rafmacalaba/data-use-mentions-extended huggingface dataset:
to the ECD intervention (Behrman, Cheng and Todd 2004, Gertler 2004, and Frankenberg, Suriastini and Thomas 2005 are recent exceptions).

In this paper, we estimate the impact of an important ECD initiative of the Philippine government.<sup>1</sup> The overall aim of the program, initiated in the late 1990s, is to enable local governmental units (LGUs or municipalities) within three regions (comprising 13 provinces) to deliver a broader and better set of ECD-related services to pregnant women and children under age seven. Here we address the research question: Did this ECD program have a positive effect on children’s well-being in the form of better cognitive, social, motor, language development and health and nutrition? We use longitudinal survey data collected in three rounds over three years on 6,693 children in randomly selected households. The surveys covered two “treatment” regions that received the ECD program and a “control” or comparison region that had not received the program. Program assignment was non-experimental and so we use difference-in-difference ________________

> 1 Although the program is being coordinated by the Department of Social Welfare and Development (DSWD), this government program is an interdepartmental program that also involves the Department of Education and the Department of Health.
<mark>understand cross-country differences in functionally equivalent organizations (Gingerich 2013).</mark> As a complement to existing measures of comparative bureaucracy, direct survey measures of the experiences and perceptions of bureaucrats themselves provide an analogous source of data to household and enterprise surveys, but for the public service. <mark>This paper introduces the Global Survey of Public Servants (GSPS), a global initiative to harmonize large-scale comparable micro survey data from public servants around the world (accessible at: https://w</mark> <u>ww.globalsurveyofpublicservants.org</u> <u><mark>/). The GSPS was founded by the authors of</mark></u> <mark>this paper – researchers at or affiliated with the World Bank Bureaucracy Lab, the Stanford University Governance Project, University College London, and Nottingham University.</mark> The intention of the GSPS is first simply to encourage further surveying of public officials around the world. However, publishing data at a more granular level than has been available to date allows for research on questions of the state that are complementary to what has been undertaken to date.

<mark>To contextualize the GSPS, this paper introduces a conceptual framework of the survey, which reflects the dual academic-practitioner aims of the initiative. Such a conceptual framework is required given how limited the conceptual consensus is in any literature on the drivers of state capacity and administrative quality. The GSPS’s objective is to improve scholarly understanding of how public administrations and states around the world work and to help governments manage public servants better. As such, we require a theoretical lens that allows us to make choices on what measures to include in our effort.</mark> <mark>The paper then details the methodological approach of the GSPS and current state of play of the GSPS dataset (</mark> accessible at: <u>https://www.globalsurveyofpublicservants.org/indicators/<
themselves or the household” or “helped without being paid in any kind of business run by the household”, but in other surveys, “running or helping” in a business is asked in one question. The ILO LFS pilot study findings also recommend maintaining separate questions for running versus contributing family work in a business, to better identify household members’ roles (Benes and Walsh, 2018a), although similar care needs to be taken in the guidance and framing of questions to ensure that respondents do not default to traditional gender norms where women are always considered as “helping” in the business and men are “running” the business, even if their roles are similar.

## Summary and ways forward

Rural women’s employment can be underreported in employment surveys, owing to multiple factors including their concentration in informal and seasonal work, social norms about how they view their labor contributions, and other domestic responsibilities they are engaged in. Box 3 summarizes key points highlighted in this section.

Box 3. Summary: Ways forward on counting rural women’s work

- Boundary questions that distinguish between market- and own-use production in agriculture are crucial under the 19th ICLS. Further exploration is needed on adapting these questions to account for seasonality, as well as proxy reporting.

- Within off-farm wage work, amid shrinking land ownings and rising seasonal work on farms, asking about traditional forms of casual labor on others’ farms that may be missed by standard questions on wage employment is also important (as in the case of _ganyu_ in Malawi, where rural women wage workers across most age groups are almost entirely concentrated).

- Greater attention is needed to how questions are framed around self-employment, to identify individuals running a farm/enterprise separately from contributing family work.  Owing to social norms and other time burdens, rural women are more likely to underreport this work.  Recovery questions have also been shown to be particularly effective in re-classifying women as contributing family workers, when they initially report not being employed.

