rafmacalaba/data-use-mentions-extended huggingface dataset:
poverty at the national level, (1) predicting changes from the past distribution is superior to predicting poverty rates directly, and (2) conditional on knowing real GDP per capita, publicly available remote sensing data carry little additional information.

COVID-19 illustrates both the promises and pitfalls of nowcasts. On the one hand, the pandemic caused an unprecedented temporary stop in the production of household surveys leading to an important gap in timely data and an important role for methods that circumvent the availability of primary data sources. On the other hand, the nature of the shock casts doubts on whether models trained on historical data work in a radically changed environment. Partially due to this uncertainty, the country-level nowcasts we produce should not be understood as a substitute for country-level nowcast exercises. When the objective is to nowcast poverty for a single country, often relying on other methods, such as microsimulation tools, or more granular data will be superior as they can be tailored to country-specific contexts. Yet as those methods are hard to implement consistently across many countries, the present exercise can be attractive when the objective is to compare poverty across a range of countries.

# **2 Method**

We will employ several methods to predict poverty around the world. In this section we outline the methods we use, their advantages and disadvantages, and other important methodological choices. We will focus on three distinct issues: (1) The target variable being predicted and (if not poverty rates directly) how poverty rates are obtained from these predictions, (2) the algorithms used to generate the predictions, and (3) the evaluation criterion used to judge predictive performance. Throughout this paper, when we refer to predicting poverty rates, we are using the international poverty line of $1.90 per day (Ferreira _et al._ , 2016).

## **2.1 The target variable**

When deciding how to predict poverty, it seems intuitive that the target variable to predict should be the poverty rate in each
for information intervention in Table A2).

**Observational data.** In addition to the two experiments, we also have a non-experimental contrast between the control group from experiment 1 and the treatment group from experiment 2. By comparing these two groups, we can estimate the effect of receiving the standard grant amount associated with the parental involvement program. In other words, this non-experimental contrast reveals the effect of providing grants to parent associations at the extensive margin (i.e., no grant versus single grant) whereas the double grant experiment focuses on the effect of providing grants at the intensive margin (i.e., single grant versus double grant).

As described in Section 2 and noted in Figure 2, schools in experiment 1 were those already receiving the “standard” parental involvement package of grants and information, while schools in experiment 2 were those that had not yet received the parental involvement program. Historically, the government selected schools to implement the parental involvement program based on an increasing function of indigenous student population. This means that schools in experiment 1 historically had larger proportions of indigenous students than schools in experiment 2. While we do not know the precise selection formula, we confirm in Figure 3a that the probability of being assigned to experiment 1 is strongly increasing in the proportion of indigenous students. This implies that schools in experiments 1 and 2 have different proportions of indigenous students, and therefore, we cannot simply compare across the two experiments.

Given our knowledge of the selection process to experiments 1 and 2, we trim our data to exclude indigenous schools – focusing only on general schools – when comparing across the two experiments to estimate the effect of the single grant. As shown in Figure 3b, dropping the indigenous schools imposes a common support restriction on the proportion of indigenous students and brings the distribution of indigenous students in treatment schools (single grant & information from experiment 1) and comparison schools (no grants & information from experiment 2) close together. Moreover, we drop one treatment
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data underreporting because of measurement error and appears less useful as an indicator of underreporting by individual firms (section 2).

The MIMIC model approach also shows that underreporting is systematically related to firm characteristics and investment climate variables. This is true for underreporting to the tax office but also for sales reported in the survey. Even though our analysis is based on cross sectional data and we are not able to fully control for firm heterogeneity, our finding that firms also underreport in the survey may pose a serious challenge for economic analyses that use firm-level survey data without adequately controlling for possible and systematic underreporting bias.

