rafmacalaba/data-use-mentions-extended huggingface dataset:
reasoning acts as a safeguard, rather than motivator, of many behaviors. This insight has important potential implications on how best to influence financial behavior. Even when people are fully aware of the most appropriate action to take, cognitive biases and heuristics may prevent this knowledge from translating into action. Thus, the traditional causal framework linking improved financial knowledge to changes in awareness, perceptions, attitudes and behavior, may underestimate important psychological barriers to financial inclusion that weaken the suggested causal chain. Acknowledgement that we base many decisions on heuristics rather than full information helps to explain why, for instance, “rule‐of‐thumb” approaches to financial education can be more effective at changing behavior than teaching more detailed accounting principles (Drexler _et al._ , 2012).

Although improvements have been registered in the last 3 years, 46% of the Nigerian population remains financially excluded, with no access to formal or informal financial services.<sup>1</sup> This compares unfavorably

> 1 Results presented here are based on a recent nationally representative survey of 20,000 consumers conducted by EFinA in 2010: <u>http://www.efina.org.ng/our‐work/research/access‐to‐financial‐services‐in‐nigeria‐survey/.</u>

2
able to examine the effect of access to two distinct types of social capital— access to the traditional family-based social network and access to the modern, partybased political network—and contrast how they affect access to finance and the private welfare of households, and how those benefits differ across segments of the society.

Our main finding is robust evidence that access to informal loans significantly

> 7 Those authors rely on the 2009 Rural Finance Survey conducted by the National School of Development at Peking University, which covers 1,950 households in 81 rural villages in three provinces (Heilongjiang, Hunan, and Yunnan).

5
that exports follow the booms and busts in the destinations, as captured by the destination fixed effects in the regression. The difference can be explained if small, land-locked countries are more limited in their choice of trade partners than other countries. The fact that this process of following booms and busts is not taking place on the same scale in the land-locked countries could simply mean they have less freedom of choice. Therefore, exporting to attractive countries is not a survival-related choice by the firm but rather a natural constraint: exporters in landlocked countries, facing high trade facilitation costs, are stuck with exporting to their neighbors. However, to really judge the effects of this “landlocked hypothesis” with any degree of certainty, one would need this type of firm-product-destination data from more countries.

**Table 9:** Surviving past the first year (probit – marginal effects) excluding garment or footwear products

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|**Attractiveness**<br>|**0.141**|**0.0675**|**0.0659**|**0.0458**|**0.0799**|
|**Agglomeration**|**0.217*****|**0.158*****|**0.185*****|**0.159*****|**0.174*****|
|**Exp. w. product**<br>|**1.050*****|**0.936*****
Long-Term Study of PROSPERA on Intergenerational Occupational Mobility<sup>1</sup> Iliana Yaschine, Delfino Vargas and Curtis Huffman<sup>2</sup> In collaboration with Hiram Carreño, Ulises Hernández and Tlacaelel Mendoza

**Key words:** conditional cash transfers; intergenerational occupational mobility; occupational attainment; latent treatments; impact evaluation

**JEL:** I38; C31; J62; J68; Z13

1 This paper is part of the research project “Studies of PROSPERA’s long-term results” which was funded by the Government of Mexico through the National Coordination of PROSPERA (CNP) as part of a technical cooperation agreement with the World Bank. The World Bank team is grateful to the Government of Mexico, including the Ministry of Social Development (now Ministry of Welfare), Ministry of Education, and Ministry of Health for the close collaboration in this research project, including the provision of the main datasets. The World Bank team would also like to thank the National Council for the Evaluation of Social Development Policy (CONEVAL), the National Institute of Public Health, development partners such as the Inter-American Development Bank, and the national and international researchers that contributed in the Advisory Evaluation Group. This group was established to ensure technical quality and methodological rigor of the “Studies of PROSPERA’s long-term results.” These studies were coordinated by Maria Concepción Steta, WB Senior Social Protection Specialist; Clemente Ávila, WB Social Protection Economist; and Mónica Orozco, WB Consultant.

