rafmacalaba/data-use-mentions-extended huggingface dataset:
relating to a household’s assets (including consumer durables) and amenities, including materials used for housing construction and its access to water and sanitation. These variables are then aggregated into an index using factor-analytic methods, with the wealth index being identified as the first principal component of the data. The DHS wealth index comes as a z-score, i.e., standardized with mean zero and standard deviation of unity. So the index is country specific—not intended to be comparable across countries.

We focus on the poorest 20% and 40% of households based on the wealth index. These are arbitrary choices, although the 40% figure does coincide fairly closely with the overall poverty rate found for SSA using the World Bank’s international line.<sup>27</sup> The 20% figure allows us to focus more on the lower part of the wealth distribution. We also provide key results for the full range of the distribution.

It should not be forgotten that the DHS wealth index is a proxy, not a direct measure of wealth. The index focuses on durable and productive asset wealth rather than labor or education wealth, arguably the main assets of many among the poor. When compared to the results of a full-blown consumption survey, the DHS index will undoubtedly count as poor some who are not (often called “inclusion errors”) and count as non-poor some of those who are in fact poor (“exclusion errors”). In practice, policy makers targeting poor households almost never have access to accurate measures of wealth or consumption for the population as a whole, and must rely instead on a relatively small number of indicators, such as those embodied in the DHS index. Nonetheless, we also conduct the analysis using household consumption per capita for the sub-set of countries for which this is feasible. Surveys that contain detailed household consumption data as well as anthropometrics for women and children are not common, but some do exist including within the LSMS (specifically the LSMS Integrated Surveys on
collection and fieldwork improvements, changes in item categorization, differences in poverty line definitions, and the inherent uncertainty in imputation methods all contribute to the potential limitations of the approach. These limitations should be carefully considered when interpreting results .

# **IV. Results**

Evaluating the quality of the imputation process hinges on a critical principle: maintaining the consistency of imputed parameter distributions between the donor and recipient surveys, mainly when these surveys are conducted within a relatively short time frame. Assuming that the populations have similar characteristics and patterns is reasonable in such occasions. Given the six-year interval between the surveys, this situation is expected to apply to the imputation exercise between 2016 and 2022.

However, the reliability of preserving the imputed parameter distributions becomes tenuous when there is a substantial time gap between the surveys. This is particularly true when the parameters are connected to variables that may have undergone significant changes over time. Therefore, when data from 2022 is utilized to project values back to 2010, more critical shifts in consumption patterns might be anticipated.

Being mindful of these limitations, the rest of the chapter is divided into four sections: reconstructing poverty and inequality levels in 2016 and 2010, rebuilding trends between 2010 and 2022, and robustness checks.

1. Reconstructing poverty and inequality levels in 2016

- a. Imputed shares

When examining the total imputed share (food and non-food) in 2016, the confidence intervals of the average share of additional consumption in 2022 and the imputed values in 2016 overlap for all deciles (Figure 1). This indicates no significant differences by decile between the original 2022 and the imputed average shares. In other words, the method successfully captures the distribution of additional food and non-food consumption in 2022 within the corresponding deciles of the 2016 data. However, the imputed shares generally appear slightly subdued compared to the original 2022 distribution. This trend might imply that the simulated consumption data for 2016 slightly underestimates the increase in consumption, or it might point to actual shifts in consumption patterns between 2016 and 2022.

9
# **1. Introduction**

COVID-19 has worsened gender inequalities in many countries around the world. In particular, the pandemic resulted in more job losses and labor income decreases for women than men (Dang and Nguyen, 2021; Alon _et al.._ , 2022). Yet, the existing literature focuses on richer countries and there are fewer studies for poorer countries, perhaps due to a shortage of nationally representative survey data that span the pandemic in the latter.<sup>1</sup> Furthermore, recent studies offer mixed evidence on the impacts of COVID-19 on gender gaps in employment for poorer countries.

Examining the pandemic effects in India, Deshpande (2022) finds that in August 2020, for women, the likelihood of being employed was 9 percentage points lower than for men, compared to April 2019, conditional on previous employment. However, by December 2020, gender gaps in employment were at the pre-pandemic December 2019 levels. Alfonsi _et al._ (2023) find that lockdowns imposed in Uganda reduced employment by 69% for women and by 45% for men, and the gap persisted 18 months after the onset of the pandemic. On the other hand, Dang, Nguyen, and Carletto (2023) analyze Viet Nam’s Labor Force Surveys spanning 2015 to 2020 and find negative pandemic effects on employment, temporary layoff rates, and employment quality, but this study does not find differential effects for women. Halim _et al._ (2023) even observe that the pandemic has reduced the gender gap in employment and participation for Indonesia.