35
# **Long-Lived Consequences of Rapid Scale-Up?**

**The Case of Free Primary Education in Six Sub-Saharan African Countries**

Deon Filmer The World Bank JEL Codes: I21; I25; I28; O12

## _Keywords: Free Primary Education; Learning Outcomes; Teacher Quality_

Acknowledgments: Fozia Aman and Diwakar Kishore provided excellent research assistance. The analysis has greatly benefitted from inputs and useful conversations with Roberta Gatti, Nicola Dehnen, and Andreas Yi Chan, as well as comments on an earlier draft from David Evans, Michelle Kaffenberger, and Halsey Rogers. The work rests on the large set of people— too many to mention—without whom the SDI data would not exist. In addition to funding from the World Bank, financial support from the Research in Improving Systems of Education (RISE) and the Knowledge for Change Program (KCP) Trust Fund is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.
,000. An event ends when the stock falls below 25,000, or 10 years after initiation if the destination is an OECD country, whichever comes first.<sup>2</sup> Capping the termination date of the event also puts earlier and later years in the sample on a more equal footing, as stocks in later years contain earlier vintages of refugees. An added benefit of restricting the sample to large refugee events is that this procedure also removes source countries with small numbers of refugees, who fled not

> 2For OECD countries, UNHCR does not include most individuals who have been in the country for more than 10 years in their published refugee statistics.

5
semester in close collaboration with public schools as part of the students’ obligatory curricula.

The sample consisted of 4,599 students enrolled in 138 ninth-grade classrooms from 69 public schools spread across 21 major Colombian cities. The control group received the status quo: brief biology class coverage or sporadic visits by health personnel.<sup>1</sup> To measure differences in outcomes after treatment, the study uses three sources of data: a follow-up survey one week after course completion to measure short-term changes in knowledge and attitudes, a second follow-up survey six months after the course to measure the same outcomes in the longer term; and redemption from local health clinics of a voucher for condoms.

The main focus of the study is on knowledge and attitude indicators since these are the main outcomes of interest in the literature studying young adolescents who, for the most part, have not had sex. Furthermore, these two factors have been shown to be the strongest protective factors in preventing sexually transmitted infections (STIs), hu- man immunodeficiency virus (HIV), and pregnancy among teens (Kirby, Lepore, and Ryan 2007). Recent research has also documented the important role that social norms play in responsible sexual behavior (Munshi and Myaux 2005; Ashraf, Field, and Lee 2014). By changing knowledge and attitudes in youth attending school, sexual education can ultimately play a fundamental role in achieving desirable aggregate changes in sexual behavior.

The condom voucher makes it possible to avoid obvious problems with self-reported sexual behavior data, and follows O’Donnell et al. (1995) and Thornton (2008). Increased condom demand and distribution is a common policy target and tool as part of efforts to reduce sexually transmitted infections and teenage pregnancy. The researchers view this as a key part of the design of the study, given the concern in this literature regarding the relationship between knowledge and attitude outcomes and actual behavior (Ross et al. 2007), and the challenge of gathering accurate responses to sensitive questions on sexual practices
|Rank on|Author|%<br>publications|_I_<sup>3</sup> =0<br>Publication|Rank on_I_<sup>3</sup><br>_with_=0|_I_<sup>3</sup> =0.5|42<br>Rank on_I_<sup>3</sup><br>_with_=0.5|_I_<sup>3</sup> =1<br>Total|Rank on_I_<sup>3</sup><br>_with_=1|_h_-index (_I_<sup>1</sup>)|_I_<sup>2</sup><br>Quadratic|Rank on_I_<sup>2</sup>|Maximum<br>citations|Rank on<br>maximum|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|h-index||not found in<br>Google<br>Scholar|count||||citations|||influence<br>function|||citations|
|95|Frans F. H. Rutten|25%|24|137|97|147|825|191|13|0.373|180|199|544|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed author was given full credit for a publication in the case of a co-authored publication. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at
infrastructure has increased production and transportation costs and reduced the job opportunities available to the poor. Together, these factors are contributing to a long-term decline in economic productivity. Meanwhile, Guatemala’s low tax revenues limit its capacity for redistributive fiscal policy and pro-poor spending. Guatemala’s high poverty rates and tight fiscal constraints underscore the critical importance of effectively targeting public spending.

