rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8913

# **Abstract**

Mobile money lowers the costs of financial transactions, making it a promising tool for brining financial services to rural areas. This paper studies the effects of rolling out mobile money agents in poor and remote areas in Northern Uganda. The authors randomly assigned 329 areas to receive an agent in 2017, using 329 areas as a control group. Data from a 2018 household survey and administrative transactions data show little effect of the rollout. Mobile money transactions remained low, suggesting policies focused only on opening mobile money agents will not lead to transformative effects in poor and remote areas.

This paper is a product of the IFC-Mastercard Foundation Partnership for Financial Inclusion. 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 cwieser@worldbank.org and mbruhn@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 the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
,152|0.41|0.49|
|Years of schooling|70,923|7.56|4.94|

_Notes_ : The table presents the number of observations, the means, and standard deviations (S.D.) for demographics, intimate partner violence outcomes, attitudes outcomes, labor market outcomes, contraceptive use, fertility preferences and behavior, and husband’s characteristics. The samples in Panels A and B include ever-married women in the domestic violence module from the 2015-16 National Family Health Survey (NFHS), with Panel A presenting the summary statistics for the cross-sectional data and Panel B presenting them for the retrospective woman-year panel. The sample in Panel C includes ever-married men from the 2015-16 NFHS.

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# **I. INTRODUCTION**

Among other factors, transport connectivity is an essential part of the enabling environment for sustained and inclusive growth. In developing countries, particularly in Africa, the vast majority of farmers does not have good access to the local, regional, or global market, and depends on subsistence farming with few advanced inputs. Limited connectivity is a critical constraint in accessing social and administrative services, especially in rural areas where the majority of the poor live. Africa’s manufacturing and other local businesses are also lagging behind in the global market, except possibly for a few textiles, agrobusiness, and mining activities (Dinh et al. 2012).

The literature is supportive of the importance of transport connectivity. In the short term, transport costs and travel time can be reduced by improved road conditions (Lokshin and Yemtsov 2005; Danida 2010). Over the longer term, agricultural productivity will be increased (Khandker, Bakh, and Koolwal 2009; Bell and van Dillen 2012), and firms will become more profitable with more jobs created (Mu and van de Walle 2011). Poverty will then be alleviated (Dercon, Hoddinott, and Woldehanna 2008; Khandker and Koolwal 2011).

However, somewhat surprisingly, there are few global indicators that measure transport connectivity systematically. The traditional measurements are the total length of roads and the percentage of paved roads. The original data were sourced from the International Road Federation and were included in the World Bank’s World Development Indicators until recently. However, these indicators are only collected at the national level, and it has long been argued whether they are actually meaningful for measuring development efforts made on the ground, because they cannot provide any granularity of local connectivity, and the
# **I. Introduction**

**Rising energy prices over recent years demonstrate that many households are vulnerable to energy poverty.** The recent energy crisis in Europe raised concerns about energy affordability, especially among low-income households. Households with lower disposable income might spend a higher share of their overall income on energy. Therefore, they might be more affected by the energy crisis or rising energy prices, more generally speaking, than high-income households. The fact that poor people spend a larger share of their income on energy and might even decrease their energy usage in response to rising prices is often described by the concept of energy poverty. Energy poverty has negative welfare implications for poorer households. Energy poverty might result in severe health deterioration, especially in cold climates and during winter months. According to data by Eurostat, nearly one-tenth of the population in Europe, a total of 35 million people, is affected by energy poverty (European Commission, 2023).<sup>2</sup>

