rafmacalaba/data-use-mentions-extended huggingface dataset:
That notwithstanding, there is a growing push for harmonizing the methodology for producing poverty statistics to ensure regional and global comparability (Mancini and Vecchi 2022; Zezza et al. 2017), including the transition from diary to recall methods, due in part to the high cost and human capital requirements (in the form of literacy and supervision) in diary implementation (Beegle et al. 2012). The East African Community (EAC) is one such regional body that is recommending to member countries to use the 7-day recall methodology for food consumption data as part of a broader effort to harmonize statistics in the region in general. Mancini and Vecchi (2022) recommend that any methodological change (e.g., from diary to recall or vice versa) should be preceded by an experiment to ensure that there is no break in the poverty series.

These harmonization efforts and the experimental recommendation by Mancini and Vecchi provide an interesting avenue for this study in Tanzania. Since the work of Beegle et al. (2012) on understanding how different methods of collecting food consumption data could impact consumption aggregates and ultimately, living standards measurements, several other studies have been conducted in this area (see for e.g., Christiaensen et al. 2022; Battistin et al. 2023; Brzozowski et al 2017). These studies generally focus on understanding measurement errors in food consumption data, and different approaches for improving the quality of measurement. There are other studies that have fielded country-specific food consumption data collection methods to compare them with potentially new methods to be adopted by the respective country (Durazo et al. 2017; Backiny-Yetna et al. 2017).

This study adds to the growing body of literature on measuring household food consumption expenditure by using nationally representative randomized household survey data from Tanzania to compare two methods of data collection – recall and diary. The study compares 3
housing demand. Public hostel or rental programs for the young have been set up in several countries but seem rather insignificant. Furthermore, the “residualization” of the social rental sector is mirrored by the marginalization of its tenants in terms of income and age profile in all countries. The exceptions are Poland - which continues to have an active public housing policy - and Russia, with its large and yet undefined social rental stock.<sup>102</sup> Although only equilibrium values of tenure patterns and rent levels can be observed, the conclusion from the quantity-price relations observed in the studied countries is that rental housing demand in transition economies is rather strong. The housing expenditure and rent data suggest that, with the exception of Poland (which has residues of rent control), private rental housing costs are high relative both to values typically obtained in transition countries, and to public rental or owner-occupied housing expenditures. However, they are moderate compared with the rental burden in Western economies.

In terms of the efficiency of matching demand and supply through market mechanisms - including non-institutionalized and informal ones - the studied countries may be divided into three broad groups:

- The most advanced transition (and EU accession) countries (Lithuania and Poland) experience high demand pressure for renting, with supply being somewhat responsive, mitigating the upward pressure on rents.

- The mid-advanced transition countries (Romania and Russia) experience high demand pressure for rental apartments, which coincides with a pronounced scarcity of supply.

- The least advanced post-conflict countries (Armenia and Serbia) experience relatively subdued demand for housing in general, including rental housing.

These groups correspond to countries’ progress along the general reform path.

# **Regional Tenure Policies**

# **_Legal, Regulatory, and Tax Reform_**

Progress in legal and regulatory reform of the rental housing sector in the region has been painfully slow, with strong political resistance by numerous privatized tenant-owners and social housing tenants. These tenants dominate the political arena, given that they continue to enjoy
Figure 6: EAP: Infrastructure, environment, health, and education indicators

