rafmacalaba/data-use-mentions-extended huggingface dataset:
and 11% in Sierra Leone. These low values suggest that large-scale utilization of azithromycin as a COVID19 treatment would be difficult without substantially increasing stocks in facilities.

While antibiotics (other than potentially azithromycin) are not viable treatments for COVID-19, examining a composite indicator of availability of any of a wide range of antibiotics (amoxicillin, azithromycin, ampicillin) in the facility can serve as a proxy for availability of key medicines more broadly. Encouragingly, over 90% of health facilities in Kenya had at least one of these three common antibiotics and over 80% in Sierra Leone and Tanzania, however, it was rare for a facility to have all three. Figure 9 indicates that the availability of all three of these antibiotics is highly variable, with most of the variation coming from differences in availability between hospital and lower-level facilities (as opposed to urban/rural location) (Appendix Tables 3 and 4).

Information on other relevant treatment equipment and supplies (such as oxygen cylinders, pulse oximeters, etc.) is not available from recent SDIs, and as described above, the depth of stocks is also unknown. While this could be estimated based on direct observation or patient exit interviews, for example, the frequency of utilization of equipment, supplies, and medications is also not quantified.

**Figure 9** : Availability of amoxicillin, azithromycin, ampicillin by facility type (hospital vs. non-hospital) and urban/rural location* in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Hospitals Non-hospitals<br>Kenya - Rural<br>Kenya - Urban<br>Sierra Leone - Rural<br>Sierra Leone - Urban<br>Tanzania - Rural<br>Tanzania - Urban<br>0 1 2 3<br>Number of antibiotics available<br><!-- End of picture text -->

> * Sierra Leone sample did not have any rural hospitals, and therefore a single dot for the “Sierra Leone –
# **Table A6: Attrition test – Balanced sample**

|Dependent variable:|Employment<br>status|Weekly labor income<br>($1,000 AUD)|
|---|---|---|
||(1)|(2)|
|Standardized mental health scores|-0.048***<br>(0.010)|-0.200***<br>(0.065)|
|_First stage of 2SLS (dependent varia_|_ble is standardized m_|_ental health scores)_|
|Trauma experience*Year after<br>arrival|-0.044***<br>(0.011)|-0.063***<br>(0.020)|
|Kleibergen-Paap F stat.|17.627|19.635|
|AR 95-CIs|[-0.094, -0.019]|[-0.469, -0.124]|
|Dep. Mean|0.455|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|2,497|2,497|
|Number of individuals|1,003|1,003|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of IV-Logit-FE model (marginal effects) in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6-item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental
# Figure 13: MNA: Investment growth and correlates

A. Investment growth <!-- Start of picture text --> Percent<br>15 2000-21 average<br>10<br>5<br>0<br>-5<br>-10<br>MNA EMDEs<br>C. Investment<br>Index, 2019 = 100<br>130 Current<br>125 Pre-pandemic<br>120<br>115<br>110<br>105<br>100<br>95<br>90<br>2019 2020 2021 2022 2023<br>E. Terms of trade<br>Index, 2010 = 100<br>130 Oil importers Oil exporters<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>Sources: Haver Analytics;PRS Group; World Bank.<br>2010 2012 2014 2016 2010 2012 2014 2016 2018 2020 2022.<br>2010 2011 2012 2013 2014 2015 2017 2018 2019 2020<br>2018 2020 2022<br>2016<br><!-- End of picture text --> <!-- Start of picture text --> B. Economies with below average or<br>negative<br>investment growth<br>Percent of countries<br>Below long-term average Contracting<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2016 2017 2018 2019 2020 2021<br>D. Composition of investment growth<br>Percent<br>Private Public<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>2017 2018 2019 2020<br
(e.g., Russia) or recently reformed (e.g., Brazil); 2) ratings from Moody’s, S&P and or Fitch; and 3) comparable budget balance data for years when rated. Sampling from these 18 countries yields 141 country-year observations from 1989-2004 with as few as three (Ecuador) and as many as 14 (Brazil) observations per country. The election year dummy, _Electionit_ , equals one for 32 of these 141 country-year observations.<sup>2</sup>