We offer an early study on the pandemic impacts on the labor market in the Islamic Republic of Iran. The Islamic Republic of Iran offers an interesting, even unique, case study for several reasons. Female labor force participation (LFP) in the Islamic Republic of Iran is among the lowest

> 1 Most existing studies for developing countries rely on high-frequency phone surveys (Egger _et al._ , 2021; Khamis _et al._ ,
_Employment Guarantee Scheme: Evidence from Andhra Pradesh_ . World Bank Policy Research Paper 6543, Washington, DC: World Bank.

- Dercon, S., and L. Christiaensen. 2011. “Consumption risk, technology adoption and poverty traps: Evidence from Ethiopia.” _Journal of Development Economics_ 96:159– 173.

- Desai, P., Sonalde amd Vashishtha, and O. Joshi. 2015. _Mahatma Gandhi National Rural Employment Guarantee Act: A Catalyst for Rural Transformation_ . New Delhi: National Council of Applied Economic Research.

- Duflo, E., M. Kremer, and J. Robinson. 2008. “How high are rates of return to fertilizer? Evidence from field experiments in Kenya.” _The American Economic Review_ 98:482– 488.

- Dutta, P., R. Murgai, M. Ravallion, and D. Van de Walle. 2012. _Does India’s Employment Guarantee Scheme Guarantee Employment?_ . World Bank Policy Research Working Paper 6003, Washington, DC: World Bank.

- Fafchamps, M. 1993. “Sequential labor decisions under uncertainty: An estimable household model of West-African farmers.” _Econometrica_ 61:1173–1197.

- Fetzer, T. 2014. _Can Workfare Programs Moderate Violence? Evidence from India_ . Suntory and Toyota International Centres for Economics and Related Disciplines, LSE, Working Paper 53, London: London School of Economics.

- Foster, A.D., and M.R. Rosenzweig. 2010a. _Is There Surplus Labor in Rural India?_ . Department of Economics Working Paper 85, New Haven, CT: Yale University.

- —. 2010b. “Microeconomics of technology adoption.” _Annual Review of Economics_ 2:395– 424.

- Galab, S., S. Vijay Kumar, P. Prudvikhar Reddy, R. Singh, and U. Vennam. 2011. _The Impact of Growth on Childhood Poverty in Andhra Pradesh: Initial Findings from India_ . Young Lives Round 3 Survey Report: India,
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.4. Performance compared across Central African railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Angola, CFM|580|4,045|30|950|||
|Cameroon, CAMRAIL|603|4,738|26|868|5|2|
|Congo, Dem. Rep. of, CFMK|18|64|10|257|14|4|
|Congo, Dem. Rep. of, SNCC|38|275|4|317|13|3|
|Congo, CFCO|221|3,212|27|300|11|6|
|Gabon, SETRAG|1,778|1,891|39|902|||
|Rail concessions|350|2,945|23|491|5|2|

_Source:_ AICD railways database. _Note:_ Labor productivity = ̳000s traffic units per employee; locomotive productivity = millions of traffic units per locomotive; carriage productivity = ̳000s passenger-km per carriage; wagon productivity = ̳000s net tonne-km per wagon.

# Ports

Central Africa has numerous ports, of which the two most significant ones are Douala and Pointe-Noire. Douala is the most important port from a regional perspective, since it handles transit traffic for the Central African Republic and Chad. Pointe-Noire is one of the best natural deepsea ports in Africa, and once played an important role in the region prior to the civil conflict affecting the Republic of Congo. But since that time, the quality of the country’s road and rail surface transport links has deteriorated markedly, preventing cargo from being channeled into Pointe-Noire. In the
# **4. Data**

The ride-hailing data that we use to implement our event study design comes from _Gojek_ , which, along with _Grab_ , is Indonesia’s biggest ride-hailing company. These two companies each control about half of the ride-hailing market in Indonesia that for the largest part consists of two-wheeled ride-hailing services on motorbike. _Gojek_ is an Indonesian-owned firm that started in 2010 with a fleet of 20 drivers connected to a call-center, but it was only after the launch of its mobile phone application in January 2015 that the company experienced exponential growth.<sup>13</sup> It now has a fleet of over 1 million drivers and, as of November 2019, _Gojek_ had an estimated 29.2 million monthly active users in Indonesia.<sup>14</sup> The ride-hailing data from _Gojek_ has a daily frequency and covers the period from 1 September 2018 to 1 November 2019, which encompasses the staggered opening of the Phase 1 MRT line that took place between 12 March and 13 May 2019. For each day, we have information on the number of, as well as the average distance-traveled on, _Gojek_ trips originating from (pick-ups) and terminating at (drop-offs) both our treatment and (both sets of) control locations as defined using circles of 50 m and 100 m radii.<sup>15</sup> The data further provide a breakdown between rush and non-rush hour trips, where rush hour trips are defined by _Gojek_ as pick-ups and drop-offs that take place between either 5 – 8 am (morning rush hours) or 4 – 8 pm (evening rush hours).<sup>16</sup> In total, our data contains information on more than 17 million ride-hailing journeys between September 1, 2018 and November 1, 2019.

