rafmacalaba/data-use-mentions-extended huggingface dataset:
IPCC 2012, Oxfam 2017).<sup>6</sup> Meanwhile empowerment, voice, agency, citizen engagement, and social accountability are grouped in one concept, namely, process legitimacy (e.g., Kabeer 1999, Fox 2007, Joshi 2008, IFPRI 2020, IDS 2020). This synthesis illustrates, both the indicators often used in terms of their global coverage, and the possibility to be unpacked by vulnerable groups and populations. This snapshot of indicators, though not comprehensive, first confirms that there are numerous indicators available from a relatively small amount of reputable data sources. Second, it shows that indicators notably vary in terms of their country coverage. This is partly a reflection of their methodologies: experts’ opinion-based sources usually have a global coverage with frequent updates, while household surveys vary in the data collected and may be realized far less frequently. Disaggregation-wise, there are also trade-offs: data based on expert opinion is national in scope and is often not designed to explore subnational, inter- and intragroup heterogeneity, including among vulnerable groups. Other sources like household surveys can unpack results across groups and location, although the extent to which those disaggregations are considered in the sample design to provide statistically significant results varies from source to source.

> 6 _Absorptive capacity_ is the capacity to take intentional protective action and adopt coping strategies to bounce back after a shock, ensuring stability as it limits the negative impact of shocks. _Adaptive capacity_ is the capacity to make intentional incremental adjustments in anticipation of or response to change, to create more flexibility in the future. _Transformative capacity_ is the capacity to make intentional change to stop or reduce the causes of risk, vulnerability and ensure a more equitable sharing of risk. It is about fundamental changes in the structural causes and aggravators of vulnerability and risk.

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# **1 Introduction**

Timely and comparable poverty estimates are vital to assess countries’ development International estimates serve as a for re- progress. poverty public good searchers and inform the development community on efforts to meet the first Sustainable Development Goal, to end extreme poverty by 2030. Within international development organizations, national development agencies, and NGOs, they also inform the allocation of resources and the development of strategic priorities.

Yet timely and comparable estimates of poverty are lacking for many reasons. In some countries, fragility, conflict, and violence make it difficult to conduct household expenditure surveys altogether, while in other countries, lack of monetary resources is the main obstacle. Even when surveys are frequently conducted, the time it takes to field a survey, collect, process, and analyze the data, often implies a two-year lag from the time of data collection to the release of international poverty estimates. With the world changing at an ever more rapid pace, as illustrated by the unexpected onset of COVID-19, this lag risks painting an outdated picture of poverty in a country. As of October 2021, on average across the developing world, the most recent survey with international poverty data was from 2014. In addition, 16 economies with a population greater than 1 million had no international poverty estimate at all. For these reasons, initiatives that reliably and cost-effectively predict what the poverty rate is today are crucial for informed and effective high-level decision-making.

The objective of this paper is to test various methods to estimate extreme poverty in all countries of the world as of the present year and as of the preceding year – at the time of writing, 2021 and 2020. We will refer to estimates of poverty for the present year as nowcasts and estimates of poverty for the preceding year as nearcasts. For _nearcasting_ , one can rely on all data that are produced with less than a one-year time lag. We will use more than 1,000 variables from the World Development Indicators, the World Economic Outlook, and the Google Earth
on industrial composition for every country in the sample. In contrast, by virtue of using data from several countries, the results have wider scope. The study is also able to investigate the role of barriers to divorce.

> 6 Understanding domestic violence in developing countries is important because rates are higher and women’s outside options are more limited, related to their having weaker property rights (Agarwal, 1994; World Bank, 2012), more limited labour market opportunities, and facing cultural and legal barriers to divorce.

> 7 This is useful because the macroeconomic unemployment rate captures changes in all women’s employment opportunities, including those who do not work.

> 8 Here, the study has summarized research predicting IPV, without attempting a comprehensive survey and focusing upon studies that model IPV as a function of individual employment or aggregate unemployment rates. There is a wider literature. For instance, Gulesci et al. ( _forthcoming_ ) find that mothers whose daughters were exposed to compulsory schooling increases are 12 percentage points less likely to find domestic violence justifiable, and there is not similar spillover associated with increases in their son’s education. Other studies have explained familial or regional variation in IPV with reference to _cross-sectional_ differences in childhood experiences or the historical shaping of gender norms respectively (Alesina et al. 2016; Tur Prats 2015).

> 9 Both of these studies use the Bartik instrument, relying upon variation in local industry shares.

> 10 Other studies that highlight the beneficial impacts of women’s own employment and income for IPV include Bowlus and Seitz (2006), Tauchen et al. (1991), Farmer and Tiefenthaler (1997) and Pronyk et al. (2006).

