rafmacalaba/datause-encoder-data huggingface dataset:
used over a three-year period.<sup>24</sup> It then follows that surveying the full population would cost about 11 percent of the value of additional benefits that would be received annually by households with at least one child aged below sixteen from the poorest three deciles in SUSETI districts.

Overall, these results point to the feasibility of achieving substantial targeting improvements with additional surveying input. We acknowledge, though, that the implied cost-benefit ratio may differ in other low-income countries (and) with less well-developed statistical enumeration capacity.

# **V. Discussion**

This paper evaluates the effectiveness of one of the world’s largest targeting systems for delivering social program benefits. Results show that the use of the Unified Targeting Database (UDB) in Indonesia is expected to significantly reduce leakage of benefits to non-poor households. However, undercoverage remains relatively high and is due largely to the difficulties of enumerating the right households for inclusion in the UDB. A significant decrease in undercoverage is predicted under simulations that consider enumerating a larger set of households before proceeding to the stage of estimating PMT scores. By relying on a unique combination of administrative and survey data, this paper provides the first evidence on the relative importance of enumeration versus PMT errors in determining the overall effectiveness of a large-scale targeting system. Our findings suggest practical strategies for improving the effectiveness of national targeting registries recently established across the developing world.

Our key results highlight the value of increasing the number of households enumerated in the national targeting registry survey. One option is to conduct a census of the full population rather than only select households expected to be poor. While it is commonly argued that it is too expensive to visit the entire population, we find that, under a reasonable set of assumptions, the costs of surveying the remaining population from our study districts amounts to only 11 percent of the value of additional benefits received by households from the poorest three deciles previously omitted from the registry. If

> 24 Based on the 2010 Population Census and 2011 Susenas, there are a total of about 1.6 million households in our study districts, among which 1.15 million have at least one child aged below 16.

25
Policy Research Working Paper 8664

# **Abstract**

“Leaving no one behind” is an overarching principle of the Sustainable Development Goals. Many countries are prioritizing resources for those who are furthest behind. Existing malnutrition indicators—underweight, stunting, wasting, overweight, and severe wasting—are headcount ratios. They do not capture how far behind malnourished children are relative to the World Health Organization growth standards. To understand the severity of malnutrition, this study develops a new malnutrition measurement, using the method originally developed for estimating poverty. This study estimates the prevalence, gap, and gap squared for stunting, wasting, overweight, and underweight, using data from 94 developing countries over 20 years. The results show that although in most cases the headcount measures and gap measures are moving in the same direction, in many other cases, they are moving in opposite directions. Moreover, employing the new measures, the study can identify countries that have low levels of headcount for a malnutrition measure but comparatively high severity of malnutrition according to the gap measures, and vice versa. This suggests that these new malnutrition measures provide additional information on the severity of malnutrition that is not possible to be known from headcount measures. These new measures of the severity of malnutrition can therefore improve the monitoring of child malnutrition across countries, and consequently help countries to achieve their Sustainable Development Goals.

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/research. The authors may be contacted at juan.feng@fao.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
Laotian data to derive econometric estimates of responsiveness to road upgrading while controlling for a host of economically-significant forest clearing determinants. Third, a comprehensive biodiversity assessment can incorporate many dimensions of biodiversity risk into a comprehensive index that ensures representation for broad stakeholder concerns.

In this paper, we have developed and illustrated an assessment that incorporates all of these factors. We should also note that our approach is not limited to national-scale assessments. Since our methodology uses high-resolution spatial databases, it can also be “zoomed” for assessment of forest-clearing, ecological risks, and road improvement impacts in specific road corridors or small geographic regions. It can also be extended to other road network databases (e.g. open street maps), and to the analysis of potential impacts for proposed roads that do not currently exist.

In summary, since road upgrading will inevitably accompany rural development programs, we believe that explicit identification of ecologically-vulnerable areas and transport investment impacts can provide at least two valuable types of information. First, with limited budgets, it can help steer road upgrading programs toward corridors where expected biodiversity losses will be minimized. Second, it can inform the adoption of appropriate protection measures in vulnerable road corridors and neighboring areas.

