rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Conclusion**

The goal of this paper is to showcase the power of paradata in generating operationally relevant insights to assist in designing household surveys and improving the quality of survey data collection in low- and middle-income countries. The analysis utilizes the timestamped paradata for the nationally representative household surveys that were implemented by NSOs in Cambodia, Ethiopia and Tanzania between 2018 and 2020. Each survey coupled a multi-topic socioeconomic household questionnaire with an individual questionnaire that was attempted to be administered to adult household members in private and that elicited in-depth individual-level data on a range of topics, with a strong focus on labor and asset ownership. Our paper conducts a range of analyses related to interview length that yielded interview length estimates at the module-, individual-, and household-level; unit cost estimates as a function of interview length and total survey budget; and estimates of interviewer effects on variation in module duration.

Module duration estimates constitute useful reference points for survey practitioners aiming to conduct similar data collection in comparable contexts. Often the decision concerning whether to include an additional module or an additional person to interview depends on how this would affect overall interview burden and overall cost. Due to the disaggregated nature of paradata, the smallest unit of analysis is an “event” which provides the time a specific question is asked and therefore allows us to construct various measures of interview length at the module, individual, and household levels. We provide duration estimates for each module per country as well as an estimate of average total time spent in the household, which ranges from 82 minutes in Cambodia to 120 minutes in Tanzania. The food consumption module is the longest module, on average, ranging from 22 to 26 minutes, depending on the country. Additional high duration household modules include non-food consumption, housing, and household roster. Although not done in this paper, one could potentially conduct an analysis at the question level to find ways to make the module more efficient in terms of time and the amount of information the module extracts.

For
the entire region in which they are located, because the population of the MVs is in all cases a tiny fraction of the rural population of each region.<sup>16</sup> Furthermore, the MV region trends in most cases are similar to rural trends that span each nation. Despite these weaknesses, the differences-in-differences estimates presented here are better measures of the true effects than simple differences.

> 14  We define “substantially less improvement” as an absolute difference of more than 5 percent points between the estimates by the two methods.  Because MVP (2010c) does not include standard errors for the indicator estimates, we are unable to estimates confidence intervals for either the before-versus-after or the differences-in-differences estimates. As a result, we cannot determine which estimates are statistically significant. Also note that “improvement” in the indicator means a positive change, except for stunting, for which a decline is improvement.

> 15 For example, Sanchez et al. 2007 note that a health clinic in Sauri receives some patients who are non-residents.

> 16 Sauri comprises 1.32% of the rural population of Nyanza Province, Kenya. Bonsaaso comprises 1.59% of the rural population of Ashanti Region, Ghana. Pampaida comprises 0.02% of the rural population of the Northwest Region, Nigeria. This calculation assumes: 1) 65,000 residents of Sauri, 35,000 in Bonsaaso, and 6,000 in Pampaida, as reported by the MVP (2010c), 2) each national government’s census-based regional population estimates: 5,442,711 for Nyanza Province, Kenya in 2009; 4,589,377 for Ashanti Region, Ghana in 2008; and  35,786,944 for Northwest Region, Nigeria in 2006, 3) the percentage of people defined as living in “rural” areas weighted by sampling weight in the most recent DHS survey data: 90.2% for Nyanza Province, Kenya in 2008; 48.0% for Ashanti Region, Ghana in 2008
and Melinda Gates Foundation, n.d.; OHCHR, n.d.; Plan International, n.d. ; UNICEF, n.d.; Woodon et al., 2018) and in Nigeria (see UNICEF, 2017; UNGEI, 2008; World Bank, 2018), education deprivation shares do not display noteworthy differences across gender neither in the rural nor urban areas, nor when comparing north to south, when solely considering national or regional figures, as displayed in Figure 6 d) to i) and Figure A.2 in the Appendix.<sup>22</sup> One exception is the higher share of rural girls aged 15-17 that has never been to school in the NW compared to boys, 20% versus 11% in wave 4. For the North East, the graph however displays the opposite.

As severe education deprivation however does not inform about the length of education and early dropout rates of those not deprived, estimates for the share of children not in school while of school going age are presented in Figures 11 to 15.