# **_The Role of Data in Poverty Reduction_**

Effective poverty-reduction strategies require detailed information on the current geographic distribution of poverty and the characteristics of households living below the poverty line. Censuses and household surveys can shed light on a wide range of economic and social indicators. However, these methods are expensive and time-consuming, and implementing them requires considerable institutional capacity. Moreover, adverse local conditions such as violent conflict, high crime rates, or political instability may make traditional in-person surveying impossible in certain areas. As a result, policy makers must often base critical decisions on outdated or incomplete information.

Social scientists are increasingly using so-called “big data” analysis to supplement more traditional sources of information. Satellite imagery, logs from sensors (e.g. traffic, weather), smartphone applications, and cell phone data—the subject of this paper—have already yielded important insights in numerous fields. Unlike household surveys, which are specifically designed to address certain research questions, big data are usually collected in a non-research context, often as the byproduct of a commercial activity or public service. Analyzing big data requires new research methods, many of which are still in the early stages of their development. Emerging research methodologies based on Call Detail Records (CDRs) and advanced machine-learning techniques have especially promising applications in developing countries, as they can potentially generate reliable poverty data at a far lower cost than conventional household surveys.

CDR analysis can play a vital role by filling the spatial and temporal gaps left by traditional research methods. By making inferences based on cellular network usage, CDR analysis can reliably project the evolution of poverty dynamics over a specific timeframe.
and poor suitability as a GDP proxy in rural areas (Gibson et al., 2021).

To address challenges associated with using VIIRS nightlights data as a proxy for economic activity (Skoufias et al., 2021), we apply filters to remove pixels with extreme values (i.e., we restrict the sample to values comprised between the 1<sup>st</sup> and 99<sup>th</sup> percentiles) and account for the number of observations available per pixel.<sup>1</sup> We calculate the average nightlight radiance monthly spanning from 1 to 6 months before and after the occurrence of the 2019 floods in Mozambique and the 2022 floods in Nigeria. Two variables are computed: "avg_rad," representing the nightlight radiance at the flooded pixel level, and "avg_radBuff05" which averages for each flooded pixel the nightlight radiance of the pixel itself and adjacent pixels within a 0.5-kilometer buffer.<sup>2</sup> The latter variable is preferred to ensure maximum observation availability and to capture the impact on indirectly affected grid cells.<sup>3</sup> We also extracted the associated variables "cf_cvg" and "avg_cvgBuff05", which indicate the number of cloud-free observations in the month used to calculate average nightlight radiance.<sup>4</sup>

# _Flood data_

Flood events are determined based on the methodology outlined by DeVries et al. (2020). We use S1 Ground Range Detected scenes from the Synthetic Aperture Radar sensors onboard the Sentinel-1 satellite, part of the European Space Agency's Copernicus program (ESA, 2023). These scenes provide data on Z-scores derived from SAR backscatter time series of single band co-polarization vertical transmit vertical receive (VV) and dual cross-polarization vertical transmit horizontal receive (VH). Since October 2014, this data has been available every 6 days at a 10-meter resolution.

floods from permanent or seasonally occurring surface water, we utilize the historical Landsatderived monthly water probabilities dataset
Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. _Sci Data_ , 7(168).

- Michaels, G. (2008). The Effect of Trade on the Demand for Skill: Evidence from the Interstate Highway System. _The Review of Economics and Statistics_ , 90.

- Michaels, G., Rauch, F., and Redding, S. J. (2012). Urbanization and structural transformation. _The Quarterly Journal of Economics_ , 127(2):535–586.

- Mitnik, O. A., Sanchez, R., and Yanez-Pagans, P. (2018). Bright Investments: Measuring the Impact of Transport Infrastructure Using Luminosity Data in Haiti.

- Moneke, N. (2019). Can big push infrastructure unlock development? Evidence from Ethiopia. Working paper.

- Morten, M. and Oliveira, J. (2018). The effects of roads on trade and migration: Evidence from a planned capital city.