The authors thank Clemente Ávila, Raymundo Campos, Rodolfo de la Torre, Polly Jones, Mónica Orozco and Concepción Steta for their comments, as well as the participants of the two seminars organized by the World Bank and PROSPERA, where the progress of the research was discussed. Furthermore, we acknowledge the work of the PROSPERA and the World Bank teams for integrating the ENCEL panel that we used.

2 University Program of Development Studies (PUED), National Autonomous University of Mexico (UNAM).
# **3.3 The Living Standards Measurement Study (LSMS) survey program as a candidate for studying climate migration**

<mark>The Living Standards Measurement Study (LSMS) is the most widely known household survey program of the World Bank and has been at the forefront of methodological research on data collection in low- and middle-income countries since its start in the early 1980s. LSMS surveys have several distinct strengths that make them a solid source of data for studying the effects of climate change at the micro level. Their multi-purpose, multi-topic nature and their nationally representative samples allow tracing the impacts of climate change on a wide range of outcomes including poverty, mobility, agriculture, and health, and assessing these impacts for different groups of households and individuals, especially the poor and most vulnerable ones. These surveys regularly collect data on ownership of various asset types; financial inclusion; employment; access, usage, and management of natural resources, energy, and water access; consumption; health; and anthropometrics. This rich contextual information is essential for accurately understanding the main transmission channels through which climate migration impacts materialize and how they may disproportionately affect different population groups.</mark> <mark>LSMS leads the production of detailed plot-level data on agricultural productivity, inputs, and production practices including those that are relevant for climate change adaptation and mitigation.</mark><sup>25</sup> <mark>This is critical information in the context of climate change, given that most of the global poor are engaged in agriculture and that this sector is highly vulnerable to climatic variability – especially in low-income countries where smallholder farmers rely on rainfall as the primary source of irrigation. In these settings, agriculture is possibly the leading channel to explain climate migration and, as such, detailed agricultural data collected at the household level are key information for studying this nexus. The community survey complementing the household level data collection in the LSMS survey system is also an important source of information for better understanding changes and
out of government funding allocations. Although the supply of and demand for data may fluctuate in the short run, gaps between demand and supply erode the long-run sustainability of an NSS.

Canada’s chief statistician, Ivan Fellegi, argues that “the greater the authority of the chief statistician, the more important it is to have a variety of mechanisms through which the different needs of different client groups can be determined” (Fellegi 1995). Assessments of NSSs in the Organisation for Economic Co-operation and Development (OECD) identify deficiencies in understanding evolving user needs (PACAC 2019; APAC 2020). Similar deficiencies are found in low- and middle-income NSSs. An AIDDATA survey of policy makers, government technocrats, and NSS staff found that the most important and frequent users of statistics were international organizations and development partners, not domestic users (Sethi and Prakash 2018).

The four supply side pillars of the SPI are a good proxy for the NSS’s capacity to produce and disseminate a range of data products. The empirical model presented here aims to proxy the demand for official statistics. It draws on the same criteria underlying the SPI.<sup>8</sup> Our empirical approach is derived from the literature on inferring the “social efficiency” of economic indicators by the measured deviation of these indicators from the efficiency frontier (e.g., Sen 1981; Moore et al. 2000; Wang et al. 1999). The efficiency frontier is identified from the residual of a regression of an indicator of interest (in our case, the SPI) on a set of control variables, a methodology sometimes referred to as benchmarking (Ravallion 2005).

# **_Empirical Model_**

Our empirical model relates the values of the SPI index and its pillars with the variables that may determine a country’s demand for and supply of statistical data. This reduced-form model is represented in the following form: S 1L 2 3 4S 5 6 7 + ii,kk ii ii ii ii ii ii
# **Table 3 Share of Migrants in Selected Employment Sectors in China, 2008–09**

_(percent)_

||2008|2009|
|---|---|---|
|Manufacturing|42.0|39.1|
|Construction|16.3|17.3|
|Hotels and<br>catering|7.6|7.8|
|Wholesale and|7.0|7.8|
|retail trade|||
|Transport|5.6|5.9|
|Other|21.5|22.1|

_Source_ : Calculated from NBS national rural household survey data compiled in Sheng (2009).

