rafmacalaba/data-use-mentions-extended huggingface dataset:
R-squared 0.312 0.311 0.281 0.379 0.318 0.289 Notes: The above table presents regression estimates of equation 4. We include state level fixed effects in the regressions to account for time invariant state characteristics. We estimate the regression using data from 1999–2000 onward for the sample of the large Indian states. All specifications are estimated with heteroskedasticity robust standard errors.

# **3.3 Policy response to the GFC and macroeconomic stability**

The impact of the GFC on different countries and the pace of recovery depended both on the preconditions, such as the pace of GDP growth, and credit and investment growth in the years prior to the crisis, and on the policy response to the crisis. The initial impact of the crisis is considered relatively mute on India (see Acharya 2012), largely due to a prompt and rather large policy response to the crisis, including monetary policy easing, a large fiscal stimulus, and regulatory forbearance on banks (or what some have referred to as the “evergreening of loans”).<sup>29</sup> Mohan and Kapur (2015) and Mundle et. al. (2011) have persuasively argued that in the run-up to the 2009 general election, the fiscal stimulus in fact started prior to the GFC. The excessively stimulative policy response and the subsequent macroeconomic management of the economy however worsened macroeconomic stability and possibly prolonged the slowdown. The slow and delayed recognition and resolution of stressed bank assets subsequently added to the issues with impaired balance sheets.

A fallout of these policies was that some of the macroeconomic indicators reached crisis proportions by 2013, as the general government deficit touched nearly 10 percent of GDP; inflation reached double-digit levels; the current account deficit increased to 5 percent of GDP; and the quality of public expenditure possibly worsened due to a decline in the share of capital expenditure. Hence, it is unsurprising that as the market sentiment turned against emerging markets in summer 2013, during the “tapering
on real-time population surveys document strong increases in employment and declines in unemployment during May and most of June. However, the current figures are still well below their pre-COVID levels (Bick and Blanding, 2020).

These findings are confirmed by studies focused on small businesses. Kurman et al. (2020) find that until mid-April employment in small businesses in the services sector dropped by 60%, equivalent to the loss of 18.2 million jobs. However, from mid-April to June more than half of the closed businesses reopened, resulting in 9.1 million additional jobs, mainly taken by previously furloughed workers. Bartik et. al (2020) find similar patterns and point to significant heterogeneity across sectors, with retail, arts and entertainment, personal services, food services, and hospitality reporting the largest declines. Using CPS data, Fairlie (2020) confirms that economic activities by small businesses significantly declined in April and only partially recovered in May.

Overall, the research so far seems to unanimously show that the workers hit the hardest by the crisis are women, young, low-educated (Béland et al., 2020a; Bick and Blanding, 2020; Cho and Winters, 2020; Cowan, 2020; Montenovo et al., 2020) or with an ethnic minority or migration background (Béland et al., 2020a; Borjas and Cassidy, 2020; Cho and Winters, 2020; Cowan, 2020; Fairlie et al., 2020; Montenovo et al., 2020). Nevertheless, some evidence suggests that during the initial stages of the crisis men might have been disproportionately affected (Béland et al., 2020a) and that some older workers might have chosen to go on early retirement (Coibion et al., 2020; Cowan, 2020).

These studies also explore whether social distancing measures have disproportionally impacted specific categories of workers. Findings from this research show that jobs that cannot be performed from home are at higher risk, while, jobs in workplaces classified as essential face lower risks (Béland
Policy Research Working Paper 10226

# **Abstract**

This paper studies cartels in public infrastructure procurement and analyzes the conditions under which they succeed in generating rents. It first conceptualizes the interplay of the central actors of a procurement project, notably the contractor, the procurement agency, as well as the supervision and design consultants. By focusing on consultants, the framework includes important yet understudied actors in cartels that design tenders, evaluate bids, and supervise the implementation of projects. The paper then explores an original data set of infrastructure procurement contracts in Lebanon and analyzes the conditions under which powerful political elites can broker deals to overprice and/or over-spend contracts. To examine how cartels operate, the analysis identifies the political connections of contractors and consultants and classifies them according to their “quality” in terms of access to institutional functions of the implementing agency. The paper argues that design consultants serve as the lynchpin of the cartel by reducing transaction costs for searching, bargaining, and enforcing of corrupt deals.

