rafmacalaba/data-use-mentions-extended huggingface dataset:
17 to which Ram's results appear to fall below those of Saunders and Klau raise questions about the content of the implicit price deflator built into the Summers and Heston data.

The same problem, of course, could confront the developing nations in the sample. In fact, t problem may be worse as is suggested by Pluta's study which pe mits comparison of current and constant price elasticities for 20 developing nations (Table 9). These elasticities diverge considerably for individual countries -- in this case, constant price values both exceeding and falling below current price estimates. These results reflect the generally recognized unreliability of deflators for government spending which often are based on input prices and, hence, disregard changes in factor productivity.

In summary, although the data base is not particularly strong, the evidence on developing economies suggests the following: (1) Government spending varies considerably across low and middle-income nations. In 1985, central government expenditures as a share of GNP ranged from 10.8% to 62.1%, and government consumption as a share of GDP ranged from 6 to 45%. These variations in reported government spending exceed those observed in developed economies; (2) Compared to OECD countries, developing economies, on average, tend to devote smaller percentages of GDP to government spending. This seems especially true for low-income nations;
1997). See Figure 10, Appendix, for a visual example of this procedure.

# **2.3**

To control for effects due to short-term growth variables, we include monthly GDP growth and consumer price index (CPI) growth, to capture the most important macroeconomic factors Ludvigson et al. (2009) identified as determinants of bond returns. We use quarterly GDP data and monthly CPI data from the IMF International Financial Statistics database. To obtain monthly GDP figures, we first calculate quarterly growth rates, Δ _퐺퐷푃푡_<sup>_푄_/</sup><sup>_퐺퐷푃_</sup> _푡_<sup>_푄_</sup> −1<sup>and then use the bench-</sup> marking method of Denton (1971), as advocated by the IMF (Di Fonzo et al., 2012; Marini, 2016). See Figure 12, Appendix for an example of this benchmarking method. For inflation figures, we calculate the monthly percentage change as Δ _퐶푃퐼푡_<sup>_푀_/</sup><sup>_퐶푃퐼_</sup> _푡_<sup>_푀_</sup> −1<sup>.We restrict our attention to these two</sup> variables due to limited data availability of other country-specific indicators.

Further, we include debt-to-GDP ratios to account for a country’s fiscal deficit and reserves-toGDP ratios to account for its liquidity in foreign reserves. Both indicators are obtained from the 9
1

# 1. Introduction

Over the past ten tears, economic and political turmoil in Venezuela, as well as a surge in criminal violence, have induced large migration flows of Venezuelans. By September 2023, about a third of the 7.7 million Venezuelans who had fled the country because of the crisis had registered in neighboring Colombia. Of these, more than 1.8 million had obtained a residence permit.<sup>1</sup> About 40% of Venezuelans in Colombia are minors.<sup>2</sup> Since forced migration most likely interrupted their education process, the implied surge in the demand for schooling constitutes an important challenge for the Colombian education system. Indeed, the extent to which local schools can absorb and accommodate Venezuelan children will determine the accumulation of skills and human capital of the younger generations of both migrants and native kids in host communities.

In this paper, we estimate the impact of the Venezuelan migration shock on schooling outcomes in receiving Colombian municipalities. In particular, we use administrative data to study the effect of the Venezuelan migration on school enrollment, dropout rates, promotion rates, and test scores. We use individual-level administrative data to distinguish between native and migrant children, as well as in terms of gender and school characteristics such as their public or private ownership.

Our empirical strategy exploits two sources of exogenous variation in cumulative Venezuelan migration inflows at the municipality-year level. Cross-sectional municipal variation comes from the share of population within each receiving municipality that was born Venezuela and arrived in 1993, well before the start of the political and economic crisis of that country. Annual variation comes from the number of Venezuelans arriving to Colombia each year, as the crises worsened during the late Chavez and under the

> 1Source: United Nations’ Regional Interagency Coordination Platform.

> 2See `https://migravenezuela.com/informes/caracterizacion-de-la-ninez-y-adolescenciamigrante-en-colombia/(lastaccessed15/01/2024).`
**Figure 9. Average Psychosocial Stimulation Scores Among Children Aged 0-72 Months in Pakistan by Parent Gender and Educational Attainment**

<!-- Start of picture text --> 8.50<br>8.00 7.88<br>7.70<br>7.65<br>7.50<br>7.09<br>7.00<br>6.62 6.61<br>6.50<br>6.00<br>Less than Primary Education Primary or Middle Education Secondary or Higher Education<br>Mothers/Female Caregivers Fathers/Male Caregivers<br>Note: All estimates are weighted. Psychosocial stimulation as measured by children’s access to learning materials and<br>participation in stimulating activities with adult caregivers in the past 24 hours on a scale of 0-14. Sample includes all children<br>(N=2,896))<br>Psychosocial Stimulation Sum-Score<br><!-- End of picture text -->