> 2 Classification as a presidential system is based on the DPI’s “SYSTEM” variable.  A country must score a 0 or 1, meaning that it has a non-ceremonial chief executive elected directly by voters or specialized electors (0), or have a non-ceremonial chief executives chosen by the legislature but with substantial powers to avoid recall by that same

5
nationally representative of households that have phones; hence their national representativeness depends on the country’s mobile phone penetration. In addition, by design, the questionnaires are much shorter (ideally, they should take less than 15 minutes to complete, with relatively simple questions) than standard face-to-face surveys, to ensure quality of responses. This means that the resulting indicators collected may differ from the official definitions (for instance, on employment) and that the analysis may be less comprehensive than what can be done with standard labor force surveys or the equivalent.

> 4 Note that the data used in this paper does not come from the World Bank’s global harmonized data set. While from the same sources of raw data, the data used here has been harmonized specifically for the purposes of this paper.

> 5 Four countries – Cambodia, Malaysia, Solomon Islands, and Thailand – also administered the HFPS but are not included in this analysis. At the time of this paper, Malaysia and Thailand had only implemented one round, for which data were not yet available. Solomon Islands had low representation of bottom 40 households. Cambodia’s small sample size, particularly in later rounds, made quintile-level disaggregation difficult.

3
# **_2.1.2 Precipitation Shocks_**

To measure precipitation shocks, we use reanalysis data produced by the European Centre for MediumRange Weather Forecasts (ECMWF). More specifically, we use the new land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA-5 Land dataset (Muñoz-Sabater et al., 2021). This dataset is produced by the ECMWF as part of the ongoing operations of the Copernicus Climate Change Service (C3S), a subdivision of the Copernicus program, which is the Earth Observation arm of the space program established by the European Commission. ERA5-Land is a global-scale dataset that contains hourly records of more than 50 key meteorological variables (including precipitation) at a 9 km spatial resolution over the period from 1950 to the present. These records are produced by running downscaled meteorological forcings obtained from the ERA5 climate reanalysis<sup>1</sup> through a high-resolution land surface process model developed by ECMWF. For our application, we use the daily aggregated version of the dataset, which is freely provided on the Copernicus Climate Data Store (CDS) and accessible via Google Earth Engine.

There are several key features of the ERA5-Land data. First, as detailed below, constructing the precipitation shock measure we favor in our analysis involves normalizing daily rainfall observations against a day-of-year and grid-cell specific climate distribution. Computing the relevant moments of these localized distributions requires a long, consistently measured, time series of daily rainfall data. Reanalysis datasets like ERA5-Land have both these features. Second, with a resolution of 9 km, the ERA5-Land data allow us to measure precipitation shocks precisely in the exact areas where the firms in the Enterprise Surveys are located. Finally, with its complete global coverage, using ERA5-Land means we can construct shock measures for any location in the world, and thus for every single firm in the ES data. By contrast,

> 1 For an overview of ERA5, see (Hersbach et al., 2020).

7
between and within components of inequality are positively associated with the economic shock variables. In addition, columns (5) and (6) suggest that bad economic conditions increase the between-component of inequality more than the withincomponent. Therefore, in regard to Afghan refugees in Iran, while economic shocks increase both intercommunity and intra-community inequalities, they have a stronger effect on inter-community inequality.

# **6. Policy and program implications**

This paper investigates the unequal effect of economic shocks in the host country between refugees and host communities. The macroeconomic fluctuations in Iran in the 2010s provide us an excellent testing ground for this topic. Using different rounds of household surveys, we draw an analogy between Afghan refugees and Iranians in terms of the labor market outcomes, income, and consumption expenditure. The numbers show that while Afghan refugees have lower unemployment and layoff rates, they work in lowstatus and unskilled jobs. They participate more in the labor market, work longer hours, and are more productive (get higher hourly wages) than similar Iranians. Their earned income is higher on average, but they have lower unearned income and total income than Iranians. The average per capita expenditure of Afghan households is less than Iranians, even though per capita food expenditure is closer between the two communities.