# III.
our sample of emerging markets allocate around half of their total credit portfolio to firms whose business processes are highly or very highly dependent on one or more ecosystem services.<sup>11</sup> This metric appears to be negatively correlated with the countries’ income level (Figure 3). In lower-middle-income economies, banks allocate on average 55 percent of the loans to firms subject to potential financial losses due to a deterioration of ecosystem services. Within this group, countries with the highest dependency are Mauritius (73 percent), Pakistan (60 percent), and Indonesia (52 percent). In upper-middle income countries the dependency is lower, at 47 percent on average. Additionally, upper-middle-income countries show a lower dispersion compared to other country groups, ranging from 56 percent in Argentina to 43 percent in the Russian Federation. High-income countries in our sample show on average the lowest direct exposure to nature loss, with 45 percent of banks' credit portfolios directed to firms highly or very highly dependent on one or more ecosystem services. Within that income group, Hungary exhibits the highest dependency at 52 percent and Chile the lowest at 37 percent.<sup>12</sup>

> 11 We use the high or very high to one or more ecosystem services metric to allow for comparability with available literature. We present alternative metrics in Table 5 in the Appendix.

> 12 As highlighted before, this paper is based on publicly available data, and thus the results may only be interpreted as a broad indication that would require more in-depth and detailed assessment, relying on unpublished data from local authorities. For instance, Banco de Mexico estimates a comparable dependency metric at 36.5 percent using

6
# **“She Helps Me All the Time”: Underestimating Women’s Economic Engagement in Rural Honduras**<sup>**1**</sup>

Miriam Muller<sup>2</sup> and Liliana D. Sousa<sup>3</sup> JEL Classification: J16, J21, J43 Keywords: labor markets, gender, labor force participation, survey design, rural, Honduras 1 The opinions, interpretations, and conclusions expressed herein do not necessarily reflect the views of the World Bank, its Board of Executive Directors, or the Governments they represent. The authors gratefully acknowledge contributions from a large team, especially Giselle del Carmen who was critical in overseeing the field work, and Ursula Casabonne and Gaia Hatzfeldt for their valuable analytical inputs. Martha Patricia Jaen and Nadine Lacayo were key in the qualitative survey collection and data processing and Judit Abarca transcribed the interviews. Andres Cesar performed empirical analysis of the household survey and Marilia Magalhaes assisted with the literature review. The team worked under the guidance of Oscar Calvo-Gonzalez and Ximena Del Carpio, Friederike Koehler, and Seynabou Sakho. The team benefitted greatly from comments received from Ramon Ernesto Arias, Masako Hiraga, Amparo Palacios-Lopez, and Akiko Sagesaka. We thank the staff of the Projects PROSASUR and ComRural, including Wilmer Sanchez and Francisco Posas, the corresponding coordinators, and Augusto Garcia (team task leader), who facilitated entry in the communities in Honduras for the purpose of data collection. Ana Funes, Pamela Gaye Gunio, and Noris Salinas (Program Assistant) and provided support throughout the implementation of the study. This study was partially funded by Trust Fund for Statistical Capacity Building (TFSCB). Finally, our deepest gratitude to the key informants and to the women and men who shared their personal stories with us. 2 World Bank. E-mail: mmuller1@worldbank.org 3 World Bank. E-mail: lsousa@worldbank.org
# **Box 3.2: Benchmark regressions on servicified and non‐servicified firm performance**

To perform a more formal analysis of whether some types of firms are more productive than others in Turkey, the EIS firm‐level data set was used to perform benchmark regression analysis. By applying dummy variables for all but one category that is omitted in the regression equation, an empirical conclusion can be reached on whether the included categories are positively or negatively (non) significant against the omitted variable category. The following equation was used: in which TFP stands for the productivity measure as defined by Ackerberg et al. (2015). CAT denotes the vector of types of firms separated by four categories, namely goods firms, servicified goods firms (i.e. both non‐exporters), goods exporters, and servicified goods exporters. Based on which category is omitted, the other three categories are included in the regression equation (1).

Since we are dealing with firm‐level data, but cannot include firm‐fixed effects as this would wipe out the variable of interest as well as the dependant variable, we include firm‐level control variables in the vector FIRM, which are the size of the firm, whether the firms is a “Kurum” (which is a bigger type of enterprise), and whether the firm is more capital‐intensive. Moreover, we also apply sector (γ) as well as time (δ) fixed effects while standard errors (ε) are clustered by sector‐year.

# **Figure B3.1: Results of benchmark regression analysis (2007‐2014)**

<!-- Start of picture text --> Benchmark against goods firms Benchmark against goods exporters<br>24.6<br>13.5<br>-8.8<br>-17.7 -19.7<br>-34.0<br>Servicified Goods exporter Servicified exporter Servicified exporter Goods Servicified<br
incorrect comparisons coming from data constraints. Loosely speaking, this indicator yields the largest “signal-to-noise ratio” when filtering available data.

The review focuses on global multidimensional poverty measures. First, multidimensional poverty measures are arguably the multidimensional indicators most used for policy making, perhaps because of the simplicity of the Alkire-Foster (AF) methodology. Second, global applications face the most stringent data constraints. However, most of the improvement opportunities we identify still make sense at the national or regional level. The conceptual framework proposed can also guide well-being measurement practices beyond poverty.