This paper is a product of the Fragility, Conflict and Violence Global Theme. 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/prwp. The authors may be contacted at mmahmalat@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 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
Table 1. Nominal and real (food) hourly wages for several occupations Annual after-tax wage divided by the number of effective annual hours of work, March 2009

||Building l|aborer 1/|Skilled indust|rial worker 2/|Engin|eer 3/|
|---|---|---|---|---|---|---|
||Nominal<br>after tax<br>wage (in $)|Real food<br>wage|Nominal<br>after tax<br>wage (in $)|Real food<br>wage|Nominal<br>after tax<br>wage (in $)|Real food<br>wage|
|New York|16.6|16.6|29.0|29.0|26.5|26.5|
|London|9.7|15.4|19.0|30.4|22.1|35.2|
|Beijing|0.8|1.3|2.3|3.8|5.8|9.5|
|Delhi|0.5|1.7|2.1|6.9|2.9|9.1|
|Nairobi|0.6|1.5|2.0|4.7|4.0|9.2|
|Rich vs.<br>poor<br>(unweighted<br>ratio) 4/|20.4|10.9|11.0|5.8|5.8|3.3|

Note: Food prices are estimated from a basket of 39 food products with weights reflecting West European consumption patterns. New York food prices are set equal to 1. Real food wage (in New York food prices) is estimated by dividing the nominal after-tax dollar wage by the food
# **Global Technology for Local Monitoring of Air Pollution in Dhaka**

Susmita Dasgupta* Lead Environmental Economist, World Bank

# M. Khaliquzzaman Consultant, World Bank

David Wheeler Consultant, World Bank _Keywords:_ Air pollution; Fine particulates, Nitrogen dioxide; Satellite imagery; Traffic congestion; Google Traffic; Bangladesh _JEL Classification:_ Q53 Authors’ names in alphabetical order.

Acknowledgement: The authors are thankful to the Department of Environment, Government of Bangladesh for data on air quality from CAMS-3 at Darussalam.

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.
## —Insert Figure 1b—

Although debt relief has brought the debt of HIPCs to sustainable levels and allowed these countries to gain access to external financing, the evidence suggests that such countries remain somehow credit constrained (for instance vis-à-vis other IDA countries that, despite their higher debt ratios, could build a track record of creditworthiness). Furthermore, the new borrowing from private creditors to HIPC has come at high interest rates. The default risk premium that postMDRI countries have to pay on new debt can be measured by the spreads between the yields on the dollar bonds issued by Ghana and Senegal and the yield on US Treasuries of the same maturity. Figure 2a shows that the yield spread on the Ghana bond has remained above 4% while the spread on the Senegal bond has been even higher at 6.5%.

## —Insert Figure 2a—

Further evidence of the relative high interest rates at which post-MDRIs borrow comes from the GDF database. Figure 2b shows the simple mean for post-MDRI countries of the average interest rates that they pay on new public and publicly guaranteed debt from private creditors and from offi cial creditors.<sup>6</sup> While interest rates on concessional offi cial lending has remained below 2% over the whole period, interest rates on private lending has fluctuated around 4% reaching 6% in 2007 when new borrowing became available after the implementation of MDRI relief.<sup>7</sup> More importantly, there is no evidence that debt relief initiatives have brought about a reduction in the interest rate on new private debt despite the fall in debt levels.

## —Insert Figure 2b—

This brings us to the critical question of our paper: Would it have been possible to design the debt relief initiative so that markets could distinguish between good borrowers and bad ones and doing so would facilitate capital market access to the former (but not to the latter)? In order to shed some light on such an important question, and provide some thoughts on how to
|_Formal_|_Formal_|There are no pre-|
|---|---|---|
|•Number of laws/acts supporting<br>community level organization and<br>association<br>•Number of decision-making processes<br>decentralized to local authority<br>control|•Number of public meetings at which the implications of rules<br>are discussed per year<br>•Percentage of cases in which rules of community membership<br>groups reflect normative formal rules<br>_Informal (cultural)_|existing data sources<br>for data in the<br>community sub-<br>domain. Data must<br>be collected using the<br>national survey or in-|
|•Percentage of budget allocation<br>decentralized to local authority<br>control|•Percentage of labor force employed outside any traditionally<br>expected roles based on social identity<br>•Variance between membership diversity|depth research<br>techniques.|
|•Institutional framework exists for<br>local government – civil society –<br>private sector partnerships<br>•Number of laws/acts addressing<br>social, ethnic and religious<br>discrimination|(gender/social/ethnic/religious) of community associations and<br>diversity of local community<br>•Number of reported cases of community association<br>membership restrictions based on<br>gender/social/ethnic/religious identity per year<br>•Percentage of decision-making positions with occupied by<br>people from lower castes or classes||