Meanwhile, our data on daily tap-ins and tap-outs into the MRT system at each individual newly opened MRT station comes from <mark>PT Mass Rapid Transit Jakarta. It covers the period from 1 April 2019 onwards
Policy Research Working Paper 8754

# **Abstract**

This paper examines the public sector wage premium using nationally representative household surveys from 91 countries. The public sector generally pays a wage premium compared to all private sector salaried employees, but the size of the premium is sensitive to the choice of the private sector comparator and varies considerably by worker characteristics. For most countries, the average premium disappears when the public sector is compared to only formal sector private employees, especially when controlling for occupation. The public sector wage premium is higher for women and low-skilled workers. In contrast, highskilled public sector employees are most often paid the same as their private sector counterparts or may even pay a penalty for working in the public sector. Consistent with this, the public sector premium is greater for employees with less education, those working in lower paid occupations, and those whose earnings fall in the lower part of the conditional earnings distribution. Across countries, the wage premium is only weakly associated with countries’ level of development. These findings nuance the existing consensus that public sector workers tend to enjoy a significant wage premium over their private sector counterparts, and that this premium is especially large in low-income countries.

This paper is a product of the Governance Global Practice and the Poverty and Equity 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 zhasnain@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, interpretations, and conclusions expressed in this paper are entirely those
9 Months After Prog**|**ram Ended**|||||
|=1 if Conditional Schoolgirl|-0.269|-0.175|-0.127|-0.137|-0.183|
||(0.168)|(0.192)|(0.161)|(0.154)|(0.152)|
|Sample Size|287|287|287|287|287|
|Control Structure:||||||
|Maternal selection controls + propensity weight||X|X|X|X|
|Father selection controls|||X|X|X|
|Cubic in child age in months||||X|X|
|Maternal age inyears,age interactions|||||X|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. The height-for-age z-score is calculated using the 2006 WHO child growth standards. Specification (1) controls for the gender of the child. Specification (2) adds selection weights and controls directly for maternal baseline characteristics ( stratum indicators, household asset index, highest grade attended, and an indicator for never had sex). Specification (3) adds controls for paternal attributes (highest education level, religion, ethnicity, main activity, and likely HIV status). Specification (4) adds a linear, quadratic, and cubic in child age. Specification (5) adds maternal age and maternal age interacted with the other baseline covariates. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
movement for a large number of people between health facility catchment areas at the daily level. I leverage mobile phone metadata for 9.5 million SIM cards in Senegal in 2013 to extract patterns of movement between dierent areas from the approximate locations of 15 billion calls and texts. For each month and health facility area, I measure the number of incoming travelers from other regions weighted by the incidence of malaria in these regions and the length of time spent in the origin and destination to calculate "expected imported malaria cases." I study an area of Senegal close to elimination to focus on reintroduction eects. I use a panel data strategy to estimate the impact of imported incidence on total malaria incidence in this low-malaria setting using a linear dynamic panel-data model and controlling for time xed eects. If infected travelers only lead to a malaria case being detected in the destination rather than the origin of the traveler, but do not generate any externality in the form of additional malaria cases, then a standard model would predict for each expected imported case one more additional case reported in the destination. Instead, I nd that one additional expected imported case of malaria in a low malaria area leads to 1.7 cases of malaria reported, indicating an externality of .7 new cases.

Given that migration has numerous economic and social benets, policymakers face trade-os between economic growth and improving public health in designing policies to reduce travel-linked malaria cases. This paper provides a useful framework for strategic targeting of high-risk populations in low-incidence areas to reduce negative externalities from travel with minimal interference to travel patterns. There are two categories of targeting considered: (1) targeting high-risk travelers entering a low malaria area from a high malaria area and (2) targeting all travelers in only specic areas of low-malaria regions that are likely to receive many high-risk travelers.<sup>5</sup> Within each type, I compare a strategy that incorporates daily information on origins and destinations of travelers from mobile phone data
of workers employed, the labor input is calculated by normalizing the wage bill of each firm by the average wage prevailing in a given 2-digit sector in a given year. Materials are deflated by input-output coefficient weighted sector deflators based on the wholesale price index. Energy inputs are deflated using National Accounts Statistics price indices for “Fuel, Power, Light and Lubricants.” Services inputs are aggregated from detailed data on reported expenses on travel, transport, legal services and accounting, and non-interest banking expenses. These items are deflated using a weighted average of services sector deflators from the national accounts statistics. Given that our interest is in upstream services reform, a proper accounting for services inputs at the firm level is essential to control for changes in the intensity with which firms use