Regarding the unequal impact of economic shocks, our results imply that Afghan refugees exit the country more often during bad economic times in Iran. Comparing Afghan refugees who stay with all Iranians while controlling for observable characteristics suggests that economic shock disproportionately reduces Afghans’ wages while raising their working hours. As a result, the net effect of shocks on their 13
) EUR (Right) LATAM SSA OTHER<br>LATAM (Right) EUR (Right)<br>e. Local Claims of Foreign Affiliates in All f. Loan to Deposit Ratio, 2010<br>Currencies, 2000:Q1–2011:Q4 (Median) (In percent)<br>20 50<br>140<br>120<br>40<br>15 100<br>80<br>30 60<br>10 40<br>20 20<br>0<br>5<br>10<br>0 0<br>ASIA MENA LATAM<br>SSA OTHER CEE (Right)<br>EUR (Right)<br>Q1 2000 Q4 2000 Q3 2001 Q2 2002 Q1 2003 Q4 2003 Q3 2004 Q2 2005 Q1 2006 Q4 2006 Q3 2007 Q2 2008 Q1 2009 Q4 2009 Q3 2010 Q2 2011 Q1 2000 Q4 2000 Q3 2001 Q2 2002 Q1 2003 Q4 2003 Q3 2004 Q2 2005 Q1 2006 Q4 2006 Q3 2007 Q2 2008 Q1 2009 Q4 2009 Q3 2010 Q2 2011<br>Q1 2000 Q3 2000 Q1 2001 Q3 2001 Q1 2002 Q3 2002 Q1 2003 Q3 2003 Q1 2004 Q3 2004 Q1 2005 Q3 2005 Q1 2006 Q3 2006 Q1 2007 Q3 2007 Q1 2008 Q3 2008 Q1 2009 Q3 2009 Q1 2010 Q3 2010 Q1 2011 Q3 2011 Q1 2005 Q3 2005 Q1 2006 Q3 2006 Q1 2007 Q3 2007 Q1 2008 Q3 2008 Q1 2009 Q3 2009 Q1 2010 Q3 2010 Q1 2011 Q3 2011<br>Q1 2005 Q3 2005 Q1 2006 Q3 2006 Q1 2007 Q3 2007 Q1 2008 Q3 2008 Q1 2009 Q3 2009 Q1 2010 Q3 2010 Q1 2011 Q3 2011<br><!-- End of picture text --> Sources: Panels a–e, IMF, _World Economic Outlook_ , and BIS, Consolidated Banking Statistics, table 9A, table 9B, table 9C; Panel f, World Bank, FinStats.
point, we lost access the administrative data, and hence are unable to track when money was withdrawn.

_Training Intervention:_ This treatment was a training program based on the International Labor Organization’s _Improve Your Business_ (IYB) program. IYB is a five-day program intended to generate growth in microenterprises. The modules covered are marketing, buying, costing, stock control, record keeping, and financial planning. We asked that the training also include additional material on hiring and managing employees, as employment generation is a key outcome of interest in the project. The training was provided by the Sri Lankan Business Development Centre (SLBDC),<sup>19</sup> a Sri Lankan non-profit training institution established in 1984. SLBDC is the most experienced provider of ILO entrepreneurship programs in Sri Lanka, having offered the first training on the island in 2001. All of the SLBDC training staff involved in the project were university qualified and trained under the national-level SIYB training programs conducted by the ILO. Each had a minimum of five years of experience delivering SIYB training. Therefore, any failure to find impacts should not be due to low quality trainers or inexperience with the materials. Those selected for training were offered a stipend of 1,000 LKR and an additional bonus of 1,500 LKR paid at the end if they attended all five days. The stipend was meant to cover transport and the opportunity cost of not working in the business on the training days.

Appendix Table 3 shows that assignment to these supplementary interventions is balanced compared to the control group and wage subsidy only groups in terms of baseline observable characteristics.