# **4. Discussion**

ECD has been an understudied area in Pakistan, particularly for very young children, and this study contributes to our understanding of how young Pakistani children are faring, through a holistic lens. Other national-level surveys tend to measure nutritional and physical growth metrics among children under age 3, academic performance among older children, or provide province-level but not national-level data, as in the case of the MICS surveys. This study, which provides national estimates of ECD, fills key gaps in the literature on children’s development under age 3 and school readiness for children aged 3-6 years. We quantify the compounding and pernicious impact of multiple risk factors on children’s development for both children under 3 and aged 3-6, with clear declines in development and school readiness as risk factors increase.

The study also explores which household- and caregiver-oriented variables have the strongest associations with child outcomes. For children under age 3, we found that
, 2004) that have shown that investments into relationships by one contracting party can generate information about the contracting party and promote trust and trustworthy behavior.

# **5 Conclusion**

This paper uses a nationwide randomized experiment in India to estimate the effects of debt moratorium offers on borrower beliefs and loan repayment. Contrary to widely held assumptions, we find that the debt forbearance offers extended as part of our experiment do not change borrower beliefs in a way that could give rise to moral hazard. On the contrary, when lenders are able to take credit for granting repayment flexibility and moratorium offers are framed as an initiative of the lender, loan repayment improves substantially once the moratorium expires. We can rule out a number of confounding mechanisms, including reminder effects and the relaxation of financial constraints and additionally show that forbearance offers also have no negative spillover effects on the repayment of other loans. Using data on the universe of outstanding loans for borrowers in our sample, we show that forbearance causes borrowers to prioritize loan repayments to the lender that offered forbearance and away from other lenders. This substitution result has interesting implications for the relationship between competition and credit discipline, which we leave for future work to explore.

Moreover, our findings suggest a mechanism through which repayment deferrals presented as an initiative of one’s lender improve loan performance. Consistent with the interpretation that the offer reminds borrowers about the value of a continued relationship with their lender, borrowers in 26
2019), but this development will rely on future data availability.** Hanming and Shepard have labor supply and demand information and data from employer-provided health insurance. In this paper we do not have firms’ information; the estimated labor market parameters do not allow us to disentangle whether the values found are coming from reduced labor market productivity or from employer discrimination against Roma males or females. Given this limitation, in the policy experiments we will include only male and female labor parameters to simulate if there is a reduction in discrimination, or if Roma wives’ productivity increases over Roma husbands. We then calculate spouses’ optimal time allocation, labor supply, and wages for these scenarios.

**This paper is organized as follows.** Section 2 describes the model. Section 3 describes the data. Section 4 discusses the identification of the model. Section 5 interprets and presents estimates from the model parameters and compares them to the Roma data. Section 6 presents policy experiments, and section 7 offers a gender decomposition of the time usage and labor market gaps. Section 8 discusses this paper’s limitations and proposes future research, and section 9 concludes.

# **2. Model**

**The model aims to explain married couples’ optimal decisions regarding participation in the labor market, which job offers to accept, wages, as well as time allocations** . It also describes how these allocations change when children are present. The model was developed in Salazar-Saenz (2020) and is closely related to Flabbi and Mabli (2018) and Choi (2018). Flabbi and Mabli (2018) modeled intensive and extensive margin decisions in the labor market but restricted the allocation of time between leisure and work. Choi (2018) introduced home production into spouses’ decisions to explain specialization in the labor market or chores, and how paternity leave impacts spouses’ decisions. This model was

5
Second, our paper contributes to evidence on SCP returns. Most of this literature focuses either on a single US state<sup>14</sup> or a single country.<sup>15</sup> Our results extend the current literature by highlighting—within and across countries—the variance of a large set of program quality determinants, the association between these determinants and student outcomes, and the heterogeneity of these associations. We do not estimate SCP returns relative to a high school diploma (as in some of this literature) but rather compare SCPs among themselves.

Finally, this paper also relates to the literature that uses surveys to collect international data on practices to compare across countries. Some recent efforts of this kind include surveys on managerial practices in schools, firms, and health care (Bloom et al., 2015; 2016; 2020) as well as on universities’ practices on governance and performance (Aghion et al., 2010; McCormack et al., 2014). While data from administrative sources and “big data” methods have become increasingly prevalent, survey data remains an attractive option for two reasons. First, granular data on program quality determinants is usually not collected administratively either in developed or developing countries. Even countries that collect general institution or program characteristics do not inquire about issues such as links with the productive sector, faculty, and infrastructure. Second, as Bloom et al. (2016) note, some practices rarely have comparable measures across countries. To our knowledge, this is the first paper to conduct a multi-country data collection initiative on higher education quality determinants and student outcomes for cross-country comparisons.