# A. Ranking of overall infrastructure

# B. Quality of trade and transport-related infrastructure

<!-- Start of picture text --> Ranking 2019 2011-12 Index 2018 2010 World<br>150 4<br>120<br>3<br>90<br>2<br>60<br>1<br>30<br>0 0<br>C. Share of urban population living in D. Environmental performance<br>slums<br>Percent of urban population Index 2020 2010<br>60 2020 2010 World 60<br>45<br>40<br>30<br>20 15<br>0<br>0<br>-15<br>E. Under-5 mortality rate F. Ranking of capacity to retain or<br>attract talent<br>Number per 1000 live births Ranking 2019-2021 2016-18<br>2020 2010 World 120<br>75<br>90<br>50<br>60<br>25<br>30<br>0 0<br>East Asia Island economies<br>Malaysia China Thailand Indonesia Vietnam Lao PDR Philippines Mongolia Cambodia<br>Myanmar Cambodia Philippines China Mongolia Thailand Vietnam<br>China Malaysia Mongolia Vietnam Indonesia Cambodia Myanmar Lao PDR Tonga Palau Samoa Solomon Is. Tuvalu Vanuatu Fiji Nauru Marshall Is. PNG Kiribati<br>Lao PDR Indonesia<br>Thailand Philippines Micronesia Timor-Leste<br>Malaysia Thailand Philippines Indonesia China Lao, PDR Cambodia Vietnam Mongolia Myanmar<br>China Malaysia Thailand Vietnam Indonesia Philippines Lao PDR Cambodia Mongolia Myanmar<br>Malaysia China Philippines Thailand Indonesia Mongolia Vietnam Lao PDR Cambodia<br><!-- End of picture text --> Sources: Environmental Performance Index; World Economic Forum; World Bank, World Development Indicators database.

A. World Economic Forum ranking of 140 countries according to the quality of their infrastructure. 1=
9 America (with PAHO) (Wagstaff et al. 2015), then in the Universal Health Coverage Study Series (UNICO) countries (Wagstaff et al. 2016), and more recently globally (with WHO) (Wagstaff et al. 2018a; Wagstaff et al. 2018b). The 2018 HEFPI data set includes the datapoints contributed by the World Bank to the joint 2017 WHO-World Bank data set (80% of the total), but also many others generated (by the World Bank) since. With the new datapoints, the financial protection part of the 2018 HEFPI data set is larger than the 2017 WHO-World Bank data set and covers more countries. On both the health equity and financial protection sides of the HEFPI database, the country coverage has expanded as well – from 109 countries in 2012 to 193. The number of household surveys used has increased even more dramatically – from 285 in 2012 to over 1,600.

The datapoints in the 2018 HEFPI data set, like those in the previous three data sets, have, wherever possible, been computed from the original microdata. For some surveys, this was not possible, and we have had to make do either with published reports (as in the case, for example, of the STEPS surveys) or with studies by researchers who have used the same methods as us (see e.g. Van Doorslaer and Masseria 2004; Van Doorslaer et al. 2006a). One goal behind re-analyzing the original microdata was to ensure maximum consistency across surveys, countries and years. Sometimes, this means that our data are not identical to those on the websites and in the reports of the organizations that produced the microdata. For example, we use the same (2006) standards for childhood stunting and underweight in all surveys. The DHS reports, by contrast, use whatever standard was in force at the time the survey was done. In addition to ensuring consistency, there was a second reason to re-analyze the microdata: to ensure we have data
. Drought affected status and humanitarian assistance are not included in the OLS regression due to the lack of variation in the data within Mogadishu. Humanitarian assistance is not included in the IV regression due to collinearity with the region fixed effects. Standard errors considering sampling weights in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

We further explore the mechanisms through which a terrorist attack from Al-Shabaab against civilians can lead to a decrease in consumption. Table 5 presents the results for the impact on the proportion of household members aged 15 to 50 that were employed and earned income after an incident. The DiD coefficients are obtained from the sample of households in Mogadishu, while the IV coefficients from urban areas with exposed and control households in Waves 1 and 2; both from our most complete specification. All estimates are negative and statistically significant. Similar to other outcomes, IV point estimates are larger and have bigger standard errors. However, the IV estimates also reinforce the DiD findings, which indicate a decrease of around 15% in the share of both, household members working and earning income between 1 and 7 days after an incident. A reduction in employment and income could lead to a decline in consumption and exacerbate poverty and vulnerability among households exposed to an incident.

There are other supply-side mechanisms, such as limited availability of food items and higher prices, which could help explain how a terrorist attack disrupts the economy and affects welfare conditions of the population. To assess this, we compare the cost of the consumption basket –made out of 38 core food items– of exposed households from Mogadishu in Wave 2 against that from a group of households also in Wave 2 located within 1 kilometer from incidents in Mogadishu but that were interviewed before the 18
-45-

# Levels of Value Added in 17 Industries

Table 3.9 shows Brazilian and Mexican value added in US dollars at official exchange rates, compared with US figures for value added derived from the Annual Survey of Manufactures.