We identify three sources of potential improvements: “wasting” less of the available data, better filtering the data used, and better harnessing the added-value of multidimensional poverty measures. The first two sources relate to the construction of the indicator while the third source relates to the use of the indicator.

The main source of potential improvements relates to “wasting” less data. Ironically, in spite of stringent data constraints, large amounts of relevant data are simply ignored. To a large extent, this problem originates from conceptual rigidities constraining the indices used to aggregate across dimensions. We identify three ways of reducing the current “waste” of data.

First, the AF methodology to identify the multidimensionally poor is based on dichotomous deprivation statuses, i.e., either being deprived or non-deprived in a given variable. However, not all variables are dichotomous. For instance, this is neither the case of consumption, which is a cardinal variable, nor the case of some health or education outcomes, which are captured by categorical variables. Dichotomozing such variables leads to a loss of information. We identify in the literature two conceptually sound solutions to reduce this loss of information and show how these solutions can be implemented even in the absence of information on the relevant preference.

Second, in spite of its large intrinsic value, longevity is ignored by all global multidimensional poverty measures. Mortality data are often available but these data are not used. One difficulty is that the joint distribution
employment for the population. About 60 percent of the country’s population relies on agriculture for their livelihoods (Oseni and Winters, 2009). The majority of industries and cities of the country are located in the South, while the North is more rural and less densely populated (Oseni and Winters, 2009), thus, it is not surprising to find a socio-economic divide in the country. In addition, poverty is found to be much more prevalent in Northern Nigeria than it is in the South and concurrently, the majority of the country’s middle class is concentrated in the Southern regions (Corral et al., 2015).

The analysis uses data from two rounds of the Nigeria General Household Survey Panel (GHS- Panel) conducted by the National Bureau of Statistics in 2010-2011 (round 1) and in 2012-2013 (round 2). The GHS-Panel which has a sample of 5,000 households is part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) project which aims to improve agricultural statistics in Sub-Saharan Africa. The GHS-Panel provides information on household agricultural activities, household’s human capital, other economic activities, and access to services and resources. Moreover, since the surveys were administered in two visits, namely: post-planting (Aug-Oct 2010; Sept-Nov 2012) and postharvest (Feb-Apr 2011; Feb-Apr 2013), it contains data on farm and non-farm activities for the corresponding season. High resolution precipitation records for the 2001-2009 periods are derived from the National Oceanic and Atmospheric Administration (NOAA) climate prediction center. The latter are geo-referenced amounts for the households in the GHSPanel sample.

The observational units for this analysis are Nigerian farming households that are present during both survey rounds,<sup>7</sup> in total we have 2,232 farming households<sup>8</sup> . The outcome of interest is a dummy variable that equals 1 if the household diversifies its sources of income. In order to construct this indicator, various modules from the GHS-Panel
by assessing the impact of digital tools on the inclusivity of citizen engagement during the COVID-19 pandemic, Hofstra et al. (2023) found that digital tools, including interactive platforms, facilitate citizen participation.

However, the consensus on the impact of digital platforms on citizen engagement is also debated and notable caveats exist. David (2017) explored both the theoretical and empirical literature on civic engagement in the context of e-government and found that citizen engagement through e-government has the same shortcomings and caveats as citizen participation, and the use of ICT itself brings with it additional challenges. According to this study, quality control measures must be followed to use ICT for civic engagement to produce the expected benefits. Similarly, Lee-Geiller and Lee (2019) conducted a qualitative meta-analysis of four strands of literature and developed the Democratic E-governance Website Evaluation Model. From the literature review they conducted, they found that governments have used their websites mainly for nascent patterns of communication, particularly information sharing and communication between the citizens and their governments. Spada et al. (2015) studied the effects of technology on citizen engagement through online voting in Brazil, finding that technology appears only more likely to engage people who are younger, male, of higher income and educational attainment, and more frequent social media users.

Other literature has studied the contextual factors needed to ensure that digital technologies and platforms translate into improved citizen engagement. World Bank (2022) highlights the potential of e- government to make the interactions between government and citizens easier by allowing governments to reach more people and provide a wide range of information. However, the study identifies key foundations needed to be in place for such initiatives to succeed, such as digital infrastructure availability, digital skills of public servants, as well as the legal and regulatory framework. Gulati et al. (2014) assessed the relationship of political factors and policy initiatives to improved e-government services and e- participation capabilities using the online service index and the e-participation index of the United 4
may have a differential impact on the household’s accumulation of assets, compared with households without an absent member.<sup>16</sup> For these reasons, it is more appropriate to examine the broader impact of migration on outcomes for households rather than the impact of remittances alone. The analysis in this paper follows this reasoning, analyzing the impact of migration, instead of remittances, on the assets accumulated by households that have sent family members to work outside Kerala.

