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

In this paper, we report on what primary school teachers in Africa know and do, using representative data from an ongoing survey program: The Service Delivery Indicators data. The findings provide a concerning picture of teacher effort, knowledge, and skill, although there is significant variation in teacher quality, both within and across countries. Even taking this variation into account, however, if “adequate” teaching is defined by students that during most of their scheduled time are being taught by teachers with at least basic pedagogical knowledge and minimum subject knowledge in language and mathematics, then very few public primary school students, in the seven countries we surveyed, benefit from decent quality education.

We argue, and partly show, that this lack of quality education in turn can help explain why so many students learn little and complete their primary education lacking even basic reading, writing, and arithmetic skills. Potential human capital for cohorts of students is consequently lost.

23
**Figure 7: Percent of Firms with a Bank Account/Loan or Line of Credit by Size** **_Panel A: Bank Account (%) Panel B: Loan or Line of Credit (%)_**

<!-- Start of picture text --> 100% 80%<br>80%<br>60%<br>60%<br>40%<br>40%<br>20%<br>20%<br>0% 0%<br>Africa Rest of the World Africa Rest of the World<br>Small Medium Large Small Medium Large<br><!-- End of picture text --> _Source_ : World Bank Enterprise Surveys, various years. Available at: www.Enterprise Surveys.org. _Note_ : Sample size is 106 countries. Africa includes 38 countries all from Sub-Saharan Africa. All country point estimates are weighted. All regional averages are simple averages.

Next, we examine one-year growth rates in employment and identify ―high-growth‖ firms, defined as 1-year growth in employment greater or equal to 20% (OECD, 2008). On average, we categorize about 15% of SME’s in both Africa and other developing countries as high-growth firms. However, we find important differences in the sources of financing used to finance new investments. In Africa, 84% of investments of SME’s are financed through internal funds compared with 70% in other developing economies. The share of bank financing is 8% (compared to an average of 11% in other countries) and the share of equity financing in Africa is less than 2%, as compared to 8% in other developing economies (Figure 8). This data suggest that firms in Africa, with similar growth opportunities as firms in other developing countries, are more dependent on internal funds and more credit constrained in accessing external formal financing.

14
# ANNEX 1 _ENEU_

This paper uses information from the National Urban Employment Survey (ENEU), which is also a micro-level data set collected by (National Institute of Statistics and Geography of Mexico) INEGI and contains quarterly wage and employment data over 1987-2002. According to INEGI’s methodology document on the ENEU, the data are representative of the 41 largest urban areas in Mexico, covering 61 percent of the population in urban areas with at least 2,500 inhabitants and 92 percent of the population living in metropolitan areas with 100,000 or more inhabitants. In 1985 the ENEU included 16 urban areas: Mexico City, Guadalajara, Monterrey, Puebla, León, San Luis Potosí, Tampico, Torreón, Chihuahua, Orizaba, Veracruz, Mérida, Ciudad Juárez, Tijuana, Nuevo Laredo, and Matamoros, covering 60 percent of the urban population for that year. In 1992, 18 more urban areas were included in the survey: Aguascalientes, Acapulco, Campeche, Coatzacoalcos, Cuernavaca, Culiacán, Durango, Hermosillo, Morelia, Oaxaca, Saltillo, Tepic, Toluca, Tuxtla Gutiérrez, Villahermosa, Zacatecas, Colima, and Manzanillo. In 1993 and 1994 Monclova, Querétaro, Celaya, Irapuato, and Tlaxcala entered the ENEU. Finally, Cancún and La Paz joined the survey in 1996. According to INEGI, the ENEU always has covered about 60 percent of the national urban population.

The data are from household surveys, which fully describe family composition, human capital acquisition, and experience in the labor market (the variables contain information about social household characteristics, activity condition, position in occupation, unemployment, main occupation, hours worked, earnings, benefits, secondary occupation, and search for another job). As with the National Household and Income Survey (ENIGH), the sampling design was stratified in several stages (where the final selection unit was the household) and with proportional probability to size.<sup>3</sup> This statistical construction allowed us to make comparisons among different years. Moreover,
anchored to a food basket that ensures a minimum calorie intake, in case where food inflation is expected to be significantly different from general inflation between baseline and projected year, the baseline poverty line would not be enough for a household to meet the basic food requirements in the projected year.

In the case of Iraq poverty is defined in terms of consumption so the last step consists in transforming household incomes into predicted consumption levels by using the consumption income ratio for each household from the baseline year.