------------------------------------------------------------

__--------------

|Value Added (US Census Concept)<br>at official<br>exchan<br>-----------------------------------------------------------------|TABLE**3.9**<br> <br>in Brazil,<br>M<br>ge<br>rates<br>(1975<br>|exico and the<br>US dollars)<br>|USA in 1975,<br>__---------<br>|
|---|---|---|---|
||Brazil<br>(million<br>dollars)|Mexico<br>(million<br>dollars)|USA<br>(million<br>dollars)|
|Total Manufacturing<br>Value Added|37,748.2|17<sup>,</sup>585 <sup>.6ab</sup>|<sup>442,485.2</sup>|
|Grain Mill Products
# **VI. Conclusions**

In this paper we use a comprehensive cross-country firm-level data to understand how formal African firms stack up against those in other regions. We also explore whether Africa’s political and business environment can explain its performance shortfalls (if any), and what the key factors are for explaining Africa’s performance. We begin by comparing the unconditional means of various performance measures. We find that formal African manufacturing firms show significant disadvantages in all measures of firm performance. In the unconditional means, African firms exhibit lower total factor productivity (TFP) levels, lower labor productivity levels and growth rates, lower sales growth, and lower investment rates and export orientation.

Once we control for key differences in firm characteristics, geography, infrastructure, political and institutional factors, business environment, and access to finance, Africa manufacturing firms actually exhibit a _conditional advantage_ in productivity levels and growth, as well as sales growth. This conditional advantage remains reasonably intact across a variety of robustness checks and additional specifications. Most importantly, the negative and significant coefficient on the Africa dummy becomes either positive or insignificant when controlling for observable factors in the environment faced by these firms. The implication is that African entrepreneurs are not inherently disadvantaged. In light of the highly challenging environment, they actually perform better than those in other regions of the world at similar income levels.

The conditional advantage for African firms is higher in low-tech than in high-tech manufacturing, and in manufacturing than in services. The key factors in explaining Africa’s disadvantage in firm performance relative to better-performing similar income countries are infrastructure, lack of access to finance and political monopoly. The impacts of telecom and finance, especially informal finance, are felt across the board. In contract, the damages of party 34
providing information on perception of international investors of corruption in a country in comparative context. International investors’ perception of the Bulgarian investment climate has substantially improved since 1998. The verdicts given by participants of Transparency International annual surveys have been increasingly favorable for Bulgaria. While in the mid 1990s Bulgaria together with Romania was ranked somewhere between CEEC-8 and CIS economies, the latter notorious for high incidence of corruption, Bulgaria has been moving up. It has not only the ‘cleanest’ institutional environment among Balkan countries but, by 2000, Bulgaria has also become like other CEEC-8 countries in terms of the value of CPI (Table 2). The decline in perception of corruption has been impressive since 2000, as Bulgaria has been moving towards the median CPI value of 4.4 in 2000-04 for CEEC-8 countries. Bulgaria’s current rating stands at 87 percents of the CEEC-8 average and 93 percent the CEEC-8 median value of CPI.

**Table 2:** _Values of Corruption Perception Indices for selected countries in 1998-2000 and 2002-2004_

||1998|1999|2000|2002|2003|2004|Index, 2004<br>2000=100|
|---|---|---|---|---|---|---|---|
|Bulgaria|3.3|3.5|3.5|4.0|3.9|4.1|117|
|Romania|3.3|2.9|2.6|2.8|2.8|2.8|108|
|Average CEEC-8|4.2|4.6|4.5|4.6|4.6|4.6|104|
|Best 1999-2004: Slovenia<sup>**a/**</sup>|n/a|6.0|5.5|6.0|6.0|6.0|109|
|
# **Appendix 1: Descriptive Statistics Couple-Level Data**

