted organizations and individuals (notably staff from the World Food Programme and Rural Access Programme) (WFP 2018, RAP 2018).

Strategic Urban Roads (SUR), Urban Roads (UR) and unpaved DRCN speeds (model Class 3) were determined using the results of the HRVS survey. The vehicle travel speed for each household was estimated by the below equation 3. The reported distance and time to financial institutions (banks) was employed to maximize the denominator and numerator of the equation and so reduce error. The estimated average vehicle speed was roughly 11 km/hour and the average on‐road slope was approximately 27% in Nepal (based on GIS calculations using DEM and road network data). These estimations were approximately half the speed for Class 2 roads in equivalent (mountainous) slopes.

Extrapolating from this, we set the speed for Class 3 to half of Class 2 for all slope categories. These speeds were then confirmed via consultations with partners.

## **_Equation 3_**

Where: Db: Distance to bank V: estimated vehicle travel speed of Dr: Distance to road V=<sup>Db
& Nguyen, T. Q. (2005). _Can Debt Relief Boots Growth in Poor Countries?_ (IMF Economic Issues No. 34). Washington, D.C.: International Monetary Fund.

- Cooper, W. O., Boyce, T. G., Wright, P. F., & Griffin, M. R. (2003). Do Childhood Vaccines Have Non-Specific Effects on Mortality? _Bulletin of the World Health Organization_ , _81_ (11), 821-826.

- Coppedge, M., Gerring, J., Lindberg, S. I., Skaaning, S.-E., Teorell, J., Altman, D., ... Farhad, M. (2015). _Varieties of Democracy (V-Dem) Project Dataset v5._ Gothenburg: The V-Dem Institute.

- Crespo Cuaresma, J., & Vincelette, G. A. (2009). Debt Relief and Education in HIPCs. In C. A. P. Braga & D. D ̈omeland (Eds.), _Debt Relief and Beyond - Lessons Learned and Challanges Ahead_ (p. 3548). Washington, D.C.: The World Bank.

- DHS Program. (2015). _Demographic and Health Surveys._ Rockville, MD: ICF International.

- Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015). The Next Generation of the Penn World Table. _American Economic Review_ , _105_ (10), 3150-82.

18
6. **Trust** . As with the previous point, households that do not fully understand the product may place significant weight on their trust in the insurance provider or the individual who markets the product to them. They may also rely on product endorsements from a trusted friend, village leader, or family member. Similarly, households may perceive a risk of default of the insurance company (Doherty and Schlesinger 1990). Guiso, Sapienza, and Zingales (2008) argue that trust has important effects on financial market participation.

7. **Framing and behavioral influences** . Research in psychology and behavioral economics suggests households are affected by subtle changes in the way a product is presented to them. For example, in a field experiment in South Africa, Bertrand et al. (2010) find subtle advertising cues significantly influence credit demand. For example, including the picture of a man rather than a woman on an advertising flyer for a consumer loan shifts loan demand by up to 2.2% in the monthly interest rate.

# 7.1 Empirical evidence

Our research has sought to identify the relative importance of the different demand factors described above. As a first simple type of evidence we present results of household surveys from our study areas in Andhra Pradesh. The survey ask households to describe in open-ended fashion why they did or did not purchase insurance. Responses are classified into one of a number of categories. Households that purchase insurance generally cite reasons relating to “security” or “risk reduction.” Reasons cited by households that do not purchase insurance are presented in Table 6.2.

[Table 6.2 here]
ing and targeting ‘influencers’ within those communities. It suggests that a next step in the development of intervention-based studies on refugee-host social cohesion could be to pilot interventions that introduce information, representations, or topics for deliberative debate into neighbourhood or household-level focus groups, and explore their impact on intra-group attitude formation at the level of the neighbourhood. Further research could also shed light on the causal mechanisms through which positive and negative attitudes form and diffuse within networks (e.g. through intra-communal norm entrepreneurs).