> 19 http://www.slbdc-lk.org/

47
the year to December 2012, with a sharp shift in composition from the private sector to the publicly controlled sector. FAI by state‐owned enterprises or enterprises with majority state participation grew by 20.5 percent (year‐on‐year) while private investment growth slowed to 2.9 percent (Figure 3.1).<sup>32</sup> Weak private sector investment adds to concerns about growth prospects as the private sector generates about 65 percent of total investment, around 50 percent of GDP, and 80 percent of employment.

Against this backdrop, this appendix addresses the following questions:

1. How has investment in China evolved since 2010?

2. What has driven the slowdown in China’s investment growth?

3. How large are the spillovers from China’s investment slowdown?

4. Which policies can support an orderly rebalancing of investment in China?

This appendix documents the slowdown in China’s investment growth as well as its shifting composition, with pronounced private sector investment weakness. The slowdown in China’s investment growth may

> Note: This appendix was prepared by Ekaterine Vashakmadze, Hideaki Matsuoka, and Trang Nguyen, with contributions from Raju Huidrom.

> 30 Private investment (“minjian” investment) is defined by the Chinese National Bureau of Statistics as the sum of Fixed Asset Investment (FAI) made by enterprises that are registered as collectively‐owned, cooperative, private sole proprietorship, private partnership, private limited liability company, business individual, or partnership of business individuals. Private (“minjian”) investment also includes FAI by those enterprises in which the above‐mentioned entities hold a controlling ownership stake. 31 Major stimulus was initiated in 2009.

> 32 In the remainder of this appendix, investment is measured as FAI (in nominal terms), for which monthly data are available. Unlike gross fixed capital formation (in real terms) in the national accounts, it includes purchases of land and other already‐owned assets. Real gross fixed capital formation
53|9|2||

# _Source_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows descriptive statistics for the 245 surveyed programs in Ecuador. The unit of observation is a program. Columns (1) and (2) refer to the 153 programs that do not match to students in the individual-level dataset and have at least two students; columns (3) and (4) refer to the 92 programs that match (with 2+ students per program); and column (5) presents the p-value for the t-test of the difference in means between columns (1) and (3). P-value is in boldface 80
COVID-19 vaccination rate. We broadly classify these design choices into questions of measurement and representation (Groves 1989; Groves and Lyberg 2010). Together, the design choices we study span some of the most common sources of error covered under the total (survey) error framework that has been the guiding paradigm for research on data quality (Amaya et al. 2020; Groves and Lyberg 2010; Groves 1989).

We find that the substantial misalignment between survey and administrative data-based estimates of vaccine coverage is only partly explained by measurement and representation issues in survey data. After accounting for observed errors of representation and measurement at the household and individual level, there remains a statistically significant gap of 6%-52% between survey and administrative estimates in our study countries. Our findings indicate that design choices determine the reliability and suitability of the survey data for capturing vaccine coverage. At the same time, our explorative analysis suggests administrative data also suffers from flaws and inaccuracies. Without giving due consideration to both survey design and potential inaccuracies in administrative records, both data sources can lead to different findings and different policy conclusions. Our findings have implications for research and policy making on COVID-19 vaccination but also for future data collection on routine immunization and supplementary immunization activities.

Our study relates and contributes to several strands of literature. Conceptually, we frame our analysis as part of the literature on total survey error and total survey quality (Amaya et al. 2020; Groves and Lyberg 2010; Biemer 2010) as well as studies analyzing the role of design choices in producing credible research and value for development (De Weerdt et al. 2020a; Jolliffe et al. 2023). This research emphasizes the sensitivity of both survey and administrative data to nonclassical sources of measurement error. In this framing, conducting robust research is akin to an optimization problem in which the researcher tries to maximize credible knowledge subject to budget and data quality constraints (Dillon et al. 2020). Improvements in data quality provide opportunities to improve the quality of inference conducted. In
While there exists a large and well-established literature that empirically examines the strength of agglomeration economies for developed countries,<sup>22</sup> there have been comparatively very few papers that have done likewise for developing countries. The main exceptions are recent papers by Duranton (2016), Chauvin _et al_ . (2017), and Quintero and Roberts (2018). Duranton provides estimates of the strength of agglomeration economies for Colombia, while Chauvin _et al_ . do likewise for Brazil, China and India.<sup>23</sup> Quintero and Roberts, meanwhile, present estimates of the strength of agglomeration economies for 16 Latin American and Caribbean countries.<sup>24</sup> In all three cases, these papers estimate the strength of agglomeration economies using either individual or pooled cross-sections of data on workers drawn from either household or labor force surveys. They estimate the strength of agglomeration economies by regressing a worker’s nominal wage on a measure of either the size or density of the city in which the worker lives while controlling for observable characteristics of the worker, including, most notably, the worker’s level of education and workforce experience, as proxied by the worker’s age.