||Mean|Std dev|
|---|---|---|
|Religion<br>|||
|Fraction of Muslim couples|0.78||
|Men’s sibling composition at time of first child birth|||
|Fraction of men who have at least one living brother|0.80||
|Men’s average total number of living siblings|3.97||
|Year of first child birth|||
|Fraction of couples with first birth in 1975-1979|0.039||
|Fraction of couples with first birth in 1980-1984|0.10||
|Fraction of couples with first birth in 1985-1989|0.14||
|Fraction of couples with first birth in 1990-1994|0.18||
|Fraction of couples with first birth in 1995-1999|0.20||
|Fraction of couples with first birth in 2000-2004|0.22||
|Fraction of couples with first birth in 2004-2007|0.11||
|Fertility preferences and actual outcomes|||
|Women’s ideal proportion of sons|0.42|[0.21]|
|Women’s ideal number of sons|1.23|[0.86]|
|Women’s ideal number of daughters|1.22|[0.85]|
|Women’s ideal number of children|2.96|[1.29]|
|Men’s ideal proportion of sons|0.41|[0.24]|
|Men’s ideal number of sons|1.28|[1.02]|
|Men’s ideal number of daughters|1.18|[0.94]|
|Men’s ideal number of children|3.14|[1.47]|
|Couple’s actual proportion of ever-born sons|
–12 Bank Regulation and Supervision Survey is available at http://go.worldbank.org/WFIEF81AP0. Data from previous iterations of the survey are available at http://go.worldbank.org/SNUSW978P0.

> 4 See Table 1 for a detailed list of countries in this survey. The distinction between “advanced economies” and “emerging market/developing/economies” follows IMF’s September 2011 _World Economic Outlook_ .

4
# **C- Western countries and Japan**

<!-- Start of picture text --> Norway: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Japan: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>UK: Spactral Density Function of Real Credit<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>
**Table 4: Percentage of Responses with Complete or Some Distrust in Courts**

|No|Commercial<br>court|Number of<br>respondents|Complete or<br>some distrust|
|---|---|---|---|
|1|Belgrade|260|62.93|
|2|Kragujevac|100|50.00|
|3|Kraljevo|80|43.04|
|4|Leskovac<sup>*</sup>|20|85.00|
|5|Niš|60|68.33|
|6|Novi Sad|80|65.00|
|7|Pančevo|60|46.67|
|8|Požarevac|40|60.00|
|9|Sombor|40|77.50|
|10|Subotica|40|57.50|
|11|Užice<sup>*</sup>|20|25.00|
|12|Valjevo|40|65.00|
|13|Zaječar|60|66.67|
|14|Zrenjanin|100|65.00|
||Total|1000|59.83|

* Note that commercial courts Leskovac and Užice are excluded in our analysis Source: 2006 Life in Transition Survey

**Table 5: Summary Statistics**

|2003-2011||||||||||
|---|---|---|---|---|---|---|---|---|---|
||N|mean|min|max|p10|p25|p50|p75|p90|
|**_# of new firms registered_**|108|559|70|4507|110|166|246|353|1020|
|**_# of new firms entering_**|108
of previously underreported earnings to increase maternity benefits.

We estimate an event study regression following Kleven et al. (2019) to characterize the time pattern of reported earnings among expectant mothers relative to otherwise similar women who do not give birth: where _i_ indexes individuals, _t_ indexes calendar time, _Eit_ indicates event time, _Wit_ is the log of reported monthly earnings, **A** _it_ is a vector of age dummies, _γi_ captures individual fixed effects, _δt_ captures calendar month effects, and _Bi_ is a binary indicator for giving birth in the sample. Event time for women who did not give birth is defined by randomly assigning a placebo birth year and month. The coefficients of interest are the _βj_ , which capture the differential evolution of log monthly earnings for women who gave birth relative to women who did not. We set _−_ 13 <u>�</u> _j_ = _−_ 30<sup>_βj_= 0, thus the estimated coefficients show the deviation from the average (conditional)</sup>

> 7For a comprehensive overview of the administrative data, see Seb ̋ok (2019).

> 8We do not consider a childbirth as first if we observe a prior maternity payment receipt. While we do not have information on births before 2009, maternity benefit data are available for 2003-2017.