Apart from a rural – urban education deprivation divide, there is an even more extreme north-south divide, with children living in the North-East and North-West regions facing the highest though over time decreasing severe education deprivation rates, as displayed in Figure 7. Nevertheless, still even in the fourth GHS wave, close to 30 percent of children in the oldest rural age group in these regions compared to less than 1 percent of the children of this age group in the South-South have never received any education. While the pattern of the GHS and MICS estimates across regions is generally consistent, the GHS data suggests higher education deprivation rates for the rural North-West, whereas the MICS data suggests even higher rates in the rural North-East. This discrepancy may be related to differential access to conflict affected areas of the rural North-East by the survey teams of the GHS and the MICS survey, which may suggest that the temporarily inaccessible areas display notably higher education deprivation rates than
|44|-8|16|33|17|18|30|12|24|42|17|
|Azerbaijan|56|45|-10|24|16|-8|51|61|10|15|5|-10|21|12|-9|47|26|-21|

Notes: Ch. denotes change in satisfaction between 2006 and 2010. Countries sorted in order of average increase across all seven services. Data on satisfaction are not fully comparable between the 2006 and 2010 LiTS surveys. The 2006 survey asked respondents if ―you personally‖ used a service (and, conditional on use, whether you were satisfied); while the 2010 survey asked if ―you or anyone in your household‖ used the service.

16
Evidence from the Interstate Highway System " , The _Review of Economics and Statistics_ , 90(4): 683-701 Michalopoulus, Stelios and Elias Papaioannou (2013). " Pre-Colonial Ethnic Institutions and Contemporary African Development " , _Econometrica_ , 81(1): 113-152.

Pesaresi, Martino, Daniele Ehrlich, Stefano Ferri, Aneta J. Florczyk, Sergio Freire, Matina Halkia, Andreea Julea, Thomas Kemper, Pierre Soille, and Vasileios Syrris (2016). _Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014_ , JRC Technical Report EUR 27741 EN; doi:10.2788/253582 (online) UN Press Release (2013), " World population projected to reach 9.6 billion by 2050 with most growth in developing regions, especially Africa says UN " , 13 June 2013 UNDP (2017). “Income Inequality Trends in Sub-Saharan Africa: Divergence, Determinants, 29
health-related decision making. Thus, educating men about the reproductive process, disease prevention and the benefits of reproductive health care for both men and women seems to be urgent to reduce, for instance, the growing AIDS epidemic in India.

Men’s knowledge about contraception is important, but its use and effectiveness depend directly on men’s involvement. Several studies have examined the ways in which culture and social organizations influence contraceptive patterns. Research from Ghana (Ezeh 1993) and Nigeria (Bankole 1995) suggests high levels of influence by men over women’s contraceptive decisions. However, the reverse may not be true. Bankole’s analysis (1995) of the Nigerian Yoruba shows that the number of offspring has important consequences for apparent equality in spousal desire for more children. Additionally, there is evidence that men’s preferences have a major direct impact on the first decade of a marriage and the first four children. It seems that men are likely to want more children in families with few members and that women’s wishes prevail with larger numbers of surviving children in the family.

The importance of including men in policy design and research related to reproductive health has been highlighted by other researchers. Bankole (1995) and Dodoo (1993) suggest high probabilities of invalid estimates for unmet contraceptive need in sub-Saharan Africa when these estimates are derived from data collected only on women. In Zimbabwe, although men report having “the final say” in contraceptive use, women are responsible for obtaining contraceptives (Mbizvo and Adamchack 1991). These and other studies demonstrate discordance within couples in terms of contraceptive use. The main conclusion in previous research is that men agree on using contraception for birth spacing purposes but not to limit family size. The fact that men prefer more children than women suggests that reproductive health programs or policies in developing countries should involve both sexes.

Note that most of the conclusions in previous studies are derived from surveys in which individuals stated their preferences about contraception use and knowledge. Stated preferences may substantially diverge
This version: 15 Feb 2024 53 <mark>Dumont, Louis. 1966.</mark> _<mark>Homo Hierarchicus, essai sur le systeme des castes.</mark>_ <mark>Paris, Editions Gallimard.</mark> Easterly, William, Life During Growth, _Journal of Economic Growth,_ Volume 4, Issue 3, September 1999, pp. 239-275 Graham, C., and S. Pettinato. 2006. Frustrated Achievers: Winners, Losers, and Subjective Well-Being in Peru’s Emerging Economy. _The Annals of the American Academy of Political and Social Science_ , _606_ , 128–153.