# **Limitations and assumptions**

There are several limitations and assumptions to apply this method which are important to mention. Firstly, the quality of model projections depends on the nature and accuracy of the data underpinning the exercise. The results would depend not only on the validity of the micro‐models but also on the macro projections. In addition, the use of the last available household data (IHSES 2012) as a comparator is tricky because the comparison could potentially attribute certain outcomes to that particular projection when they are a result of other factors that occurred over the period.

Secondly, the simulation relies on behavioral models built on past data that reflect the pre‐existing structure of the labor market, household incomes and their relationships with demographics as they stood before the expected change. Consequently, the simulation assumes these structural relationships remain constant over the period for which projections are made. The more distant in the past the baseline year is, the more questionable this assumption is likely to be.

The model is limited in its ability to account for shifts in relative prices between different sectors of the economy as a result of the shock. While the poverty impact of shifts in the price of food relative to other prices is taken into account, other potential sources of price impacts are ignored – for example, the general equilibrium effect of a change in the terms of trade between agriculture and other sectors. In the absence of a CGE model, it is nearly impossible to explicitly model for changes in terms of trade between sectors.
and formal tenure modes. The theoretical model exposes the trade-off faced by the homeowner in deciding how much backyard land to rent out: loss of yard space consumption in return for a gain in rental income. Higher rent raises the opportunity cost of own-consumption of yard space (depressing it), but the gain in rental income from higher rent has the opposite effect, leading to an ambiguous net impact of rent on consumption. Under common forms for preferences, however, the homeowner’s own-consumption of yard space falls as land rent increases, causing more land to be rented to backyarders. Better job access, which raises the land rent backyarders are willing to pay, then tends to lead to more backyarding, but it also raises the homeowner’s income net of commuting cost, which leads to less backyarding. Under a natural assumption, the net effect is an increase in backyarding, leading to a predicted positive association between job access and the extent of backyarding. This result matches the usual connection between job access and population density in urban models, but it comes from a subtler combination of This hypothesis is tested by combining a satellite-based count of backyard dwellings per parcel with job-access data, which come from job data at the level of transportation zones together with an origin-destination matrix showing trip times between zones. The empirical results strongly confirm the prediction that better job access increases the extent of backyarding. In addition, the estimated inverse relationship between backyarding and parcel size suggests that lower homeowner income (associated with small parcels) may spur backyarding, as also predicted by the model.

Thus, using information from a number of remarkable data sets, the paper provides unique insights into a land-use practice that is mostly absent in developed Western countries, even though it bears some resemblance to room rentals by homeowners under an Airbnb-style arrangement. An understanding of the forces that drive the backyarding phenomenon, particularly the link to job access, is potentially useful to South African city planners as they attempt to manage the evolution of their urban areas. For example
Policy Research Working Paper 9151

# **Abstract**

Foundational to the monitoring of international goals on land ownership and rights are the household survey respondents who provide the required individual-disaggregated data. Leveraging two national surveys in Malawi that differed in their approach to respondent selection, this study shows that, compared with the international best practice of privately interviewing adults about their personal asset ownership and rights, the business-as-usual approach of interviewing the most knowledgeable household member(s) on adult household members’ ownership of and rights to assets leads to (i) higher rates of exclusive reported and economic ownership of agricultural land among men, and (ii) lower rates of joint reported and economic ownership among women. Further, substantial agreement exists on agricultural landowners and rights holders, as reported by the privately-interviewed spouses. When discrepancies emerge, proxies for greater household status for women are positively associated with the scenarios where women attribute at least some land ownership to themselves.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tkilic@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
27 of jobs outcomes and economic development, it seems imperative to continue to work on the design, implementation, and rigorous evaluations of comprehensive migration/jobs strategies along the lines described in this section. Not all evaluations will be able to rely on randomized control trials; some of the interventions are simply too complex or would take too long to deliver results and inform policy. But there are cheaper alternatives able to provide real-time feedback on the effectiveness of different policies/programs that are being actively discussed in the evaluation literature.<sup>54</sup> Effective migration policies will also require investments in data and diagnostics tools, a new research agenda for operational solutions, technical and financial assistance to countries, and continued global dialogue and coordination among the different actors. It is critical to expand and improve migration data<sup>55</sup> to monitor global trends in movements and remittances, to properly document the spatial gradients in livelihood prospects, to identify the main drivers and constraints affecting migration decisions (e.g. economic opportunities, access to amenities, information and aspirations), and to develop the analytical understanding of the effects of climate and environmental change on migration.