6
# 1 Introduction

The environmental conditions experienced in utero and in early life have profound inuence on human biology and long-term health (Golden, 1994; Martorell et al., 1994; Forsdahl, 1977; Barker, 1992; Bateson et al., 2004; Gluckman et al., 2007, 2008). Similarly, early life conditions have lasting and signicant impacts on adult economic outcomes (see reviews by Strauss and Thomas, 2008; Currie and Vogl, 2013). These ndings are highly relevant in the context of civil conicts, which cause considerable human suering, death and property destruction. Despite the potential for conict to contribute to lasting health impacts, the empirical evidence on long-term and intergenerational eects of conict on health is limited.<sup>1</sup> Lack of conict data at detailed geographic scale has made it dicult to measure precisely the consequences of conicts.

In this paper, I investigate the impacts of early childhood exposure to Nepal's 1996-2006 civil conict on women's nal adult height and on second-generation health using the 2016 Nepal Demographic Health Survey (NDHS) and village-level variation in conict intensity. By exploiting detailed geographic information on conict incidents (village-level conict intensity), I am able to identify the eects of exposure to conict more accurately than previous research.<sup>2</sup> The literature on the consequences of war, including in Nepal<sup>3</sup> has thus far mostly focused on conict variation at

> 1Previous studies have documented long-run eects at the cross-country level, suggesting conicts have large and negative immediate eects on overall economic growth. However, the recovery to equilibrium is rapid (see review by Blattman and Miguel, 2010). Correlated with war exposure, the literature has extensively documented long-run eects of exposures to stress on mental health (Persson and Rossin-Slater, 2018), height and diseases (
Policy Research Working Paper 8782

# **Abstract**

The Three-Gap Model examines the determinants of low-quality health care by examining the patterns and determinants of three gaps. Using four measures of performance—target performance, actual performance, capacity to perform, and knowledge to perform—this paper defines three gaps for each health worker: the gap between target performance and what they have the knowledge to do (the know gap), the gap between their knowledge and their capacity to perform (the know-can gap), and the gap between their capacity and what they actually do (the can-do gap). The paper demonstrates how the patterns of these gaps across health workers in a sample can be used to diagnose failures in the system as well as evaluate the outcomes of policy experiments. Using data on pediatric care from hospitals in Liberia, the paper illustrates how the model can be used to investigate the potential for improvements in the quality of care from several possible policy interventions. The analysis of the relationships between these gaps across health workers in a health system help to paint a better picture of the determinants of performance and can assist policy makers in choosing relevant policies to improve health worker performance.

This paper is a product of the Health, Nutrition and Population Global Practice. 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 ibnatf@terpmail.umd.edu, kleonard@arec.umd.edu, and rmohammed@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
More specifically, for the rec+ scenarios, the increases in government health and education spending (split into consumption [current] and investment [capital]) during the period 2023-2026 are defined to permit the country to deliver services typical of the 90<sup>th</sup> LIC percentile starting from 2026.<sup>9</sup> It is assumed to generate a more gradual increase in Yemen’s human-capital index (HCI) to the 90<sup>th</sup> LCI percentile during the period 2023-2030 as a growing part of the labor force benefits from improved health and education. In its turn, labor productivity grows relative to the 2022 level at a rate that, according to cross-country evidence, is expected given the HCI increase.<sup>10</sup> For infrastructure, the rec+ scenarios impose gradual increases in consumption (operations and maintenance) and investment spending that, on average may be sufficient for low- and middleincome countries to achieve the infrastructure-related sustainable development goals (SDGs; Rozenberg and Fay 2019; Foster et al. 2022). Drawing on cross-country estimates (Gupta et al. 2014, p. 171), it is assumed that the infrastructure capital stock has a marginal productivity (MP) of 0.30 for rec+t and rec+ta, and 0.60 for rec+ta+. The gains are realized in the form of increases in total factor productivity (TFP); more specifically, once installed and other things being equal, value-added increases by 0.30 (0.60) YER for every additional YER of infrastructure capital with the gains spread across all sectors in proportion to their initial shares in value added. Finally, the scenarios rec+ta and rec+ta+ deviate from rec+t in that additional foreign grant aid is received: in each year 2023-2026 foreign grants increase by 1 percent of 2022 GDP (i.e., reaching 4 percent in 2026); the increases are