> 14 See Bahr (2013; 2016) and Carrell and Kurlaender (2016) for California; De Vlieger, Jacob, and Stange (2017) for University of Phoenix; Kane et al. (2021) and Boatman and Long (2018) for Tennessee; Liu et al. (2015) for North Carolina; and Xu & Dadgar (2018) for
are most in need both in terms of resources (e.g. have a low PMT score) and program support (e.g. are at a higher risk of not meeting some program objective, such as having adequate nutritional intake).

We develop a PMT for Sudan based on the latest household survey, the National Baseline Household Budget and Poverty Survey (NHBPS) 2014/2015. We first identify a set of candidate variables that best suit the country’s situation and that we believe serve as good proxies for welfare in Sudan. Based on this set of variables, we then perform statistical analyses to determine the final candidate variables to be included in the PMT. This step includes the estimation of different model specifications. Later, we evaluate the out-ofsample performance of the model across different PMT cutoff scores and use several evaluation metrics to evaluate the targeting performance of the PMT. Given the recent and rapid changes of the Sudan economy, this PMT methodology can readily be applied to new household survey data when it becomes available.

Compared to social program targeting based on a uniform allocation, where everyone has the same chance of being included, the results show that a PMT should be considered for targeting social protection programs in Sudan.<sup>8</sup> The PMT would assist social programs to reach a higher share of the poor and vulnerable, while reducing expenditure going towards households that are less in need, improving both the poverty impact, as well as the sustainability of the program. We explore various specifications of the PMT and find that our proposed Sudan national PMT performs as well as a rural and urban specific PMT, making it easier to implement and manage in practice. Furthermore, costly and time-consuming employment information does not improve the accuracy of the PMT in a meaningful way. Finally, our proposed Sudan PMT relies on about 20 variables that could be collected by a short survey in the field on a large portion of the population.

The rest of the study proceeds as follows. Section 2
# **3.1 Coverage and its determinants**

Basic characteristics of time line and participation are illustrated in Table 1 by fortnight of the May 2005 to March 2006 survey period which according to FAO covers the harvest periods for most of Uganda‟s seasonal crops.<sup>5</sup> Column 1 illustrates that, after a rather slow start of 45 and 194 households who received the diary in the first or second fortnight of May 2005, some 400 households started making diary entries in each of the subsequent fortnights to add up to 4,451 who made any diary entries overall. The cumulative total of households reporting in any period, net of those who dropped or completed, in column 2 illustrates that we have a total of 41,673 fortnight-household combinations. The duration for which a household starting in a given fortnight remained in the sample as well as the number of diary entries made, as reported in col. 3 and 4, suggests that, with the exception of the initial and the last periods, the average household made diary entries for about 10 fortnights or 5 months and that, with some 115 entries overall (or 11.09 per fortnight), the frequency of doing so was high, i.e. almost on a daily basis. With more than half a million entries in total, this could potentially provide quite a rich basis of information to analyze agricultural performance, provided underlying information is of good quality.

Before making inferences from the diary on overall agricultural production in Uganda, it will be critical to assess the extent to which certain households will be more predisposed than others to responding, thus possibly leading to biased results. Table 2 presents sample composition and descriptive statistics to assess whether some groups are more likely to respond to or complete the diary. Overall figures suggest that, out of a total of 5,506 households who received the diary, 1,055 never made any entry, leading to a nonresponse rate of slightly below 20%. For those
# **Tracking the Sustainable Development Goals: Emerging Measurement Challenges and Further Reflections**

Hai-Anh H. Dang and Umar Serajuddin<sup>*</sup> Key words: SDGs, monitoring, data challenges, survey data, international organization JEL: F00, I3, O1

* Dang (hdang@worldbank.org; corresponding author) is an economist with the Analytics and Tools Unit, Development Data Group; Serajuddin <u>(userajuddin@worldbank.org) is manager of the</u> Development Indicators & Data Services Unit, Development Data Group. Both authors are with the World Bank. We would like to thank the Editor Arun Agrawal, two anonymous referees, Paul Anand, Jere Behrman, Grant Cameron, Francisco Ferreira, Haishan Fu, Ravi Kanbur, Duc Khuong Nguyen, and participants at the Sustainable Development Conference (University of Michigan, Ann Arbor) and seminars at IPAG Business School (Paris) and the World Bank for useful feedback on earlier versions. We would like to thank Vivien Foster, Juliette Besnard, and Yi Xu for their guidance and support on the electrification data and related work. We would also like to thank Ziqing Zhou for capable research assistance. We are grateful to the UK Department of International Development for funding assistance through its Knowledge for Change (KCP) Research Program.
# **4.1 Results from the DID approach**

Estimated program effects based on estimation of (1) are reported in table 3 panels A and B with columns 1, 3, 5, and 7 without and columns 2, 4, 6, and 8 with heterogeneity of gender effects. Regressions are weighted by the matching weight and all standard errors are clustered at village (survey cluster) level.