Globally, the need for coordination and knowledge exchange among the different national and international players is more needed than ever, with the Global Compact on Migration approved in 2018, an important new milestone.

> 54 See Andrews et al. (2017) for a review and discussion.

> 55 For example, through the use of phone surveys, ICT and Big Data applications, and/or qualitative methods such as life histories.
Smit, 2022) who also finds a mostly positive but not significant effect using synthetic control methods.

# **4.4 Incentives under constant benchmarks**

The main argument behind the above analysis is that estimating benchmark outcomes requires robust and transparent empirical methods instead of simple extrapolation of historical trends. In this section we explore a hypothetical method for setting constant benchmarks and discuss the implications of those.

for results-based financing that penalizes emissions from deforestation and forest degradation, rewards forest carbon stock conservation and enhancement, and sustainable forest management (UNFCCC, 2011, paragraph 70). Payments are contingent upon the observed difference between FRLs/FRELs and the observed deforestation (Granziera et al., 2021). This is consistent with our suggestion not to use observed outcomes as KPIs, but instead compare the outcomes with a benchmark. In fact, the guidelines in UNFCCC (2014) assess how historical data were taken into account when establishing the FRLs/FRELs.

However, some reference levels discussed in Granziera et al. (2021) are simple historical averages over 4-year to 20-year periods, reassessed periodically. Various reference levels were used for results-based financing (see Figure 7), which correspond to historical averages over an expanding window since 1996. These remain constant for 5-year periods, after which the FRELs are updated. Within the 5-year periods, the results were evaluated against the prevalent benchmark. This approach to estimating a baseline scenario has been criticized in the past as too simplistic (Fletcher 16
Network (HiCN)|2022- present|
|Visiting Senior Research Fellow, International School, Vietnam National University, Hanoi|2021- 2023|
|Guest lecturer, Georgetown University|2019- 2020|
|Visiting Scholar, Monash University|Nov. 2019|
|Visiting Scholar, Singapore Management University|Nov. 2017|
|Visiting Scholar, Beijing Normal University|Dec. 2016|
|Non-resident Senior Research Fellow, Centre for Analysis and Forecast, Vietnam’s<br>Academy of Social Sciences|2016- 2022|

# **Education**

|Ph.D. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2007|
|---|---|
|MSc. in Agricultural & Applied Economics, University of Minnesota, Twin Cities|2003|
|B.A. in International Economics, Foreign Trade University, Hanoi, Vietnam|2000|

# **Selected publications in the past 3 years**

# **_Journal Articles_**

1. Dang, Hai-Anh, Trong-Anh Trinh, and Paolo Verme. (in press). "Do Refugees with Better Mental Health Better Integrate?  Evidence from the _Building a New Life in Australia_ Longitudinal Survey." _Health Economics_ .

2. Dang, Hai-Anh, Paul Glewwe, Jongwook Lee and Khoa Vu. (2023). " <mark>What Explains Vietnam’s Exceptional Performance in Education Relative to Other Countries? Analysis of the 2012, 2015 and 2018 PISA Data"</mark> . _Economics of Education Review_ , 96, 102434.

3. Dang, Hai-Anh, and Peter Lanjouw. (2023). “Measuring Poverty Dynamics with Synthetic Panels Based on Cross-Sections”, _Oxford Bulletin of Economics and Statistics_ , 85(3), 599-622.

4. <mark>Dang, Hai-Anh, John Pullinger, Umar Serajuddin, and Brian Stacy. (2023). “Statistical Performance Indicators and Index: A New Tool to Measure Country Statistical Capacity”.</mark> 83
overdrafts at baseline: Fifty-eight percent of respondents overdrafted at least once in the year before baseline, with the average overdrafter paying GHS 226 ($105) in fees and interest over the same period. Appendix Figure A1 graphs the distribution of overdrafts.

Individuals who use costly, short-term credit are particularly likely to have time inconsistency problems (Meier and Sprenger, 2010; Gathergood, 2012). As such, we stratified our randomization by baseline overdrafting behavior (as well as gender and bank branch) because we were interested in studying heterogeneity in treatment effects by overdrafter status.<sup>7</sup> Before randomizing we constructed an index of overdrafting behavior and defined those

> 5Total baseline cash savings include savings at NVRB, savings at other banks, microfinance institutions, and credit unions, savings with group savings clubs, savings with agricultural co-ops, savings held by susu collectors, money lenders, other individuals, and cash kept at home.

> 6Seventy-six percent of the overdrafts taken out by our study sample were cleared within 31 days and 90 percent were cleared in 93 days. As a matter of policy, NVRB only formally approves overdrafts for one or two months.