35
How Factors in Creditor Countries<sup>Affect Secondary Market Fr:ces</sup> for Developing Country Debt by Sule Ozler (Department of Economics,University of California, Los Angeles and John F. Kennedy School of Government,<sup>Harvard University</sup> Cambridge, MA 02138) and Harry Huizingd* (Department of Economics, Stanford<sup>University</sup> Stanford, CA 94305)

# Table of Contents

1. Introduction 1 2. Does Bank Exposure Affect Secondary<sup>Market Prices?</sup> 4 2.1 A Cursory Inspection of t:ie Data 4 2.2 Empirical Specification 7 9

- 2.3 Estimation Results

3. How Does Deposit Insurance Affect Secondary<sup>Market Prices?13</sup> 3.1 A Model 13 3.2 Simulation Method 17 3.3 Simulation Results 18 4. Are the Results a Consequence of<sup>Implicit Insuraice?</sup> 19 5. Concluding Remarks 23 References 25 Endnotes 27 Tables 30

* We thank Alberto Alesina, Raquel Fernandez, Stijn Claessens, Daniel Cohen, Robert Kahn, Graciella<sup>Kaminsky, Maurice Obstfeld, George</sup> Pennacchi, Guido Tabellini, and especially Kenneth Froot for helpful discussions, and Robert L. Whetten for valuable advice on the Monte Carlo simulation algorithm. We are particularly grateful to Kenneth Rogoff for many insightful comments. We have also benefitled from the comments of seminar participants at U.C. Berkeley, Boston, Harvard,<sup>and</sup> Yale Universities, and of participants at NBER Summer Institute. Part of this research was done when<sup>Sule Ozler was a visitor at the National</sup> Bureau of Economic Research. Finally, the authors gratefully acknowledge the support provided by the Debt<sup>and International</sup> Finance Division of the World Bank.
# Figure 2. International reserves

# A. Global foreign reserve assets

<!-- Start of picture text --> Months US$ trillion<br>Import cover<br>Total reserves (RHS)<br>150 15<br>Total reserves excl. China (RHS)<br>100 10<br>50 5<br>0 0<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br><!-- End of picture text -->

# C. Foreign reserve adequacy

<!-- Start of picture text --> ARA metric<br><!-- End of picture text --> <!-- Start of picture text --> 2.5 Median Mean<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2007 2019<br><!-- End of picture text -->

# B. Foreign reserves

<!-- Start of picture text --> ARA metric<br>2.5 2019 2007 2000<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# D. Foreign reserve adequacy

<!-- Start of picture text --> Percent of EMDEs<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Above 1 Below 1<br><!-- End of picture text --> Source: Haver, International Monetary Fund.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. Assessing Reserve Adequacy (ARA) metric is based on IMF (2011) and determines the appropriate reserve cover on a risk-weighted basis covering shortterm debt, medium and long-term debt, and equity liabilities, Broad money and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure.

B.-D. Based on data for 55 EMDEs.

B. Unweighted averages.

C. Mean is an unweighted average. Blue vertical bars represent the 90th percentile of the distribution.

D. Data for 2019. A value below one suggests that EMDEs do not necessarily have enough reserves to meet their balance of payments requirements as defined by the IMF’s ARA metric.
# **Table A6. Balance Gender Parity and Control**