30
The dependent variable ( _ethical_orientation_ ) is a binary variable that we define in different ways to capture each ESG ethical dimension ( _mon_emi, training_ , and _top_man_fem_ , respectively). Specifically, we capture the environmental dimension with a binary variable that takes the value of one if the firm has monitored its CO2 emissions over the past three years ( _“mon_emi”_ ), and zero otherwise.<sup>8</sup> The social dimension is captured by a binary variable that takes the value of one if the firm offered formal training programs for permanent, full-time employees (“ _training_ ”) in the last fiscal year, and zero otherwise. The governance dimension is captured by a binary variable that takes the value of one if the firm has female top managers (“ _top_man_fem_ ”), and zero otherwise. The main variable of interest is the interaction between digital orientation and technological orientation (digital orientationict ∗ tech orientationict).

_X_ is a vector of control variables including firm size (“ _large_ ”), the presence of a line of credit or loan from a financial institution (“ _fin_ins_ ”), the real annual sales growth in percent (“ _sal_gro_ ”), the age of a firm (“ _age_ ”) and the logarithm of GDP per capita (“ _log (GDPpercapita)_ ”). We use median sampling weights in all estimates to allow inference to the entire population.<sup>9</sup> The model is saturated by adding countryand year-fixed effects to capture country- and time-invariant unobservable factors. Industry fixed effects alone are not included because the variable capturing technology intensity is computed at the industry level. However, in some

> 8 CO2 emissions monitoring data is only available for the years 2020-2023 for 49 countries.

> 9 For an in-depth explanation, see the ES sampling note available at the following link: <u>https://www.enterprisesurveys.org/en/methodology</u> (last accessed: July 2024). In
of the household. It also has information about expenditures and savings and by implication household income. The major shortcoming of the HBS for our purposes is that it does not contain information on the sources of income of the households. For sources of household income, we must turn to the Russian Longitudinal Monitoring Survey (RLMS). The RMLS has less than 5,000 observations and is not representative of the population on the regional level. But is has extensive information on individual and household sources of income: wages and profits from first, second, third jobs; pensions and unemployment benefits; profits and dividends from accumulated assets.

> We have employed both small area estimation and Matching techniques to generate sources of income data for all 55,000 plus households in the HBS. We describe our procedures in Rutherford, Tarr and Shepotylo. Results from both techniques yield similar results.

8
3 consumption). Under the law of one price, and given relatively weak assumptions on preferences, relative prices are equal to the rate at which consumers themselves – regardless of their income levels and allowing for different utility functions – are willing to trade one such component (e.g. bread) for another (e.g. a bicycle).

There are, of course, a number of reasons why prices may not be ideal welfare weights in practice. These range from the existence of externalities to the fact that price data is often geographically coarse, so that much actual price variation in space is missing from the information available to the researcher. Nevertheless, as Ravallion (2011, p.247) argues: “It is widely agreed that prices can be missing for some goods and deceptive for others. There are continuing challenges facing applied economists in addressing these problems. However, it is one thing to recognize that markets or prices are missing or imperfect, and quite another to ignore them in welfare and poverty measurement. There is a peculiar inconsistency in the literature on multidimensional indices of poverty whereby prices are regarded as an unreliable guide to the tradeoffs, and are largely ignored, while the actual weights being assumed in lieu of prices are not made explicit in the same space as prices.”

# _1. What is the disagreement about?_

Some of the debate between proponents and skeptics of multidimensional poverty measurement was featured in the Forum Section of the June 2011 issue of the _Journal of Economic Inequality_ (e.g. Alkire and Foster, 2011b; Ravallion, 2011; Lustig, 2011). Those articles actually suggest strong agreement on at least one basic point: “poverty is multidimensional” (Ravallion, 2011, p.236). No one seems to dispute that deprivations exist in multiple domains, and are often correlated. And that, therefore, considering information on those various dimensions, rather than only on incomes or consumption expenditures, is likely to be useful for designing policies to effectively
# **3.2 Structural estimation**