**Tackling energy poverty is one of the commitments of the European Union (EU), but identifying the energy poor is challenging, partly because of data limitations.** The EU targets both the mitigation and reduction of energy poverty (European Commission, 2023). To achieve this goal, policymakers apply a variety of tools, such as increasing energy efficiency, decarbonization strategies, and clean energy transitions. In addition, to facilitate knowledge exchange and good practices on energy poverty, the EU has launched the Energy Poverty Advisory Hub, the Horizon 2020 Energy Efficiency Calls, the LIFE Clean Energy Transition Program, and other initiatives. Still, identifying those who are most vulnerable to energy poverty remains challenging, both due to a lack of common definitions and reliable data sources (Robayo-Abril and Rude, 2023a). One important limitation is that monetary measures of energy poverty rely mainly on data gathered via household budget surveys (HBS), which often consist of small sample sizes and are only representative at high geographic levels.
two persons engage in collecting consumption data and three persons collect non-consumption data for two weeks in one PSU. Under these assumptions, we have _τ_ = 3 _/_ 5 = 0 _._ 6. Given these examples, we use _τ_ = _{_ 0 _._ 06 _,_ 0 _._ 36 _,_ 0 _._ 66 _}_ as a plausible set of values to consider.

# **Some empirical values of** _c_

Compared with _τ_ , there are more studies that provide us with some empirical values of _c_ . For Demographic and Health Surveys, Aliaga and Ren (2006, Table 3.1) present an estimate of _c_ based on past eight surveys. The value varies from 10 in Cambodia and Uganda to 52 in Togo. For consumption surveys, we are not aware of a study that provides cost comparisons from multiple surveys. Therefore, we derive the estimates of _c_ from existing studies.

Let us start with Pettersson and Sisouphanthong (2005, p.275), who provide the cost ratio, or the ratio of the cost of adding a PSU to the cost of adding a household, for the third Lao Expenditure and Consumption Survey (LECS-3) conducted in 2002-2003. This ratio is nothing but _c_ in our notation. They report _c_ = 3 _._ 9 in urban areas and _c_ = 6 _._ 1 for rural areas. The lower cost of _c_ in urban areas reflects the lower cost of travel to move between primary sampling units in the urban areas.

A few cautions are in order here. First, travel costs and field allowances are included in the calculation of _c_ but the permanent staff salaries are excluded. As Pettersson and Sisouphanthong (2005) claim, the cost ratio may be affected only slightly by the omission of salaries as the omission will have rather similar effects on both the denominator and numerator of the ratio.

Second, as noted by Pettersson and Sisouphanthong (2005), the cost ratios in
**Estimating Poverty in India without Expenditure Data** A Survey-to-Survey Imputation Approach<sup>_∗_</sup>

> _∗_ The authors thank Joao Pedro Azevedo, Benu Bidani, Urmila Chatterjee, Paul Corral, Francisco Ferreira, Haishan Fu, Dean Jolliffe, Christoph Lakner, Daniel Mahler, Rinku Murgai, Minh Nguyen, Sutirtha Roy, Carolina Sanchez-Paramo, Benjamin Stewart, Roy Van der Weide, and Nobuo Yoshida for invaluable suggestions, help, and support. This paper was previously published as a working paper entitled ”Nowcasting poverty in India for 2014-15”, which is available at: http://documents.worldbank.org/curated/en/294251537365339600/Nowcasting-Poverty-inIndia-for-2014-15-A-Survey-to-Survey-Imputation-Approach

> _†_ World Bank Group, Washington DC, and IZA., Bonn.

> _‡_ Ahmedabad University, Ahmedabad and World Bank Group, Washington DC.

1
To compare the 2009 results with those of 1999, we used the 1999 SAE poverty rates from Minot et al. (2003).<sup>2</sup> These are based on the 1998 VLSS and a 33% sample of the 1999 VPHC. The survey is implemented by GSO with funding from the Swedish International Development Agency and UNDP and with technical assistance of the World Bank. It includes 6,000 households. The 33% sample of the 1999 census includes 5,553,811 households.

# **4. The 2009 poverty map**

# **_4.1 Consumption models_**

The first step in the poverty mapping method is to select common explanatory variables in the census and household survey. After carefully screening the questionnaires and examining the data (comparing summary statistics), we have 20 household variables (Table A.1 in Appendix). We also constructed commune level data from the census and merged these variables with the household survey. For example, we constructed the percentage of Kinh people per commune, the average household size of communes, the proportion of households per commune having motorbike, etc. Note that these variables are comparable across the census and survey by construction. The total number of explanatory variables for estimation of the expenditure model is 39.