|Variable|Q|Tuungane|Sd.|Control|Sd.|Diff.|(Se.)|N|
|---|---|---|---|---|---|---|---|---|
|Distance mine|QE13|11.8|18.93|15.59|33.25|-3.79|(4.11)|169|
|Distance police post|QE13|2.91|4.34|3.36|4.74|-0.45|(0.67)|184|
|Distance kingdom HQ|QE13|7.63|10.32|9.93|10.31|-2.3|(1.53)|183|
|Ethnic heterogeneity|CQ13|0.33|0.27|0.33|0.27|0|(0.04)|171|
|Religious heterogeneity|CQ14|0.53|0.18|0.54|0.15|-0.02|(0.03)|171|
|Birth year former chief|CQ45|1937.83|20.82|1935.52|20.84|2.31|(5.14)|66|
|Former chief democratic|CQ48|0.16|0.37|0.16|0.37|0|(0.06)|150|
|Share in agriculture|CQ15|75.79|17.34|71.61|19.79|4.18|(2.85)|170|
|Share in herding|CQ15|8.48|8.34|9.74|13.34|-1.26|(1.8)|153|
|Share in commerce|CQ15|5.83|14.21|2.41|4.85|3.42**|(1.7)|156|
|Share in fishing|CQ15|2.96|8.13|5.64|13.2|-2.68|(1.75)|155|
|Share in industrial|CQ15|0.19|1.27|0|0|0.19|(0.14)|155|
|Share in mining|CQ15|2.01|4.7|2.51|7.09|-0.49|(0.97)|156|
|Share in other|CQ15|3.7|4.41|6.02|10.17|-2.31*|(1.26)|157|
|Share in other services|CQ15|5.47|5.11|5.06|4.49|0.41|(0.77)|157|
|Wells in 2006|CQ23|1.07|2.11|0.88|1.93|0.19|(0.31)|168|
|Schools in 2006|CQ24|2.37|4.02|2.96|4|-0.6|(0.62)|169|
|Clinics in 2006|CQ25|0.49|1.37|0.28|0.48|0.21|(0.16)|170|
|Churches in 2006|CQ26|1.62|1.76|1.68|1.75|-0.06|(0.27)|169|
|Halls in 2006|CQ27|0.02|0.15|0.07|0.46|-0.05|(0.05)|169|
|IDPs in 2006|CQ136|3.3|9.51|2.93|12.53|0.37|(2.11)|114|
|IDPs returned in 2006|CQ137|2.36|12.52|7.79|28.52|-5.43|(4.26)|110|
|Refugees in 2006|CQ138|0|0|1.1|5.24|-1.1|(0.7)|119|
|Refugees repatriated in 2006|CQ139|0|0|0.05|0.38|-0.05|(0.05)|122|
|Share of male respondents|QF7|0.5|0.5|0.5|0.5|-3.79|(0.01)|5457|
|Average age respondents|QF9|20.49|17.21|19.81|16.84|-0.45|(0.47)|5198|

Note: Question number responds to 2010-2011 survey (Humphreys et al. 2012). Tests of difference based on simple OLS regressions. * p<0.10, ** p<0.05, *** p<0.01 (two-tailed). Data only from the 190 villages that participated in the gender parity lottery.

11
<u>Table 1. Mean hours per day, by sex and type of activity</u>

|||Unid<br>Wo|men|Lir||Unid<br>Me|n|Lir|
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>pa<br>domestic<br>work|Personal<br>care|<br>esue,<br>social and<br>study|Market<br>work|<br>pa<br>domestic<br>work|Personal<br>care|<br>esue,<br>social and<br>study|
|Albania|2.05|5.50|11.31|5.07|4.50|0.61|12.02|6.86|
|Argentina|2.61|3.97|n.a.|0.03|5.18|1.29|n.a.|0.03|
|Bangladesh|1.67|5.69|14.23|2.65|6.18|1.40|13.36|3.06|
|Colombia|3.01|4.25|10.18|6.66|5.64|1.17|9.91|7.15|
|Ecuador|2.72|5.61|9.76|4.53|5.66|1.37|9.50|4.63|
|Ghana|2.67|5.80|12.46|4.27|3.05|3.89|11.97|6.13|
|Guatemala|2.64|6.70|10.97|2.48|6.76|1.14|11.05|3.13|
|Iraq|0.57|4.53|n.a.|6.11|5.43|1.40|n.a.|6.47|
|Mexico|3.17|5.87|n.a.|13.33|6.60|1.68|n.a.|13.18|
|Moldova|2.91|4.67|10.91|4.25|3.63|2.98|11.08|5.33|
|Mongolia|2.90|6.41|11.49|3.31|3.50|5.41|11.55|3.98|
|Pakistan|0.61|6.32|13.48|3.55|4.41|2.53|12.47|4.56|
|West Bank and Gaza|0.98|2.97|11.23|6.22|5.41|0.42|11.27|6.51|
|Peru|3.20|5.69|9.77|4.80|6.38|2.16|9.54|5.44|
|South Africa|2.58|4.36|12.41|4.89|4.23|2.16|12.31|5.84|
|Timor|1.16|2.24|n.a.|n.a.|2.73|1.26|n.a.|n.a.|
|Turkey|0.93|5.20|10.94|5.17|3.78|0.48|10.80|5.31|
|Uruguay|4.27|6.81|9.43|2.30|6.68|2.39|9.18|2.17|
|USA|3.44|3.27|10.78|5.56|4.87|2.09|10.35|6.17|

Source: Authors’ elaboration based on survey data. Notes: Women and men aged 15 to 64. In Argentina, the starting age is 18.