In what follows below, we follow a similar approach to Duranton (2016), Chauvin _et al._ (2017) and Quintero and Roberts (2018) in estimating the strength of agglomeration economies for Indonesia.<sup>25</sup> Hence, we draw on micro-data for Indonesian workers from the same survey – SAKERNAS – that we used to measure commuting flows, to estimate the size of the agglomeration wage premium in a simple cross-sectional regression framework.<sup>26</sup> Importantly, in doing so, we estimate the size of this premium based on each of the different approaches – Duranton’s algorithm, the AI, the two cluster algorithms, and the NTL approach – to defining metro areas.

# _4.1. Empirical framework_

4.1
to total household expenditure as proxy for consumption share. This methodology differs from the one used in this study. We use agricultural production information for individual commodities by drawing data from the Integrated Agricultural Survey (2015) rather than using information on net agricultural sales alone from the Household Income and Expenditure Survey (2014-15). As a result, this report is able to examine the welfare effect of price increases in three main staple food items.

Based on these proxy measures Arndt et al find that the NBR was 10 % for rural areas and -22 % for urban areas for a hypothetical, overall food price increase of 100%. Accordingly, based on the Deaton approximation, they find that a price increase negatively hurts urban consumers while benefitting rural households. In contrast, the findings from our analysis indicate a negative impact on both urban and rural households. Our results are consistent with evidence from other studies in Africa which have found that a larger than expected share of rural households also tend to be net buyers and are likely to lose from food price increases (Minot & Dewina (2015) in the case of rice and maize in Ghana; Wodon & Zaman (2008) in Ghana, Senegal and Liberia). Arndt et al also find regional differences, with urban households in south and central regions of Mozambique being the most negatively affected. Across region and expenditure quintiles, they find that the poorest households in Maputo are the worst affected, while middle income households in rural north and center are likely to benefit the most.<sup>1</sup> In order to overcome the limitations posed by their NBR analysis Arndt et al. extend their analysis by undertaking a general equilibrium analysis for Mozambique, shocking their model with prices that are reflective of the 2007-08 period. They use a CGE model calibrated to a 2003 Social Accounting Matrix (SAM) (see Arndt et al., and Thurlow(2008) for details of their model), along with the household survey data from 2002/03. The
Table 1: Total and Within-Skill-Groups Wage Variance in Latin America, 1995–2015

||Contribution<br>level|of the comp<br> of wage ineq|onent to the<br>uality|Contribution of t<br>component to the<br>inequ|he change in the<br> change in wage<br>ality|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(2)-(1)|(3)-(2)|
||1995|2002|2015|1995-2002|2002–15|
|Between-skill-groups component|29 (0.155)|28 (0.212)|25 (0.146)|24 (0.056)|34 (-0.066)|
|Within-skill-groups component|71 (0.379)|72 (0.557)|75 (0.429)|76 (0.178)|66 (-0.127)|
|Total wage variance|100 (0.534)|100 (0.768)|100 (0.575)|100 (0.234)|100 (-0.193)|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note:_ Total wage variance is the variance of the log hourly wage in the household surveys data set. Within-skill-groups variance is the variance of the residuals from a regression of log hourly wage on a vector of workers’ socio-demographic characteristics, including all possible interactions between education (five categories), experience (five-year bins), and gender. Cells