We estimate six separate regressions of log of per capita expenditure for six regions. To allow for the difference in coefficients between urban and rural areas, we interact the urban variable with all the remaining explanatory variables.

A forward stepwise technique is used so that only variables which are significant at least at the 5% level are kept. We select explanatory variables that are robust in explaining expenditure. This means that these variables have unchanged signs and are significant when the models are changing. The regression results of the large models are presented in the tables in Appendix I. The R-squared is very high. The Mekong River Delta has the lowest R-squared

> 2 In Section 6 we note that caution must be exercised in comparing poverty
**Figure 2. The tasks content of jobs across countries, US-based indexes.**

<!-- Start of picture text --> Non-Routine Analytical, US Non-Routine Interpersonal, US Routine Cognitive, US<br>AM GEGEGEAMGEGE AM GEGEGEAMGEGE COCOCOCOCOCOCO<br>PHPHPHPHPHPH<br>VN GHGHVNGH GHBOVNVNLKVNPHLKPHBOBOBOVNBOBOPHBOLKLKPHBOPHBOPHBOLKPHBOLKPHBOPHLKLKLKPHBOPHBOLKPHLKPHBOPHLKLKLKLKRSLKLKRSCOCORSCORSCOCORSCORSRSCORSRS VN GHGHVNGHVN VNVN GHBOVNVNLKVNPHLKPHBOBOBOVNBOPHBOLKBOLKPHBOPHBOPHBOLKPHBOLKBOPHLKLKLKBOBOLKLKBOLKLKLKLKRSLKLKRSCOCORSCORSCOCORSCORSRSCORSRS VN GHGHVNGHVNVNVNGHBOVNVNLKVNPHLKBOPHBOBOBOVNBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKBOLKPHPHBOLKPHLKPHBOGEPHGELKGELKAMGELKGELKRSLKLKRSRSRSRSRSRSRSRS<br>VN VN AM<br>VN<br>750 800 850 900 950 750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points GDP level, log points<br>Routine Manual, US Non-Routine Manual, US<br>VNVNVN<br>VNVN VNVNVN<br>VN VNVN<br>GHGHVN GH GHBOLKVNLKPHBOPHBOBOBOVNBOPHBOLKLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKPHLKBOPHPHLKLKLKLKRSLKLKRSRSRSRSRSRSRSRS VN GHGHVN GH GHBOLKVNLKPHBOPH BO BOVNBOPHLKBOLKPHBOPHBOPHBOLKBOPHLKBOPHPHLKLKLKBOPHPHBOLKLKBOLKLKLKLKRSLKLKRSRSRSRSRSRSRSRS<br>COCOCOCOCOCOCO PHPHPH<br>AM GEGEGEAMGEGE AM GEGEGEAMGEGE COCOCOCOCOCOCO<br>750 800 850 900 950 750 800 850 900 950<br>GDP level, log points GDP level, log points<br>.2<br>.2 .2<br>0 .1<br>0<br>0<br>-.2<br>-.2<br>Skill index Skill index Skill index -.1<br>-.4 -.4<br>-.2<br>-.6 -.6 -.3<br>.6 .8<br>.6<br>.4<br>.4<br>.2<br>.2<br>Skill index Skill index<br>0<br>0<br>-.2 -.2<br><!-- End of picture text --> Note: Each point shows the skill content of jobs using O*NET-based indexes per country and year. The horizontal axis measures GDP per capita in PPP from WDI.

When looking at the total changes in the task content of jobs in all 11 countries, there
speci...c heterogeneity in entry costs and demand.