Results in panel A suggest no statistically significant effect on the share of land/property managed or controlled by women except for female headed households where the point estimate of LARP effects is about 11 percentage points compared to a control mean at baseline of 35.1 percent. Estimated program effects on knowledge about women’s inheritance rights and LAA existence remain largely insignificant (except about the 4 percentage points increase in the former for male-headed househols and about the marginally significant 10 percentage points increase in the later for female-headed households), implying weak impact on female empowerment and awareness of institutional reforms. With less than 5% of the sampled households from the control area expressing concern of becoming involved in land-related conflict at baseline, perceived tenure security seems to have been high even without the program so that an estimated program effect of about 5 percentage points, significant at 5%, seem non-negligible.

Although land market activation was a key project objective, the data presented in panel B suggest no significant impact on land purchase market activity. This holds irrespectively of whether we use responses to retrospective questions about land transactions for the last 3 (i.e., 2010-2013 and 2016-2019) or the last 7 years (i.e., 2007-2013 and 2013-2019) for baseline and endline, respectively. Finally, panel C suggests that estimated program effects on credit market access, proxied by whether a secured home loan was taken, the household applied for a formal loan or had a credit card or bank account are all insignificant (except a marginally significant negative effect on loan application in col. 3). The only coefficient
**Figure 3.** Intensity of poverty, by wave and gender of the household head

Panel A. Distribution of poor households using the unadjusted expenditure (θ= 1) <!-- Start of picture text --> Female PA<br>Male PA<br>Decile 1<br>Decile 2<br>Female PA<br>Decile 3<br>Decile 4<br>Male PA<br>0% 20% 40% 60% 80% 100%<br>% of households, per decile<br>2013-14<br>2017-18<br><!-- End of picture text --> Panel B. Distribution of poor households using poverty adjusted by economies of scale (θ= 0.5) <!-- Start of picture text --> Female PA<br>Male PA<br>Decile 1<br>Decile 2<br>Female PA<br>Decile 3<br>Decile 4<br>Male PA<br>0% 20% 40% 60% 80% 100%<br>% of households, per decile<br>2013-14<br>2017-18<br><!-- End of picture text --> _Note_ : Own calculations based on ProGres and JD-HV database. Panel A shows the intensity of poverty using the headcount measure, while Panel B displays the intensity of poverty using a measure that accounts for economies of scale. The horizontal axis indicates the percentage of households in each decile of the bottom 40, for each wave.

19
|4.21|
|**Complete junior high**|20.06|19.80|20.67|24.85|23.34|28.39|24.90|23.41|28.67|
|**Incomplete senior high**|19.33|21.44|14.38|15.57|17.04|12.13|12.44|13.06|10.88|
|**Complete senior high**|9.45|9.86|8.50|13.93|15.48|10.30|18.23|19.32|15.48|
|**University**|18.23|20.60|12.69|23.16|27.06|14.02|29.20|34.26|16.40|

_Source:_ Author’s computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

# **Earnings and Labor Informality Are Basically Constant**

18
<!-- Start of picture text --> Figure 7f: Access to Adequate Health Services by Household Wealth<br>Prevalence of Adequacy of Health<br>By wealth<br>47<br>38 38<br>31 30<br>25<br>22 22<br>19<br>17<br>13 14 14<br>11<br>9<br>6 4<br>2<br>BGD, HK '10 BOL '08 IDN '10 NPL '11 ZWE '10<br>BGD, IFPRI '11 ETH '11 KHM '10 PER '12<br>Resource Poor Resource Rich<br>Source: Author estimates.<br>Note: (1) Bangladesh (IFPRI) has Vitamin A supplementation for 6 to 24 month olds only.<br>(2) Indonesia does not have information on prenatal checkups or Vitamin A supplementation.<br>100<br>80<br>60<br>40<br>20<br>Percentage of children who meet criteria<br>0<br><!-- End of picture text -->

# **3.3 Additional Considerations: Weak Links in a Chain**

The preceding discussion suggests a correlation between the observed reductions in stunting and the increases in access to adequate food security, adequate care and adequate environment and health services over the same period in the countries studied. For the countries with two years of similar survey data, the prevalence of stunting fell (Figures 2 and 3). In the same time span there were marked improvements in the adequacy of care (Figure 6b), some improvement in adequate environment and health (Figures 7c and 7d) and a mixed story in adequate food with some countries experiencing a decrease in the access to adequate food (Figure 5b). Whether there is indeed a “causal” relationship as opposed to a simple correlation between improved access to