# **3) Adopt a political economy lens to understand the distributive consequences of refugee hosting, and focus differently on perceived ‘winners’ and ‘losers’ within the host community.**

Our research reveals significant variation in host community attitudes towards refugees. In particular, our qualitative data suggests that one reason for this is the particular socioeconomic position of host community individuals and households with respect to the presence of refugees. For example, where host community members sit as landlords/tenants, employers/employees, or land-owners/agricultural workers may influence whether they view refugees as contributors or competitors. Put simply, there may be a social class dimension to attitude formation: owners of capital and land are more likely to view refugees positively; workers or tenants who rely upon high wages and low rents are more likely to regard refugees as a source of competition. This suggests that attempts to engage in intra-group attitude change should adopt a political economy lens. Perceived ‘losers’ need to share in the benefits of hosting refugees, including through targeted access to employment opportunities and public services. Perceived ‘winners’ should be supported to advocate for the benefits of hosting refugees at a community level.

# **4) Adopt distinctive social cohesion programmes for urban and camp contexts, with different degrees of emphasis on economic, security, identity-related, and rights-based attitudes.**

The nature, scope, and type of economic and political dynamics in urban and rural refugee and host communities are variable, and policies need to be
# **Climate Immobility Traps:**

# **A Household-Level Test**<sup>*****</sup>

Marco Letta<sup>§</sup> , Pierluigi Montalbano<sup>†</sup> , Adriana Paolantonio<sup>‡</sup>

**JEL-Codes:** C31; O15; Q54.

**Keywords** : climate migration; immobility traps; adaptation; causal forests; household data.

**Replication package:** Available at the World’s Bank Reproducible Research Repository.

**Declarations of interest:** None.

§ Department of Social Sciences and Economics, Sapienza University of Rome; marco.letta@uniroma1.it † Department of Social Sciences and Economics, Sapienza University of Rome; pierluigi.montalbano@uniroma1.it ‡ Development Data Group, World Bank; apaolantonio@wordbank.org

*For valuable comments, we are grateful to Gero Carletto, Augusto Cerqua, Talip Kilic, Kevin McGee, Donato Romano, Asmelash Haile Tsegay, Alberto Zezza and seminar participants at the World Bank. We also thank Thomas Patrick Bentze, Pauline Castaing, Josephine Durazo, Siobhan Murray, Federica Petruccelli, Akiko Sagesaka, and Philip Randolph Wollburg.
# **5 Potential Health Impacts**

The results in Section 3 show a large decline in the food-fish catch as riverine salinity moves from its minimum to maximum level during the year. A decline of this magnitude has potentially serious health implications for the region, since ample previous research has documented the role of fish protein intake in promoting mother/child health in Bangladesh (Rifata et al. 2023; Andrews et al. 2022; Dasgupta et al. 2021; Dasgupta and Wheeler 2019; Ferdous et al. 2015; Bogard et al. 2015). As Figure 3 and our econometric results show, the decline of food fish supplies is concentrated during the period of rapid riverine salinity increase in the spring. The magnitude of the associated health impacts depends upon whether mothers have the resources to compensate for declining fish supplies by increasing consumption of other protein sources. In this context, Bangladesh’s recent progress in reducing poverty is notable. According to the World Bank’s World Development Indicators, the country’s multidimensional poverty headcount ratio was 31.3% in 2010, 20.5% in 2016 and 6.6% in 2022. While the southwest coastal area remains relatively poor when compared with other regions, it has undoubtedly benefited from this trend.

Under current conditions, are the resources of poor households adequate to compensate for the sharp salinity-induced swings in riverine fish supplies in the southwest coastal region? To address this question, we have extracted the relevant individual and household data from the two most recent Bangladesh Demographic and Health Surveys (DHS): 2022 and 2018. We have limited our sample to household clusters in the six southwest coastal districts that are most strongly impacted by salinity-driven fluctuation in fish supplies. The two most recent DHS surveys do not include survey information for the peak salinity months, so we cannot draw direct inferences from the data. However, they do include information about the birth months of surveyed children. This permits investigation of differences in child morbidity and mortality measures for children born during
outside the smallest firms. This means that we are asking whether increased density resulted in job growth in larger firms, the kinds of firms that would be growing and creating employment if there were benefits from agglomeration.

The other reason for including firms with 5 or more persons engaged is that jobs in these firms are the kinds of jobs policy makers would be wanting to generate‐ they pay more and are more stable than own account work or employment in a very small firm. We have not used the enterprise surveys because in surveying only formal firms they exclude the vast majority of employment in Ghana. Our cutoff of 5 persons engaged means, for example, that the 2014 firm census captured 63% of the employees who reported being employed in the population census around the same time.