29
Policy Research Working Paper 9655

# **Abstract**

This paper shows expansionary fiscal austerity via reallocation of credit supply, but with a raise in poverty. For identification, the paper exploits the introduction of a Mexican law limiting the debt of subnational governments along with matched credit register, firm, bank, and state datasets. After the law, states with higher ex ante public debt grow substantially faster, despite larger fiscal consolidation (higher taxes and lower public expenditure). Banks operating in more indebted states reallocate credit supply away from local governments into private firms, with stronger effects for banks with higher exposure to local public debt, consistent with lowering crowding out. Effects only happen after the law, not before, and there are strong firm-level real effects associated. The reduction of crowding out is stronger for financially constrained firms and for firms operating in states with higher ex ante public spending on social services over infrastructure projects. In states more affected by the law, despite better economic effects, extreme poverty increases––especially in states with higher ex ante public spending on social services over infrastructure––consistent with a strong reduction for social services during the fiscal consolidation.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cruizortega@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent
6|30.6|18.5|6.9|
|Open defecation|% pop|38.3|40.6|20.8|23.2|11.0|
||||**2000**|**2005**|**2008**||
|Domestic water consumption|liter/capita/day|50.9|76.7|69.6|80.5|196.4|
|Revenue collection|% sales|94.1|95|95|95|99.3|
|Distribution losses|% production|34.8|21.0|12.6|10.6|28.8|
|Labor costs|connections per<br>employee|175.9|—|—|—|203.4|
|Total hidden costs as % of revenue|%|111|—|29|19|67|
|||**Bots**|**wana**|Count|ries with|Other|
|||**2005**|**2008**|scarc<br>reso|e water<br>urces|developing<br>regions|
|Residential tariff|U.S. cents per<br>cubic meter|45||45|60.26|30600|
|Nonresidential tariff|U.S. cents per<br>cubic meter|88||88|120.74|.–.|

Source: Demographic and Health Surveys and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data). Note: Access figures from the Demographic and Health Surveys (1988 and 2006) and Multiple Indicators Survey (2000). Total cost recovery is calculated assuming a capital cost of 40 cents/m3. — = Not available.

Botswana’s progress in increasing access to improved water supply is spread
# **8. References**

Arndt, C., R. Benfica, N. Maximiano, A. Nucifora, and J. Thurlow. “Higher Fuel and Food Prices: Impacts and Responses for Mozambique.” _Agricultural Economics._ 39(2008): 497-511.

Bangsberg D. et al., (2001), “Non-adherence to highly active antiretroviral therapy predicts progression to AIDS”, _AIDS_ , 15(9):1181-1183.

De Olalla P.G. et al., (2002), “Impact of adherence and highly active antiretroviral therapy on survival in HIV-infected patients”, _JAIDS_ , 30:105-110.

Deaton D., (1989), “Rice Prices and Income Distribution in Thailand: A Non-parametric Analysis”, _Economic Journal_ , Supplement 1989, 99(395):1-37.

Falagas M., E. Zarkadoulia, P. Pliatsika, & G. Panos, (2008), “Socioeconomic status as a determinant of adherence to treatment in HIV infected patients: a systematic review of the literature”, _Retrovirology_ , 5:13.

Ickovics J. & C. Meade, (2002), “Adherence to antiretroviral therapy among patients with HIV: a critical link between behavioral and biomedical sciences”, _JAIDS_ , 31:98-102.

Instituto Nacional de Estatistica, (2004), _Relatòrio final do inquérito aos agregados familiars sobre orçamento familiar 2002/03_ . Maputo, Mozambique.