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filled this gap, with data on the value of natural resource stocks and flows across 141 countries and spanning the period from 1995 to 2014. The OECD pioneered methodology to include natural resources (minerals and energy) in TFP estimations for OECDmember countries and a few other countries (Brandt et al. 2017). This paper tests the OECD approach for a broader set of natural resources and with some modifications described in section 2, for 74 middle- and low-income countries, exploiting the World Bank wealth database.

This study generalizes the preliminary ‘version 0’ estimates of TFP growth with natural resources by Hamilton (2018) in _The Changing Wealth of Nations 2018_ . It is important to emphasize at the outset that the results reported here represent ‘version 1’ estimates and future work will refine the estimates. The intent of this paper is to test the methodology for developing countries, demonstrating the extent to which including natural resources in TFP growth measures matters. We will not attempt, at this early stage, to provide in-depth analysis and policy conclusions, which will be addressed in future work.

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, but another expressed frustration with the limited options available within the sector. The desire to be independent, to have control over hours of work, tasks, and income - the key motivating factor cited all over the world by enterprise owners, large, small and micro, is indeed cited by HE owners in our focus groups, and more often than in the household survey data. And in every country where we conducted fieldwork, the desire to make more income dominated the response, even if it was expressed in one hundred different ways.

Does it matter whether HE owners were “pushed” or “pulled”? Given the inescapable math of informal enterprise growth shown above, it may not. In the medium term, as Fields, (2012) noted, labor force participants will have to create jobs for themselves, whether in agriculture, as accidental NFE owners or as NFE owners attracted into a line of business. And regardless of motivation for entering the sector, HE owners report the same constraints _across_ 20
across countries and anchors the system in the 2030 agenda so that a global view can be generated while enabling different emphasis to be applied in different countries to reflect the user needs of that country.

# **3.4 Data sources**

The data sources pillar is segmented between sources generated by the statistical office (censuses and surveys) and sources accessed from elsewhere (administrative data, geospatial data, private sector data and citizen generated data). The appropriate balance between these types of sources will vary depending on the institutional setting and maturity of the statistical system in each country. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics may reflect a lack of use of (and low score for) geospatial data. This linkage, which is inherent in the data cycle approach, should help highlight areas for investment if country needs are to be met. **3.5 Data infrastructure** The data infrastructure pillar is segmented into hard and soft infrastructure segments itemizing essential cross-cutting requirements for an effective statistical system. The segments are:

1. Legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system

2. Standards and methods addressing compliance with recognized frameworks and concepts 3. Skills including level of skills within the statistical system and among users (statistical literacy) 4. Partnerships reflecting the need for the statistical system to be inclusive and coherent 5. Finance, both domestically and from donors 36
by about 0.3 standard deviations. In contrast, the crime and violence index loses its significance when assuming that attritors reached higher values of the outcome distribution. Nevertheless, the effect size does not change much which makes us confident that estimated reduction is not an artifact of the data selection.

In addition, we address potential attrition bias by re‐weighting observations based on their likelihood to be included in the final sample. Control group participants that were reached during the phone survey had an 82 percent likelihood to be reached for the final survey, while control group participants that were not reached during the phone survey had only a 46 percent likelihood to be reached for the final survey. Likelihoods to be reached in the final survey differ similarly for the treatment group. We thus attach sampling weights to all observations based on the inverse likelihood to be successfully interviewed for the final survey. Results are reported in Tables A2.3 to A2.6.

All intention‐to‐treat estimates proof robust to our re‐weighing exercise. The weighted regressions confirm a positive average effect on the savings, investment and debt index, a negative effect on the likelihood to be vulnerable to crime and violence, and a positive effect on the list experiment on cattle raiding.

Turning to results that distinguish between “training and grant” and “training, but no grant” largely confirm our main results. Our main results show positive effects on consumption and savings for participants that received training and grant, and this finding is confirmed in the weighted regressions. What is more, the weighted regressions confirm our positive finding on the effect of grant and training on psychological well‐being. For participants that only received the training, but not the grant, we can also confirm that estimates on trust, on crime and violence and on the list experiment regarding aggressive arguments show a weakly significant negative effect.

# 6. Conclusion

Our study used the example of the unplanned cancellation of the South Sudan Youth Business Start‐ Up Grant Program to evaluate the
We combine these firm-level data with country-level data to control for national characteristics, including a country’s institutional framework. The data source for each variable is indicated in section 3.5. Data for the national variables are aligned with the survey year of a country’s enterprise survey (see Appendix 2 for information on survey years). In a few cases where national data were unavailable for the specific survey year, we choose the observation of the nearest available year.