The second urbanization economy we can investigate is the impact of industrial diversity in a location. We cannot reject the hypothesis that there is no effect of diversity on district‐industry employment growth across all specifications. Combes and Gobillion’s (2015) summary of the effects of diversity is that there is no robust effect on employment growth, so our finding is perhaps not surprising, despite the headline findings of the importance of diversity, beginning with Henderson et al (1995).

Besides the general characteristics of an area, the characteristics of particular industries within an area have also been found to be important determinants of growth and productivity in the agglomeration literature. Table 6 shows the effects of specialization, competition and average firm size in an industry‐district on employment growth. The main results across the regressions are that these characteristics all have large, statistically significant and _negative_ effects on employment growth in an industry district normalized by that industry’s growth rate.

The negative impact of specialization is counter‐intuitive. Combes (2000: 341) found a similar result for France and noted that “explanations of the negative effect of specialization can hardly be found in the agglomeration forces presented above.” Combes and Gobillion (2015) summarize the literature estimating
||||-0.006|-0.008|-0.009|
|||||(0.017)|(0.014)|(0.017)|
|Earmarkfbt* MPft||||-0.021|0.008|-0.011|
|||||(0.018)|(0.030)|(0.027)|
|Observations|231,864|156,705|161,358|231,864|156,705|161,358|
|R-squared|0.527|0.683|0.770|0.527|0.683|0.770|
|<br>Mean provision rate|1.873|1.872|1.891|1.873|1.872|1.891|
|Firm*Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank*Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Location*Sector*Month FE|No|Yes|No|No|Yes|No|
|Firm*Year FE<br>Notes: The table shows estimates of OLS|No<br>regressions on our loan|No<br>-level data for the|Yes<br>sample of firms th|No<br>at at any time in ou|No<br>r sample period b|Yes<br>egin an earmarked|

Notes: The table shows estimates of OLS regressions on our loan-level data for the sample of firms that at any time in our sample period begin an earmarked credit relationship with a bank. The dependent variable corresponds to the provision rates of working capital loans. _Earmarkfbt_ is an indicator variable that equals one for all consecutive periods after a firm receives its first earmarked loan from a bank, and zero otherwise. _Riskft_ is an indicator variable that
for Defense of Democracies is Washington, DC-based nonpartisan research institute focusing on national security and foreign policy. It aims to strengthen US national security.

> 10The LJW data aligns with the territorial control analyses conducted by SIGAR and U.S. ForcesAfghanistan, which effectively tracks Taliban-controlled and contested districts by evaluating 5 indicators of stability, including governance, security, infrastructure, economy, and communications (Roggio 2017). 11More granular data is illustrated in Figures A.5 and A.6, which depict the district-yearly geographical distribution of Taliban controlled, U.S. allies controlled, and contested districts in Afghanistan.

17
A parametric approach offers a number of advantages over empirical variance decompositions and reduced-form regressions. First, it allows a systematic assessment of the role of country-specific initial conditions for the poverty-reducing effects of growth and distributional change. While the literature has stressed how initial inequality shapes the growth elasticity of poverty, little if any attention has been paid to the roles of the initial level of development or the poverty line itself for the choice of poverty-reducing policies. For example, should the balance between pro-growth and pro-distributional policies be the same in Zambia and El Salvador, which have similar Gini coefficients, despite their wide disparity in terms of per capita income (US$361 and US$2,200 respectively in 2002)? The approach taken in this paper allows a rigorous answer to this kind of question.

Second, the parametric approach permits removing the straightjacket of a common poverty line across countries, which otherwise is virtually unavoidable in crosscountry empirical work. Most of the available studies use poverty statistics based on the international PPP US$1 a day poverty line. However, such definition is of little interest for many middle income developing countries (not to mention industrial economies). As an example, in Argentina the headcount poverty was in 2002 close to 60 percent when calculated on the basis of the nationally-defined poverty line, while internationallycomparable poverty indicators based on a dollar-a-day poverty line would place the poverty rate around 3 percent. One implication of these diverging assessments is that the findings from empirical work using cross-country poverty databases, based on a common poverty line, tend to give more weight to countries where the US$1 a day poverty measure makes sense -- i.e., low-income countries where our results below suggest that growth should be expected to dominate distributional change from the viewpoint of poverty reduction.

Of course, the advantages of the parametric approach matter only if the chosen parameterization fits the data well. We find that the null hypothesis of lognormality cannot be rejected when applied to the distribution of per capita income, regardless of whether income