> 15 We are grateful to Carolina Villegas-Sanchez for sharing with us a STATA routine implementing the procedure. 16 The industry groupings are: food and tobacco; textiles; garments and leather goods; wood, paper and printing; petroleum products and chemicals; rubber and plastics; non-metallic minerals, iron and steel; metal products; machinery, office, electrical and communication equipment; lifting, medical and industrial equipment; motor vehicles and other transport equipment.

18
programs increasingly rely on mobile phones and money to distribute benefits (cf. Gentilini et al., 2020), CDR-based targeting methods will likely play a more prominent role in the set of options considered by policy makers and program administrators — particularly in contexts like Afghanistan, where traditional targeting benchmarks are missing or unreliable. In just the past few years, for instance, data from mobile phone operators was used in the design of social assistance programs in Colombia, the Democratic Republic of Congo, Pakistan, and Togo (Gentilini et al., 2020, 2021; Aiken et al., 2021). We conclude by highlighting a few policy considerations important for CDR-based targeting.

**Speed and cost** An advantage of CDR-based targeting is that it can be used in contexts where face-to-face contact is not feasible, dramatically reducing the time required to implement a targeted program. While it typically takes many months (or years) to implement a proxy-means test (PMT), community-based targeting (CBT), or consumption-based targeting, a CDR-based model can be trained in just a few weeks (see Appendix C). Likewise, the marginal costs per household screened are substantially lower with CDR-based targeting than with CBT, PMT, or consumption-based targeting. For instance, Table S9 uses cost estimates obtained from the literature (and detailed in Table S10) to estimate targeting costs for the TUP program.<sup>12</sup> Whereas the marginal costs of screening an individual with a CBT or PMT are estimated at $2.20 and $4.00, respectively, the marginal cost of screening with CDR is negligible (see Appendix C).<sup>13</sup> For the entire TUP program, which screened around 125,721 households in six provinces, CBT and PMT would add an additional estimated $276,586 and $502,884, respectively, corresponding to 2.18% and 3.97% of the total program budget.

**Data access and privacy*
the extent of these various health hazards. Jabbara and Mallick (1994) summarized evidence from blood tests that shows that as many of one third of cotton workers in Pakistan have been exposed to dangerous levels of pesticides. Other studies have noted unacceptable levels of pesticide residues on at least 20 percent of samples of vegetables in urban markets.

The exclusive reliance on pesticides to reduce pest losses is unsustainable as pest populations change and some pests develop resistance to commonly used pesticides. Resistance to pesticides has caused the cotton crisis of the past two years, in which losses from curl-leaf virus have sharply reduced the cotton harvest and even threatened supplies to the domestic textile industry. Shortrun success in increasing cotton production over the past decade has come at the price of long-run sustainability.

Integrated pest management (1PM) is widely advocated as a means of reducing pesticide use and developing more sustainable production systems through the employment of a range of practices to combat pest populations. Despite the magnitude of the environmental and health problems associated with high levels of pesticide use on some crops, Pakistan has been a latecomer to the practice of IPM, and has been reactive rather than proactive. IPM is a knowledge-intensive practice that requires good extension service and is facilitated by literate farmers. Both are deficient in Pakistan and unless major emphasis is placed on promoting IPM it will be decades before pesticide use is reduced significantly.

17
|5.0|6.0|6.4<br>|343|404|1.02|
|Rest of South Asia|6.7|4.8|5.4<br>|398|527|1.78|
|Rest of Europe and Central Asia|7.3|5.7|6.1<br>|227|247|0.51|
|Middle East and North Africa|5.2|3.7|4.1<br>|360|469|1.66|
|Rest Sub-Saharan Africa|6.2|5.7|6.0<br>|690|942|1.97|

/a/ Growth growth /b/ EFTA - European Free Trade Area Source: World Bank‟s Development Data Group The third set of assumptions under the BaU scenario is on the population in each of the countries and regions in the model also shown in Table 2. Growth rates of population were based on the population growth projection of the United Nations. One can observe that developed countries have generally declining population growth rates.

The fourth set of assumptions under the BaU scenario is that existing biofuel policies (e.g. already implemented mandates, subsidies and import duties) will continue throughout the study horizon (2010-2020). Biofuel penetration in the BAU scenario, which is defined as the 14