Graham, Carol, Shaojie Zhou, and Junyi Zhang. 2017. "Happiness and Health in China: The Paradox of Progress," _World Development_ , 96(C), 231-244.

Harrison, Elizabeth, and Christian Seidl. 1994. “Perceptional inequality and preferential judgment: An empirical examination of distributional axioms,” _Public Choice_ , 79, 61–81.

Harrod, Roy Forbes. 1958. “The Possibility of Economic Satiety,” in _Problems of United States Economic Development_ . New York: Committee for Economic Development, Pp: 207-213 <mark>Henderson, J., D. Hulme, R. Phillips, and Noorul Ainur M Nur. 2002. Economic Governance and Poverty Reduction in Malaysia. Manchester Business School Working Paper. Vol. 440. 2002. 36.</mark> Hirsch, Fred. 1976. _Social Limits to Growth_ . Cambridge, Massachusetts: Harvard University Press (A Twentieth Century Fund Study). _Revised edition_ (1995) with an Introduction by Tibor Scitovsky, reprinted by Routledge, London and New York.

International Monetary Fund (IMF). 2024. International Financial Statistics. International Monetary Fund. Washington, DC. Accessed February 16, 2024.

<mark>Kanbur, SM Ravi, ed. 2003.</mark> _<mark>Q</mark> -squared, combining qualitative and quantitative
br>0.23|0.22|0.25|0.22|0.19|0.25|
|Joint<br>0.62|0.62|0.58|0.64|0.68|0.60|
|No. of cells<br>2148|161|532|538|414|503|

_Source:_ Rwanda Natural Resource Authority (RNRA), Land Administration Information System (LAIS). _Notes:_<sup>a</sup> Two residential and two agricultural parcels from Kigali with values above 5,000 $/m<sup>2</sup> have been dropped.

18
used in the regression reported below. The log and the squared log of the age of the head of household proxies for life cycle factors. A set of 0-1 dunmmies controls for the effect of various household characteristics on savings, comprising dummies for households located in rural areas, large households with more than four residents, households owning land, households owning productive assets, female heads of households, a set of dunmmies set equal to one if the highest level of education attained by the head of the household was primary, secondary or vocational (the reference group is tertiary education) and a set of dummies for the household position in the income distribution, measured in quintiles.

2 See Household Expenditure and Income Data for Transitional Economies (HEIDE), Appendix 2 of RAD project '?overty and Targeting of Social Assistance in Eastern Europe and the Former Soviet Union". Household savings rates are notoriously difficult to measure accurately even in mature market economies because of underreporting. While considerable effort has been made to purge the dataset from clear outliers, we additionally drop all observations with an implied dis-savings rate above fifty percent. The frequency distribution did not suggest outliers on the right-hand-side tail of the distribution, while all three samples contain several extremely large (of the order of minus several thousand percent) negative savings rates, which are eliminated by the threshold condition.
revenues of social insurance funds. Second, the SOEs which are receiving large subsidies from central government are a direct burden on the state. SOEs are not included in the general government fiscal data. However, under Eurostat rules, an SOE should be included in the general government data if more than half of its revenues are derived from government subsidies over a three year period. Around 30 SOEs are likely to be included in the general government budget in 2011, which will worsen the recorded general government fiscal balance by about 0.5 percent of GDP.

# **4. The fiscal challenges of an aging population**

# **4.1 Long-run demographic trends**

Romania’s long term demographic forecasts are a source of concern for both economic growth and public finances. The population has already fallen by about 10 percent since 1990, from 23 million to the current level of 21 million, of whom 2-3 million are working abroad. Romania’s population is forecast to shrink further, by 20 percent between 2008 and 2060.<sup>7</sup> The contraction in the population of working age over this period is even larger – about 35 percent – the second largest in the EU, after Bulgaria. The share in the total population of people aged 65 and above will rise from the current level of about 15 percent to over 30 percent by 2060, the third oldest in the EU.<sup>8</sup> The old age dependency ratio – the ratio of people aged 65 and above to the working age population is forecast to rise from the current level of about 20 percent to 65 percent by 2060 (figure 5).