International Monetary Fund, (2009), World Economic Outlook Database, April 2009 20
Since this is the quantitative part of a larger report describing the Vietnamese natural hazard environment, its main constraint is the availability of data. If a hazard or an impact channel cannot be described quantitatively, it cannot be analyzed here, but will still be discussed in the main report. For several relevant assets exposed to natural hazards, no official catalogues of geolocated assets exist or could be found. To still provide estimates of hazard exposure where possible, this work assembles some datasets from openly available sources. While these datasets should not be regarded as complete, they provide the best possible estimates, and hopefully as such are useful to policymakers.

In locating assets affected by hazards, data compiled from satellite imagery is especially important. Rather than simply using the raw output from the sensor, this analysis combines multiple types of such data and algorithmically detects specific objects, such as aquaculture ponds or rice paddies. While no such datasets were created originally for this analysis, several developed in other contexts were applied here. The employment of such datasets opens analyses of hazards at spatial granularities and geographic ranges that were not possible before, covering very large areas at very fine resolutions.

Sections two and three of this paper describe the hazard and sectoral data employed in the analyses, respectively. Section four details the methodology through which exposure estimates were computed for all relevant hazard/sector combination. Section five describes an approach to approximate the impact of such exposure in economic terms. Section six finally discusses the methodologies employed and concludes.

# 2. Hazard data

This analysis quantitatively considers four types of natural hazards affecting coastal Vietnam: flooding, typhoon winds, coastal erosion, and drought and saline intrusion. These hazards were selected based on discussions with experts and insights from existing literature describing the risk landscape in Vietnam (Rentschler et. al 2020, chapter 3). In this section, the data employed to model each hazard are described.

## 2.1. Flooding

This analysis uses one dataset modelling fluvial and pluvial flooding and a second one modelling
enabled by improved rules for allocation, can lead to improvements in student learning. Adopting repetition as a proxy for learning, we find that schools which gained additional teachers and brought PqTRs below 90 achieved significant improvements in lower primary repetition rates in comparison to schools with PqTR above 90 which did not gain additional teachers. The findings mirror others from Israel, the United States and India (Angrist and Lavy, 1999; Kreuger, 1999; Muralidharan & Sundararaman, 2013) which suggest that improvements in PTR are associated with improved learning outcomes, and demonstrate that similar dynamics persist in low-income countries. However, we do not observe impacts on dropout rates.

The descriptive analysis also reveals the extent to which rules-based approaches to teacher allocations may face challenges in implementation on the ground. Despite the clarification of allocation rules and provision of software tools to support allocations, the adherence to guidance for allocation of new teachers appears to have been weak and highly variable. At the national level, the allocation of new teachers to districts was not fully aligned with the guidance, with the share of districts’ need for new teachers met through allocations varying from a low of 32 percent to a high of 224 percent across three years. At the district level, too, the quality of allocations was highly variable, with the most successful deploying 75 percent of teachers in accordance to the rules and the least successful just 22 percent. Had the teachers deployed during this period all been allocated according to the guidance, it is likely that the number of schools achieving reduction in PqTR to below 90 would have been larger,

> 12 The comparison of staffing to identify treatment and control schools remains non-lagged, e.g. comparing EMIS data from 2016 and 2017.

21
this analysis is equal to unity if two countries have a two-way preferential trade agreement (TWPTA) or any deeper agreement, and zero otherwise. Results using this specification are presented in Appendix 1.

Data used to construct variables for economic proximity used in robustness checks and further defined below—namely the exporters’ and importers’ real GDP per capita at current PPPs—come from the Penn World Table version 8.1. Lastly, publically available data on bilateral geographic distance were downloaded from CEPII.

# _<u>2.3 Main gravity specifications for aggregate and disaggregate exports</u>_

Log-linearizing equation (4) and applying exporter-time, importer-time and country-pair fixed effects to minimize the potential biases associated with omitted variables affecting trade and EIAs yields our baseline gravity equation. This approach closely follows that used in BBF, which relies on the well-documented fact that using a panel dataset is the most appropriate method to tease out the endogenous relationship between trade agreements and trade flows (see for example Baier and Bergstrand (2007) and references therein). As such, the baseline equation we estimate and use to verify the results in BBF is written as:

> 9 See Appendix 2 for further details about the BEC classification system and how SITC Rev. 2 4-digit products were mapped to BEC categories.