<u>Table 2. Mean hours per day by educational level, type of activity and sex</u>

|||<br> <br>Wom|<br>en|<br> <br>Primar|<br>ylevel|<br> <br>M|en||
|---|---|---|---|---|---|---|---|---|
||Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|Market<br>work|<br>Unpaid<br>domestic<br>work|Personal<br>care|<br>Leisure,<br>social and<br>study|
|Albania|1.89|6.02|11.31|4.71|4.52|0.59|12.03|6.84|
|Argentina|2.03|4.45|n.a.|0.02|5.12|1.24|n.a.|0.01|
|Bangladesh|1.81|5.97|14.61|2.02|6.70|1.50|13.58|2.22|
|Colombia|2.43|5.47|10.20|5.86|6.45|1.25|9.98|5.94|
|Ecuador|2.47|6.23|9.84|3.51|5.86|1.46|9.57|3.73|
|Ghana|2.36|5.98|12.61|4.22|1.77|5.04|12.51|5.41|
|Guatemala|2.37|7.48|11.16|1.77|7.34|1.17|11.26|2.26|
|Iraq|0.32|4.89|n.a.|5.62|6.33|1.43|n.a.|5.44|
|Mexico|2.38|6.47|n.a.|11.83|6.74|1.59|n.a.|11.69|
|Moldova|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Mongolia|0.91|8.13|11.56|3.39|1.46|7.29|11.65|3.90|
|Pakistan|0.58|6.81|13.57|3.00|4.33|3.39|12.65|3.60|
|West Bank and Gaza|0.97|1.74|11.74|6.05|5.56|0.38|11.65|6.00|
|Peru|3.08|6.91|10.00|3.23|6.86|2.49|9.97|3.89|
|South Africa|2.20|4.48|13.21|4.32|3.58|2.12|13.49|5.38|
|Timor|1.09|2.68|n.a.|n.a.|3.44|1.30|n.a.|n.a.|
|Turkey|0.91|5.89|10.84|4.96|4.23|0.52|10.82|5.09|
|Uruguay|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|USA|2.72|4.85|10.75|4.15|5.11|2.38|10.16|6.14|

28
ing. A large network of CHWs typically performs routine home visits in their area of residence for various services unrelated to contraception, thus avoiding the need for extra appointments and reducing the ability of the social environment to infer about the provision of injectable shots.<sup>2</sup> We take advantage of a unique setting in Burundi, where Sayana Press, a new generation of non-intramuscular contraceptive injections, had been deployed across the country in recent years. Their operational advancement consists of simplified injections by using a pre-filled syringe for subcutaneous administration, making it easier for lower-skilled professionals to administer while offering the same three-month protection as the traditional injectable method (Keith et al., 2014; Askew and Wells, 2018). However, their key attribute for rural areas—safe administration by CHWs (Burke et al., 2014)—was not yet leveraged. In coordination with health authorities willing to allow administration upon proper theoretical and practical training more broadly and with a large World Bank operation providing financing, we implemented an intervention at the health-center level to make CHWs eligible for administering non-intramuscular injections.<sup>3</sup> It comprised a theoretical training that would help CHWs encourage rural women to visit health centers for medical assessment and receive their initial non-intramuscular injection. After completing a practical phase, CHWs became eligible to administer injection renewals during their routine home visits in the communities. Rather than the overall effect of introducing the new generation of injectables, the randomized intervention allows us to evaluate their novel feature of a substantively easier but still equally safe administration, which makes it feasible to entrust CHWs with the deployment of contraceptive injections in remote areas.

Using detailed administrative data from health centers, we find that the average number of non-intramuscular injections administered was approximately 13 units higher per month within health centers’ catchment areas due to the intervention, representing roughly a 70% increase over the mean of the control group. This statistically significant increase was observed during both the theoretical and practical phases of the CHW training. After certification, there was also a strong and statistically significant increase in renewal injections administered by CHWs

> 2 There is also the possibility that the intervention improves bodily autonomy vis-a-vis the partner (Ashraf et al., 2014) and other household members, as appointments can be scheduled at short notice at a convenient time when privacy is ensured.

> 3 As explained in Section 2, health centers manage services provided both in their facilities and within communities in their designated catchment area.