Demographic trends of this nature are clearly an impediment to long-term growth. With the workforce contracting by an average of 0.8 percent a year over the next half century, it is unlikely that Romania will be able to sustain the 4 percent real GDP growth which is forecast for

> 7 The population has already fallen by about 10
to estimate the impact of exposure to conflict during primary schooling age on early marriage, e.g., on the probability of being married by the age of 15 years old.

It is also relevant to analyze the effect of exposure to conflict during core marriageable age (15 to 20 years old for Nepalese women) on long-term marital status. I therefore carry out a separate analysis considering the impact of exposure to conflict during marriageable age on marriage by age 21, at which over 80 percent of women are married (Figure 1).

I start by estimating Equation (1), where _Yijrt_ is now a dummy variable equal to one if the woman was married at age 15 or younger, and zero otherwise, and where _casualtiesj_ is the number of casualties (per 1000 inhabitants) during 1996-2004<sup>4</sup> . The only difference with the treated and control cohorts used in the primary education analysis is that individuals aged 5 at

> 3 More household characteristics such as female headship, asset ownership, and dwelling materials are available, but these are excluded as they are more likely to be caused by conflict.

> 4 The Western Calendar is used here for the convenience of the reader. In the analysis, the cut-off point is the end of Nepalese year 2060 (April 2004), which coincides with the bulk of data collection for the 2003/2004 NLSS.

17
father’s residency status at the time of the survey. The comparison between our sample using IHDS2012 to Azam and Bhatt’s sample using IHDS-2005 is documented in the online appendix Table A1.S. To generate matched daughter-father pairs, we follow Azam (2016) closely (see the Table 1 in Azam (2016)). Since Azam (2016) also use IHDS-2012 data, we can compare our sample precisely, as shown in the online appendix Table A1.D.

The summary statistics for our various estimation samples for IHDS-2012 (for intergenerational mobility estimation), and IHDS-2005 and NSS-1995 (for estimation of the investment equation) are reported in the lower panels of Tables T1.U (for the urban plus rural sample), T1.R (for rural sample). The mean education of fathers in urban areas is 6.64 years in the sons sub-sample, and 6.44 years in the daughters sub-sample of the main 16
- (e) Satisfaction with services {i}

- i. The last time you visited the clinic in the past one month, how satisfied were you with the care that you received at the clinic?

- ii. The next time you need medical attention for some other reason, would you visit [CLINIC] again?

- (f) The last time you visited the clinic in the past one month, how would you rate the attitude of the staff? {i}

# **7. Community Support**

- (a) Reported engagement index [standardized summary index of i-iii] {com}

- i. Health monitoring facility (HMF)/clinic monitoring facility (CMF) exists

- ii. Number of HMC/FMC meetings

- iii. Contributions to clinic (e.g. expenditures, nurse veg garden, etc.)<sup>A2</sup>

- (b) Reported community engagement index (past 6 months)[standardized summary index of ii-vi] {phu}

- i. Has the community helped clean this facility?

- ii. Has the community helped you with your personal work? E.g. Farm, back garden... etc.

- iii. How often have community members helped you with your personal work?

- iv. How often has the facility had disputes/conflicts with the community?

# **8. Community Development and Political Engagement (CDPE)**<sup>A3</sup>

- (a) Development projects (Excluding NGOs) {com}<sup>A4</sup>

- i. Has [the Local Council/the Paramount Chief] done any projects that this community (In the past year, starting May 2012)

- ii. Did community members contribute labour, money or local materials for this project (Including work for food and work for pay)?

- iii. Were any community members involved in the planning of this project?