We adapt the adaptation model of Kimball et al. (2015) and Galiani et al. (2018) to incorporate delay disappointment where both adaptation and disappointment are modelled by exponential decay as in Figure 1. In particular, we estimate via Nonlinear Least Square (NLS) the following equation where the second and third terms in parentheses capture hedonic adaptation and delay disappointment, respectively. Equation (2) is valid for _t > T_ , consistent with our post-intervention follow-up data.<sup>9</sup> We focus on satisfaction with overall house quality and satisfaction with quality of family life as outcome measures of SWB.<sup>10</sup> Table 2 reports the estimated parameters of equation (2). We estimate the full version of the model which includes delay disappointment, and a restricted version that ignores it, essentially estimating the equation in Galiani et al. (2018). The delay effect _βD_ is the only statistically significant parameter when satisfaction with overall house quality is used as a proxy for SWB. Recall that SWB would eventually decline by _βD_ if the program were never implemented. In contrast, when satisfaction with quality of family life is used, only the transitory effect _βT_ is statistically significant.

Following Kimball et al. (2015) and Galiani et al. (2018), we now consider the cumulative impact of the program (i.e. “area under the curve”) and measure the specific contributions of the permanent, transitory and disappointment effects, respectively. In particular, for an individual with gross interest rate _R_ (which includes an annual mortality risk _d_ and an interest rate _r_ ), the cumulative impact of the pro-

> 9The analogous expression when _Te ≤ t ≤ T_ is

> With only post-intervention follow-up data, we cannot test if _βP_ and _βT_ changed due to delays. We thus estimate _βD_ assuming that _βP_ and _βT_ are fixed. With pre-intervention data, one could estimate _βD_ directly and allow
a radio|0.05|0.36|0.16|0.69|0.41|0.02|0.45|
|Household has a television|0.10|0.12|0.29|0.97|0.16|0.19|0.49|
|Household has a fan|0.15|0.06|0.45|1.00|0.14|0.71|0.15|
|Household has a sewing machine|0.02|0.01|0.08|0.54|0.05|0.25|0.10|
|Household has a bicycle|0.15|0.02|0.66|0.34|0.28|0.26|0.36|
|Drinking Water from Hygienic<br>Source|0.96|0.96|||0.73|0.87|0.84|
|Availability of Proper Sanitation<br>Facilities|0.3|0.61|||0.1|0.28|0.74|

Source: Authors’ own estimates based on South Asia Harmonized Micro Dataset (SARMD). All reported summary statistics are population-weighted.

The non-monetary indicators in most cases are broadly consistent with the monetary poverty rates, although Pakistan and Bhutan are outliers with respect to literacy. Sri Lanka has the lowest 19
D., Hasan, I., & Tsionas, E. G. (2014). The risk of financial intermediaries. _Journal of Banking & Finance_ , 44, 1-12.

Demirgüç-Kunt, A., Beck, T. & Honohan P., (2008). Finance for All? Policies and Pitfalls in Expanding Access. _World Bank Policy Research Report_ . The World Bank, Washington DC, USA.

Demirgüç-Kunt, A., & Klapper, L. (2012). Measuring Financial Inclusion. The Global Findex Database. _World Bank Policy Research Working Paper_ , 6025, The World Bank, Washington DC, USA.

Demirgüç-Kunt, A., Klapper, L., Singer, D., & Van Oudheusden, P. (2015). The Global Findex Database 2014. Measuring financial inclusion around the World. _World Bank Policy Research Working Paper_ , 7255. The World Bank, Washington DC, USA.

Dittus, P. & Klein, M. (2011). On harnessing the potential of financial inclusion. The case of “mobile payments”. _BIS Working Papers_ , 347. Bank for International Settlements, Basel, Switzerland.

Han, R., & Melecky, M. (2013). Financial Inclusion for Stability: Access to Bank Deposits and the Deposit Growth during the Global Financial Crisis. _Policy Research Working Paper_ , 6577. Washington: World Bank Hannig, A., & Jansen, S. (2010). Financial inclusion and financial stability: current policy issues. _ADBI Working Paper_ , 259. Asian Development Bank Institute, Tokyo, Japan.

Hawkins, P. (2006). Financial access and financial stability. in Central banks and the challenge of development, 65–79. Bank for International Settlements, Basel, Switzerland.

20