4
26 in 46 percent of countries with competitive elections, compared to 27 percent of East Asian countries with competitive elections.

**Table 3:  Intra-regime variation in the organization of citizens for collective action** **<u>(2010)</u>**

||N|Mean|Standard<br>Deviation|Minimum|Maximum|
|---|---|---|---|---|---|
|**No competitive elections**||||||
|Age of largest governing party when leader<br>took power|61|15.3|21.1|0|82|
|Average of programmatic dummy variables<br>over all parties|60|.33|.40|0|1|
|Both largest government and largest<br>oppositionpartyareprogrammatic|50|.1|.30|0|1|
|**Competitive elections**||||||
|Age of largest governing party when leader<br>took power|113|32.7|38.1|0|160|
|Average of programmatic dummy variables<br>over all parties|114|.62|.35|0|1|
|Both largest government and largest<br>oppositionpartyareprogrammatic|114|.46|.50|0|1|
|**All countries**||||||
|Age of largest governing party when leader<br>took power|174|26.6|34.1|0|160|
|Average of programmatic dummy variables<br>over all parties|174|.52|.39|0|1|
|Both largest government and largest<br>oppositionpartyareprogrammatic|164|.35|.48|0|1|

Does this apparent cross-country variation in the ability of citizens to act collectively also explain differences in policy choices across countries? That is, do countries exhibit better development outcomes when their parties are better organized to solve citizen collective action problems? Table 4 reports results that begin to answer these questions.

The estimates are based on the same specifications and samples as in Table 1, modified by the addition (one by one) of the measures of obstacles to collective action by citizens. The
# **BOX 1: Electronic Sales Registration Machines in Ethiopia (Ali et al., 2021; Mascagni, Mengistu and Woldeyes, 2021)**

**Problem:** Despite its in-built self-enforcing mechanism that creates opposite incentives for buyers and sellers, the collection of VAT is thought to be largely underperforming in low and middle - income countries (Keen and Simone, 2004; Gordon and Li, 2009; Besley and Persson, 2013; Pomeranz, 2015). One of the main reasons, apart from the administrative complexity, is widespread evasion as the lack of information on sales severely undermines the enforcement capacity of revenue authorities.

**Technology intervention:** The Ethiopian Revenue and Customs Authority (ERCA) started the rollout of electronic sales register machines (ESRMs) in 2008 with the goal of moving from paper-based receipts, which make monitoring business transactions on a daily basis extremely expensive and unfeasible, to a digital solution. ESRMs, on top of printing out receipts, automatically report transactions via a network to an ERCA server – with the agency being able to monitor sales and eventually use that information to enforce compliance.

**Research design:** The studies of Ali et al. (2021) and Mascagni, Mengistu and Woldeyes (2021) heavily rely on administrative tax data provided by the revenue authority. The authors have access to tax returns and payments data over a large span of years, as well as to the date of machine adoption. Both studies employ a difference-in-difference design. In Ali et al. (2021), the design is reinforced by a matching technique making adopters and non-adopters more comparable. In Mascagni, Mengistu and Woldeyes (2021), several restrictions are imposed on the sample (for instance, never adopters are excluded), and a triple difference-in-difference is performed to reduce imbalance between treatment and control groups.

**Impact:** Impacts from ESRM adoption are largely positive. On the one hand, Ali et al. (2021) find that the adoption leads to increase in reported sales and VAT liability. They also show that this improvement happened without the erosion of the tax base, as there was also an increase in employment rates. On the other, Mascagni, Mengistu and Woldeyes (2021) find a positive impact on tax revenue, increasing by 48 percent for VAT. Considering also income taxes, they rise by 12 percent after adoption, indicating spillover effects. Further, they measure positive impacts on the accuracy of reporting. However, such positive effect is partially muted by strategic taxpayer response, which will be discussed in more detail in Section 6. In Ethiopia, taxpayers respond by simultaneously adjusting both reported sales and costs, thus yielding net revenue gains that are proportionally lower than the increase in sales.

The key lesson from the two studies is that technology can improve compliance, but perverse responses from taxpayers can partially mute its impact.

In a different context, Rwanda, Eissa and Zeitlin (2014) find that the first version of electronic billing machines (EBM v1) that automatically records a firm’s sales leads to an 9