- (b) Collective
5.1|7.4|
|Family Farm|21.5|0.0|0.0|
|Other Farm|11.2|0.0|0.0|
|Male|35.0|3.6|15.0|
|Salaried Non-farm|0.0|1.1|5.9|
|Non-Salaried Non-farm|0.0|2.4|9.0|
|Family Farm|12.7|0.0|0.0|
|Other Farm|22.3|0.0|0.0|
|% of the total female labor force|70.3|11.2|18.5|
|% of the total male labor force|65.3|6.7|28.0|

Source: Authors’ calculations using Harmonized Household Living Standards Survey 2018/19/ECOSIT 4 The impact of the COVID-19 pandemic on labor markets is potentially disastrous. Results from the phone survey suggest that less than three months into the pandemic, 20 percent of the respondents who were employed before the outbreak stopped working due to potentially COVID-19 related reasons, and 6 percent could not work because of business or office closure, which could also partially be due to COVID19. More than nine out of ten employed respondents did not receive payment during the outbreak period.

The CGE simulation results suggest that wage rates would decline by about 60 percent on average, with a 50 to 60 percent decline for women and a 45 to 65 percent decline for men (Figure 1 and Table 3). Female labor force participation reduces by more than 3 percent in 2020 due to the pandemic and bounces back

> 9 Labor force participation rate (LFP) is defined as the percentage of the female population ages 16+ that is working or actively looking for employment.

7
|
|Generalized residuals|-0.422***|-0.381***|-0.411***|-0.648***|-0.415***|-0.403***|
||(0.111)|(0.113)|(0.112)|(0.148)|(0.111)|(0.111)|
|Women (mother=1, father=0)||||-0.190***|||
|||||(0.016)|||
|Boy|||||0.024<br>||
||||||(0.022)||
|Birth order of children||||||-0.042***|
|||||||(0.014)|
|Observations|3,863|3,863|3,863|7,603|3,863|3,863|
|R-squared|0.103|0.104|0.103|0.104|0.103|0.106|
|Note: This table reports the coef|ficients of chil|d care attendan|ce and interact|ions between c|hild care attend|ance and other|
|<br>explanatory variables in probit r<br>child care attendance on the instr<br>(Wooldridge, 2015). Then we<br>generalized residuals, interaction<br>replications. Standard errors are<br>*** p<0.01, ** p<0.05, * p<0.1.<br>Source: Estimation from VHLS|<br>egressions of t<br>ument and oth<br>estimate a pr<br>s, and other ex<br
to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample compositions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes.

# **IV GENERAL DESCRIPTIVE STATISTICS**

# **IV.A Key characteristics of adults**

Table 2 provides descriptive statistics for the adults in our study, encompassing the primary caregiver, mother and father (if residing with the child), and the individual financially responsible for the child (should they be different from the aforementioned persons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets.

Panel A indicates that around 30% of the adults in our sample are male, aligning with the common scenario where mothers predominantly serve as the primary caregivers for children. The average age of adults in the Venezuelan group is about 35, with Colombian nationals being approximately 3.5 years older. This observation is consistent with findings from previous surveys, which often show that migrants tend to be younger. Furthermore, adults from both groups possess around 10 years of education, with no statistically significant differences observed between them in terms of educational attainment. Additionally, a higher percentage of Colombian nationals (24%) are married compared 21
4|1.9|2.8|
|Middle school|11.3|12.9|21.6|19.0|4.3|6.0|
|General secondary school|36.1|33.1|21.8|20.8|7.2|8.5|
|Technical secondary or post-secondary|12.8|13.9|11.6|8.6|46.3|36.0|
|University bachelor|30.7|31.0|29.4|41.1|37.8|40.6|
|University master or higher|2.1|2.1|4.1|3.3|0.3|1.3|
|**_Occupation_**|||||||
|Working (employed or self-employed)|77.5|34.9|60.0|20.0|79.3|40.4|
|Unemployed, looking for a job|9.4|21.3|30.5|30.7|13.1|24.8|
|Not working, not looking for a job|13.1|43.8|9.5|49.3|7.6|34.9|
|_Observations_|998|997|1000|1000|953|964|

Notes: Figures are percent of respondents within gender and city.

> 20 First, the ILO data are averages for the whole country while our data only focus on the capital cities. Second, the ILO age range covers people from 15 to 64 years old while we restrict our analysis to 18 to 50 years old, which is likely to increase the labor force participation. Finally, the ILO data are from 2020 for Jordan and Egypt and 2019 for Lebanon while our sample was collected in 2022.

13