We include three different instruments for foreign output share. The selection of these instruments is based on the assumption that the instruments are correlated with the foreign output share variable, but not with domestic firm productivity in the country of interest. First, we take advantage of the fact that for many countries the Enterprise Surveys Indicator Database publishes surveys for more than one year. Based on the previous survey year for such countries, we are able to calculate the foreign output share variable as defined in equation 2 to be used as an instrument. For some countries we have to rely on the previous version of the Enterprise Surveys Indicator Database which covers the period 2002-2005.<sup>13</sup> In most cases, the time lag between the original and lagged foreign output share variables covers between two and four years. Therefore, it is safe to assume that the instrument is correlated with the foreign output share variable in _t_ , but not correlated with domestic firm productivity in _t_ .

Second, we add a sectoral measure of sector targeting by investment promotion agencies.<sup>14</sup> The measure equals 1 if a sector has been targeted by a country’s investment promotion agency in a certain year and 0 if not. As foreign output share rather reflects longer-term FDI presence, we aggregate the dummies over the period 2000 to 2004 to obtain a measure of total length of sector targeting in the early 2000s. The sum can range from 0 (no targeting over this period) to 5 (continuous targeting over this period).
DMA refers to Nielsen designated market areas. Zip3 refers to 3-digit zip codes. Implied rate refers to the unit-subsidy pass-through rates implied by the estimated coefficients assuming markups are 0.3, which implies a market conduct of about 0.16. The coefficients are used to calculate the elasticity of marginal surplus at the mean log price.

Table N17: Incidence of an additional dollar of SNAP benefits for SNAP-ineligible goods

|MPC elasticity<br>Pass-through elasticity<br>Shift magnitude<br>PS<br>CS<br>CS (SNAP)<br>CS (non-SNAP)|
|---|
|0.1182<br>0.0898<br>0.009<br>0.098<br>-0.1608<br>-0.013<br>-0.148|
|Notes: MPC elasticities are obtained from Section 4.2. A market conduct parameter of 0.5 is assumed using markups as shown<br>in Hottman (2016). Demand elasticity is estimated using panel variation as described in Section 5.3. Proportion of SNAP<br>sales of 0.168 is obtained from USDA data. Pass-through elasticity is obtained from Section 4.1 and the shift magnitude is<br>the predicted pass-through elasticity obtained using equation (8) assuming a unit-subsidy pass-through rate of 1.<br>Surplus<br>calculations are changes in surplus per marginal dollar of SNAP disbursed. PS refers to producer surplus and CS refers to<br>consumer surplus, CS(SNAP) and CS(non-SNAP) refers to consumer surplus for SNAP consumers and consumer surplus for<br>non-SNAP consumers, respectively.|

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also recalculated figure 1a under each scenario to see how the results change for each country and the results are available upon request. We see that the different assumptions explored in each of the scenarios (as presented in Figures 2a-d) do not lead to dramatic changes in our estimates, increasing our confidence in our results and further analysis.

The appendix presents additional robustness tests in which we explore how our results change with alternative assumptions about the ability of the self-employed, especially agricultural workers, to work from home. If anything, data from PIAAC surveys suggest agricultural employment in lower income countries is less amenable to working from home than in high-income countries. Based on these patterns, the DN2020 index (and our adaptation of it) does not appear to dramatically underestimate the ability of agricultural workers to work from home in developing countries.

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ungroup’ Stata routine (Abdelkrim and Duclos, 2007) which implements the Shorrocks and Wan (2008) method. As described in more detail in Lakner et al. (2014), this is very similar to the approach used by PovcalNet in the measurement of global poverty. Fitting a parametric Lorenz curve is more robust than fitting a kernel density (Minoiu and Reddy, 2014).

> 12 We thus have a consistent spatial price adjustment over time. One reason why PovcalNet adjusts for spatial price differences in China is that rural and urban surveys were conducted separately until recently.

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As for the term structure part of the hybrid model, the approach developed by Diebold and Li (2006) is used. The Diebold and Li model uses variations on Nelson-Siegel (1987) exponential components framework to model the term structure of interest rates as a threedimensional design—level, slope and curvature—evolving dynamically. In the model, yield curves are fitted using the following functional form: where t: time

> The parameter<sup></sup> _t_<sup>in the above equation governs the exponential decay rate; small values of</sup>

> <sup></sup> _t_<sup>produce slow decay and can fit the curve better at long maturities, while large values of</sup> _t_

> produce fast decay and can better fit the curve at short maturities.  1 _t_ ,  2 _t_ and  3 _t_ are

> interpreted as three latent dynamic factors where  1 _t_ governs the level of yield curve,  2 _t_

> governs its slope and  3 _t_ is closely related to the yield curve curvature (Diebold and Li, 2006).