In addition to the work cited above, this paper is related to recent research using aggregated ...rm-level data to provide some evidence on how colonial ties and linguistic similarity in‡uence trade ‡ows. Crozet and Koenig (2010) decompose industry-level bilateral exports in France into the number of exporters and the average exports per ...rm, and employ each of these variables as the dependent variable in a gravity model. Their estimates suggest that a common language and colonial ties increase both the number of exporters and the average exports per ...rm.<sup>3</sup> Anderson (2007) uses data from Sweden to investigate the link between bilateral trade and familiarity (captured by dummy variables for Nordic, Baltic and English-speaking nations). He reports a positive relation, due primarily to the extensive margin.<sup>4</sup> Our paper di¤ers from this line of research in several important respects, however. First, in addition to studying the role of colonial ties/common language, we provide evidence on whether and how migrant networks promote trade. Second, in doing so we use theory-grounded measures of export participation and intensity at the level of the individual producer (linked to information on ...rms’labor productivity and size). Third, and most importantly, we exploit the unique historical background underlying the generating process of our data to provide evidence that migrant networks constitute an important source market and ...rm-speci...c heterogeneity in entry costs and demand.

This paper is also related to recent work using country and product-level data to reexamine the microfoundations of the gravity equation in the context of heterogeneous-...rm models, including Helpman et al. (2008) and Baldwin and Harrigan (2011). Relative to this strand of work, a key distinguishing feature of our empirical analysis is the ability to link information on emigration stocks with detailed data on
in emerging markets. They not only provide an alternative estimate of long-term spreads, but also extend the sample for Mexico to cover the Mexican crisis (though they exclude Uruguay). We use the predicted term premium because it most directly captures the cost of issuing long-term debt relative to short-term debt. This variable is computed by regressing ex-post excess returns on 3-year and 9-year spreads and then obtaining the predicted value. As before, we compute robust standard errors using the country and crisis indicators as clusters.

The Tobit estimations are reported in Table 7. The estimations show that short-term issues are hardly affected by any of the conditioning variables; only the 3-year spread is statistically signi fi cant at the 10 percent level. However, medium- and long-term issues do show clear cyclical patterns. The conditioning variables become more statistically signi fi cant and the point estimates negative 27
), and 5 for G (Corporate governance, Management practices, Internal controls & audit, Compensation, Innovation).

- v. Manage missing data,<sup>9</sup> use imputation when possible (in line with the OECD-JRC Handbook on Constructing Composite Indicators methodology; OECD-JRC, 2008). In the case of filter questions, answers are imputed to replace some missing values when this is logically straightforward. Answers in the Enterprise Survey can be binary (yes/no), discrete (when various answers are possible), or continuous (a percentage for instance). Others were transformed into percentages.

- vi. Reduce the 60 ESG-related initial questions to 40, for one of the following reasons: i) too many missing data: if more than 55% of answers are missing, (e.g. because some questions are not asked from all firms due to eligibility or based on the sector they belong to), the component is excluded; ii) redundancy of some of the questions: if highly correlated with other variables/questions, they are not adding relevant information.

- vii. Transform the variables (21 related to E, 6 to S and 13 to G for a total of 40) into z-scores with mean 0 and standard deviation 1 (a z-score of 1.0 would indicate a value that is one

> 8 “Environmental awareness” is related to monitoring and target setting; “Green management” is related to the strategic objectives, to managerial responsibility of green targets and to green certifications; “Green measures” are the investments done by firms to improve their environmental impact. The latter can be _pure_ green investments (energy management, waste minimization, air pollution control measures, more climate-friendly energy generation on site, etc.) or _mixed_ green investments (that reduce the environmental impact as a byproduct of achieving other objectives: heating and cooling improvements, machinery and equipments upgrades, upgrade of vehicles, improvements of lighting system, etc.) (EBRD,
# **B. Data**

One challenge in collecting data for the evaluation has been that PW beneficiaries within communities— people who would be working on specific CfW subprojects—were not pre‐identified. Instead, they were selected on the first day of the subproject, largely on a first‐come, first‐served basis, subject to meeting the eligibility criteria. This precluded the possibility of simultaneous baseline data collection in the control and treatment households.

Data were collected in three phases: (1) data collection for beneficiary tracking, (2) data collection through unannounced site visits at CfW projects in the treatment communities, and (3) an endline household survey. All data collection was carried out by Sierra Leone’s national statistical agency, Statistics Sierra Leone. The administrative data that were used in the community (subproject) randomization process were collected and maintained by NaCSA for operational and monitoring purposes.