# **4. Data and Empirical Strategy**

# _A. Data_

In 1998, the first Kerala Migration Survey was completed in Kerala, India. A statewide, representative, demographic survey of 9,995 households across 200 communities ( _panchayat_ ), it was administered between March and November of that year. The KMS was the first in a series of periodic household surveys undertaken by the Center for Development Studies, headquartered in Trivandrum, Kerala. Five years after the first KMS survey, in 2003, the Center for Development Studies conducted a second wave of the KMS covering 10,012 households. A unique feature of the 2003 KMS survey was the creation of panel data: 5,000 households from the 1998 survey were selected for revisits, and the survey team successfully completed 4,795 follow-up interviews, achieving a high re-contact rate of 95.9 percent.<sup>17</sup> Both the 1998 and 2003 KMS survey waves asked whether any household member had been abroad or in another Indian state at any time prior to or during the survey. Survey teams recorded each of these household members’ current and pre-migration occupational status, together with the month and year of their departure from and return to Kerala. These questions from the 2003 KMS

> 16 In Kerala, people frequently refer to “Gulf Wives Syndrome.” This is a way of indicating the plight of a woman whose husband has left to work in the Arabian Gulf, leaving her with a stream of money at the emotional cost of separation and
# **How reliable and consistent are subjective measures of welfare in Europe and Central Asia?**

**Evidence from the second Life in Transition Survey**

**Alexandru Cojocaru and Mame Fatou Diagne**<sup>**1**</sup>

**Keywords:** Subjective well-being, Life in Transition Survey, Transition Economies

**JEL Classification** : I31, P36

**Sector Board:** POV

> 1 The World Bank. 1818 H Street, NW, Washington D.C., mfdiagne@worldbank.org and acojocaru@worldbank.org The findings, interpretations and conclusions in this paper are entirely those of the authors and not those of the World Bank, its Executive Directors, or the countries they represent. We are grateful to Benu Bidani, Carolina Sanchez Paramo, Kathleen Beegle, Nobuo Yoshida, Dean Joliffe, Ken Simler, Maria Davalos, Nithin Umapathi, Alaka Holla, Kirsten Himelein, Erwin Tiongson, and the participants of the Poverty and Inequality Measurement and Analysis Group (PIMA-PG) for helpful comments and remarks.
, Nigeria, and Uganda), which were collected as part of the World Bank’s Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA).<sup>21</sup> In each country, households are selected in a nationally representative way and tracked over time, such that we can use a panel dataset consisting of three rounds of surveys in Malawi (2010-11, 2013-14, 2016-17) and Nigeria (2010-11, 2012-13, 2015-16), and four rounds in Uganda (2009-10, 2010-11, 2011-12, 2013-14).<sup>22</sup> We focus on households that engage in

> 20The same argument applies to the concern that farmers’ demand for advice could also be explained based on a similar model using time rather than attention as the limiting factor, where for some types of non-agricultural shocks (e.g., illness of a family member) the opportunity cost of time would increase and thus demand for agricultural advice would tend to decline.

> 21The same data are used by Deininger et al. (2017), Gollin and Udry (2017), and Sheahan and Barrett (2017).

> 22Detailed information about the design and implementation of the surveys in each country can be found at World Bank (2018). While the LSMS-ISA database covers more countries, only the data from the three countries we use here allow for separately identifying extension contacts that were actively

14
had a waiting list. Each municipality registered vulnerable families and was allocated a budget based on poverty estimates from the Brazilian Institute of Statistics (Instituto Brasileiro de Geografia e Estat ́ıstica – IBGE). The process of including new beneficiaries from the waiting list as spots became available followed a centralized procedure led by the Ministry of Social Development (MDS).

There were several reasons why families exited the program under the 2019 regulations and many of those still apply under the new program rules. The first related to changing household demographics: once children reached adulthood, families could only receive the basic benefit, and if their income exceeded the extreme poverty threshold, they were excluded from the program altogether. Second, if families failed to meet health and education conditions without sufficient justification, they were removed from the program after being offered opportunities to comply. Third, households were required to withdraw the cash benefit within a certain period; failure to do so resulted in exclusion from the program. Finally, household income had to remain below the eligibility threshold, though with one important exception: the _Regra de Permanencia_ (permanence rule). This rule allowed beneficiaries to remain in the program for an additional two years and maintain their income above the eligibility line (up to certain limits), provided 6