To implement the hybrid model, the parameters  1<sup>,</sup> 2 and  3 are estimated first based on historical data, which constitutes the formation period. The second step involves forecasting.  2 _t_ and  3 _t_ parameters are fixed to  2 and  3 based on their estimated values from the formation period.  1 _t_ , the variable related to the level, becomes the only varying parameter in generating forecasts and adjusted values for  1 _t_ are calculated from the short-term interest rate level forecasts of CIR and CKLS models. The process for calculating adjusted  1 _t_ can be described as follows: where

- 1' _t_ : Adjusted  1 _t_

- <sup>_L_</sup> _t_<sup>:  Simulated forecast level for short term at period t</sup>

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. Specifically, this variable takes a value of 1 if the respondent reports that the household owns a toilet, and the interviewer sees it; and 0 otherwise. We complement this measure with toilet ownership status captured in the 2016 SBM snapshot and use toilet construction date reported during the survey endline survey to compare consistency between these two data sources. The third outcome of interest – sanitation behaviour – is particularly relevant for policy. We measure this through two variables: an indicator for whether the toilets reported by households are in use, and an indicator for open defecation (as reported by the survey respondent for different demographic groups in the household). Finally, the fourth outcome of interest – subsidy uptake – is measured using data on subsidy receipts recorded in the September 2016 SBM snapshot.<sup>18</sup>

> 17The included covariates are chosen to be those that most explain the variation in toilet ownership among control households, and include a dummy for whether or not the household has a child aged 3 or 4, toilet uptake status at baseline, interviewer fixed effects, and village size. The baseline survey sampling strategy over-sampled households with children aged less than 2 years, which is why we control for whether the household had a child aged 3-4 years at endline. We control for village size since the endline sample of clients was (inadvertently) selected in a manner that correlates with village size. To control for toilet ownership pre-introduction of the loan product, we use reconstructed baseline toilet ownership, using respondent’s endline report on the age of their toilet. We use reconstructed baseline toilet ownership instead of census data on toilet ownership because census was based on segmenting and therefore not available for all households in our sample. We nevertheless conduct robustness checks using actual baseline ownership data on the restricted panel sample and results remain qualitatively the same.

> 18The reason why we do not use subsidy receipt as reported in the household survey is because this was only asked of households that owned a toilet, whereas the SBM dataset records
with unobservable determinants of cross-country differences in economic performance. As pointed out in Klenow and Rodr ́ıguez-Clare (1997b), countries with policies discouraging capital accumulation are also likely to have in place policies or institutions discouraging technology adoption. This would lead to a violation of identification assumption in Mankiw et al. (1992), where the level of technology is treated as unobservable.

Various approaches have been proposed to overcome these identification challenges. Islam (1995) adopts a panel data approach, where time invariant “country effects” are added to the dynamic specification of Mankiw et al. (1992). To the extent that the common drivers of capital accumulation and technology adoption are indeed fixed over time, they are captured by these country effects and do not spuriously inflate the contribution of physical and human capital to output per worker. In this setting, Islam (1995) finds that proxies for human capital are not strongly related to output per worker, questioning the empirical relevance of the Solow model with human capital accumulation. While levels of school enrollment are positively related to levels of GDP per worker, the relationship is much weaker when introducing the temporal dimension.

This approach is itself subject to a number of issues. First, the least squares estimation of dynamic panel model including fixed effects is generally biased, as showed by Nickell (1981). Second, temporal comparisons are particularly sensitive to problems in the measurement of human capital. Given that average school attainment at the country level changes slowly over time, short-run fluctuations are likely to be mostly driven by measurement error. Indeed, Lindahl and Krueger (2001) argue that commonly used international data on educational attainment are subject to problems of incompleteness, imprecision and lack of consistency across countries. Cohen and Soto (2007) show that when using a more accurate dataset on average years of schooling across countries, increases over time in this measure of human capital are in fact positively related to GDP growth.

Other authors attempted to solve the identification problem through instrumental variables. This approach consists
line may not be the appropriate threshold for international poverty measurement when the distribution is adjusted to the level of national accounts means. Since the $1.90 line is itself estimated from national poverty lines originating from household surveys (see Ferreira et al., 2016; Jolliffe and Prydz, 2016), the poverty line is likely also underestimated, and therefore not necessarily suitable for use with living standards measured using national accounts. A simple adjustment of the international poverty line for use with national accounts, would be to scale the poverty line up by the same proportion as the survey mean (to equate with national

> 26 This is essentially true by assumption. In our exercise, we simply scale the survey mean to the national accounts mean and apply the international poverty line of $1.90 per day. 27 We analyze the differences for surveys where the household survey has more than 3 percent poverty. At low poverty estimates small changes can lead to very large changes in percentage terms.

> 28 These measures are broadly consistent with Sala-i-Martin and Pinkovskiy (2016) who calculated measures up to 2010 using 2005 PPPs and the $1.25-line. To our knowledge, the measures presented here are the first poverty estimates using national accounts data and the updated $1.90 poverty line.

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