To enable the documentation and follow‐up on treatment and control households, beneficiary tracker surveys were administered on the first day of implementation for CfW treatment (April 2012) and control (July 2012) subprojects. These trackers were the basis on which treatment and control households were identified and interviewed for the endline survey (July–August 2012). The tracker surveys collected basic demographic information from beneficiaries, contact details so they could be tracked during subsequent survey rounds, and information on beneficiary perceptions of the program and its processes prior to beginning work at the sites. The surveys were administered at 276 sites among a total of 17,670 potential beneficiaries.

Unannounced observational visits were fielded midway through the physical implementation of the subprojects at 141 of the 143 treatment sites (May–June 2012).<sup>14</sup> This survey was conducted in two parts that were designed to collect information on overall program implementation and specific processes, as well as the program knowledge and satisfaction among beneficiaries. Part 1 consisted of observations (including a worker roll call, checks of attendance, and other
# **Appendix A** . **Implementation of the CEQ Fiscal Incidence Methodology in Niger**

# **CEQ Methodology**

The general objective of the CEQ methodology is to assess the impact of a state’s fiscal policy and its public spending on household welfare. This method seeks to identify which households are bearing the burden of taxes, and which are benefitting from state social spending. The method uses two types of data: **_macrodata_** from the state budget and national accounts, and **_microdata_** from household surveys. Eligible households are allocated the amount of social spending they have received and the taxes they have paid, using institutional criteria and household survey data. When information is not available from a household survey, information from other sources, like the government budget, may be used. The analysis uses various income concepts to measure the implications of each fiscal intervention.

**_Market income_** is the prefiscal income, or an individual’s income _before_ any fiscal intervention. It includes gross income from labor and capital, self-production, and private transfers. **_Net market income_** is constructed by deducting direct taxes paid from the market income. The direct taxes included here are taxes on salaries and wages. **_Disposable income_** is obtained by adding direct transfers to the net market income. Household consumption is assumed to be equal to disposable income. That assumption is often used in low-income economies, where the data available on consumption are more reliable than data on income and savings. Other income concepts are calculated using a backward-and-forward approach. The direct transfers considered are scholarships and other support to students, and school feeding programs. The beneficiary households are directly identified in the microdata for the former, and they are randomly selected on the basis of the rules and the geographical areas covered by the program for the latter.

When indirect subsidies are added to disposable income, and indirect taxes are deducted, the result is **_consumable income, or
9 rare case,, (a female heavy smoker over age 35), however, the mortality risks of contraception are s:bstantially lower than those of childbearing in developed and especially in developing countries. Moreover, the risks associated with contraceptive use can be further reduced by judicious choice of, and switching between, contraceptive methods.

# III. IMPACT 3F ORGANIZED FAMILY PLANNING PROGRAMS ON MATERNAL HEALTH

Here we attempt to summarize what is known about the actual and potential impact on maternal health of organized family planning programs, and the implications of that knowledge for policy and for programs.

If all women saying they want no more children and currently not using effective contraception, were able with family planning to avoid all future births, about a quarter of all maternal deaths would be prevented (Maine et al, 1987). For every death prevented, at least ten women would avoid serious health impairment from pregnancy-related illness and complications (Harrison, 1987). But what do we know of the actual impact of family planning use on maternal health? And what are the program implications of what we know, in other words, how can we narrow the gap between the potential and actual contribution of family planning? Data from the 61 countries which have conducted World Fertility Surveys (WFS) and Contraceptive Prevalence Surveys (CPS) in the last decade or so provide important clues to these questions (Population Reports, 1985). We turn now to a consideration of the main characteristics affecting use of contraception, focusing mainly on the woman, and the extent to which users belong to those groups most at risk from pregnancy.

# Accegtor Characteristics

There are three main groups of clients for family planning services: those seeking to delay the first pregnancy; those wishing to space children ("spacers"); and those who wish to prevent additional pregnancies ("limiters"). Overall, there are more "limiters" than "spacers" in developing countries. WFS/CPS data demonstrate that about one quarter of contraceptive users in Asia we