> 7The baseline survey also asked respondents to make hypothetical choices between different amounts of money at different times, but there were only two opportunities for respondents to make a quasi-hyperbolic preference reversal and choices were unincentivized. In practice, just 3.8 percent of respondents made two reversals, while 20 percent of respondents made one reversal. Given the weaknesses of the survey-based measure, we do not use it in the main analysis and instead focus on heterogeneity in overdrafting behavior.

5
nexus between ECD services and children’s cognitive and social-emotional development. At present, household surveys such as PSLM, MICS and National Nutrition Survey, along with administrative data on service uptake, allow for analyses of some ECD outcomes, such as nutritional status and ECE enrollment. However, these surveys do not lend themselves to a comprehensive analysis of holistic ECD that focuses on cognitive and social-emotional development for children across the birth to age 6 span. The current survey constitutes a first attempt to gather and analyze this information—albeit in an operating environment restricted by the pandemic. Comprehensive and regular survey data tracking children’s development and school readiness, based on ECE experience, socioeconomic status, location, gender, and other factors, can provide insights into which interventions work and for whom. Monitoring of inequities is crucial to assess if the most 32
nutrition Intervention aimed at reducing stunting." _Economics & Human Biology_ (Policy Research Note World Bank) 34: 225-238.

- Gallego, F.A., E. Näslund-Hadley, and M. Alfonso. 2021. "Changing Pedagogy to Improve Skills in Preschools: Experimental Evidence from Peru." _The World Bank Economic Review_ 35 (1): 261286.

- Ganimian, A.J., K. Muralidharan, and C.R. Walters. 2021. _Augmenting State Capacity for Child Development: Experimental Evidence from India._ (No. w28780): National Bureau of Economic Research.

- Garces, E., D. Thomas, and J. Currie. 2002. "Longer-term effects of Head Start." _American Economic Review_ 92 (4): 999-1012.

- Gelber, A., and A. Isen. 2013. " Children's schooling and parents' behavior: Evidence from the Head Start Impact Study." _Journal of Public Economics_ 101: 25-38.

- Haacker, M., T.B. Hallett, and R. Atun. 2020. "On discount rates for economic evaluations in global health." _Health Policy and Planning_ 35 (1): 107-114.

- Hamlin, J., T. Ullman, J. Tenenbaum, and N. Baker, C. Goodman. 2013. "The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model." _Developmental Science_ 16 (2): 209-226.

- Havnes, T., and M. Mogstad. 2011. "No child left behind: Subsidized child care and children's long-run outcomes." _American Economic Journal: Economic Policy_ 3 (2): 97-129.

- Heckman, J.J. 2006. "Skill formation and the economics of investing in disadvantaged children." _Science_ 312 (5782): 1900-1902.
_CHAPTER 4. AREA LEVEL MODELS_ 22 based on the FH model with arcsin transformation. Corral and Cojocaru (2019) estimated poverty indicators in Moldova by districts and Seitz (2019) estimated poverty at the district level in Central Asian countries. The FH model has been extended in many different ways. For the case where the sampling errors _ed_ in (4.2) are correlated, Isaki, Huang and Tsay (1991), an later Isaki, Tsay and Fuller (2000), extended the FH model and used it to estimate census undercounts by different poststrata. Multivariate versions used to estimate several dependent area indicators were proposed by Fay (1987), and were utilized by Datta, Fay and Ghosh (1991) and Datta et al. (1996) to improve the direct estimates of median income for four-person families. A bivariate FH model with a _t_ -distribution was considered by Bell and Huang (2006) to account for outliers in the poverty ratios for school-aged (5-17) children for US states in 2002. Later, Benabent and Morales (2016) used a bivariate FH model to estimate poverty proportions and gaps at the province level for the years 2005 and 2006.

A subarea-level model, used to produce estimates at two different nested aggregation levels, was introduced by Fuller and Goyeneche (1998), and a similar model was studied by Torabi and Rao (2014). Unmatched sampling and linking models, where a one-to-one transformation of the target indicator _g_ ( _δd_ ) is taken as response variable in the linking model (4.1) but retaining the sampling model (4.2), were originally studied by You and Rao (2002b). A FH model with measurement error in the covariates was introduced by Ybarra and Lohr (2008). This model was used by Marchetti et al. (2015) using big data covariates to estimate poverty rates and mean income in the ten provinces of the Tuscany region in Italy.

The FH model has been extended to “borrow strength” over time