> 10 The DESTA dataset includes two depth measures: a depth index and a depth variable based on explanatory factor analysis. Here, we rely on the depth index although both variables are highly correlated. There are also more than 601 bilateral agreements included in the full version of the dataset. Since we exclude very shallow framework agreements, we use 601 in our analysis.

> 11 The first provision category covers whether the agreement reduces tariffs to zero. The remaining six categories of substantive provisions encompass: services; investments; standards; public procurement; competition; and intellectual property rights. Additional information can be found in Dür _et al._ (2014).

> 12 This analysis does not explore which EIA provisions are the
mathematics in the WASSCE. Using this outcome variable is particularly relevant for policy makers, as English and mathematics are the core subjects that all students must take. Furthermore, a satisfactory score<sup>10</sup> in these two subjects is among the requirements for university entrance in The Gambia. As in the other columns, the results show that PSI-PMI led to a gain of between 15 and 21 percent more students who met the basic criteria for attending university. These results are statistically significant at the 10 percent level.

**Table 5** : Effects of the PSI-PMI Program on Student Learning Outcomes: Using the WASSCE Exam Score

|Variable|Outcome|variable: St<br>mathemat|andardized<br>ics score|WASSCE|Out<br>satisfac<br>WASSC|come vari<br>tory (“cre<br>E in both<br>Eng|able: Rece<br>dit”) scor<br>mathema<br>lish|ived<br>e on the<br>tics and|
|---|---|---|---|---|---|---|---|---|
||Within<br>contro|-school<br>l group|Betwee<br>control|n-school<br>group|Within<br>contro|-school<br>l group|Betwee<br>contro|n-school<br>l group|
||[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|
|PSI-PMI program|0.788**|0.585**|0.717|0.443|0.188*|0.154*|0.207*|0.157*|
||(0.334)
# **7. References**

Angrist, Joshua D.; Lavy, Victor. 1999 “Using Maimonides’ Rule to Estimate the Effect of Class Size on Scholastic Achievement.” _The Quarterly Journal of Economics_ 114 (2), 533-575.

Angrist, Joshua D.; Lavy, Victor; Leder-Luis, Jetson; Shany, Adi. 2017. “Maimonides’ Rule Redux.” NBER Working Paper No. 23486.

Asim, S., J. Chimombo, D. Chugunov, and R. Casley Gera (2019). “Moving teachers to Malawi’s remote communities: A data-driven approach to teacher deployment.” _International Journal of Educational Development_ 65 (2019) 26–43 Asim, S. and Casley Gera, R. 2024. _What Matters for Learning in Malawi? Insights from a Longitudinal School Survey._ Washington, D.C.: World Bank Publications.

Azim Premji Foundation. 2014. “Pupil-Teacher Ratios in Schools and their Implications.” Available at: https://www.issuelab.org/resources/23973/23973.pdf [1.27.22] Bashir, S., Lockheed, M., Ninan, E., Tan, J., 2018 _. Facing Forward: Schooling for Learning in Africa._ Washington, D.C.: World Bank Publications.

Checchi, D. and De Paola, M. 2017. “The Effect of Multigrade Classes on Cognitive and NonCognitive Skills: Causal Evidence Exploiting Minimum Class Size Rules in Italy.” IZA DP No. 11211. Bonn: Institute of Labor Economics.

Chin, Aimee (2005). Can redistributing teachers across schools raise educational attainment? Evidence from Operation Blackboard in India. _Journal of Development Economics_ 78(2), 0–405.

Cummings, Clare and Ali Bako M. Tahirou with Hamissou Rhissa, Falmata Hamed, Hamadou Goumey, and Idi Mahamadou Mamane Noura. 2016. “Collective action and the deployment of teachers in Niger: A political economy