73
here, relative to quantitative research based on household surveys, are the relatively faster speed and more nuanced and more contextual responses. The disadvantages include the lack of national representativeness, the smaller sample sizes, and the resulting inability to quantify (at national levels) the frequencies of the observed impacts and behaviors. Therefore, our findings should be regarded as illustrative and indicative, not representative.

The paper is structured as follows. The next section presents the conceptual framework and summarizes the major coping responses. Section 2 discusses the hardships faced by people during the crises. Section 3 surveys the sources of support people could rely upon. Section 4 offers concluding reflections.

# **1. MAJOR COPING RESPONSES**

## **CONCEPTUAL FRAMEWORK**

The analysis of coping did not adhere to any single academic concept but was guided by a broad-based framework of coping mechanisms (see Figure 2). The analytical framework is based on observations in the field and inspired by a range of studies on poverty and vulnerability, both quantitative and qualitative, including _Moving out of Poverty_ (Narayan, Pritchett, and Kapoor, 2009) and the work by Anirudh Krishna (Krishna, 2004 and 2007; Krishna and Shariff, 2011), Robert Chambers, and Stefan Dercon (e.g., Dercon, 2004). By coping responses (or mechanisms), we refer to actions that households take to deal with shocks and avoid the worst impacts: eat cheaper foods and fewer meals, work harder, seek out new income opportunities, seek support from relatives and community support networks, etc. We distinguish between behavior-based responses related to consumption and labor supply; assetbased responses that use savings, assets, and common property resources for coping; and assistancebased responses involving support from various sources (Heltberg and Lund, 2009).

People are exposed to significant levels of uninsured risk from a range of covariate and idiosyncratic shocks. In the study sites, the most common covariate shocks included the sharp economic contraction starting in 2008; food, fuel
- _“It is a feeling - I could even say: of guilt. Because they tell you that it's not okay to leave him [in a center], but they also criticize you because you don't contribute financially to your home. It's a_

- _conflict.”_<sup>_4,5_</sup>

# 1. Introduction

There has been a significant rise in female labor force participation in the Latin America region over the past decades. Women’s participation in the region’s labor force rose by 25 percent between 1990 and 2018, in contrast with the global trend of a slight decline (Muller and Casabonne 2020). Access to education for women, the decline of fertility, and later marriage are drivers behind this increase in participation rates (Chioda 2016). Female labor force participation has had significant effects on poverty and inequality reduction in the Latin America region: The growth in female labor income in the region between 2000 and 2010 accounted for 28 percent of the reduction in inequality and 30 percent of the reduction in extreme poverty (World Bank 2012). Despite those gains, some countries in the region are still lagging behind in female labor force participation – Mexico is one of them. At 44.2 percent (women ages 15 and older, World Development Indicators, modeled ILO estimate, 2019) female labor force participation is particularly low in Mexico, among the lowest in the Latin America region and in the OECD.

Motherhood has an important impact on women's employment decisions. The disproportionate demand on women’s time associated with care is one of the key factors behind the significant gap in participation rates in the Latin America region (Mateo Díaz and Rodríguez-Chamussy 2013). That seems to be the case for Mexico as well: The likelihood of being employed declines among mothers with young children, increases when they enter pre-school, and then, decreases again as children start primary school (Inchauste et al. 2019). Among working age
rate in the labor market is likely the result of women having to stay at home to take care of their young children. Indeed, although Vietnam has accomplished almost universal primary school enrollment (Dang and Glewwe, 2018), more than half (53%) of children age 1-5 do not attend childcare. Our findings for the strong effects of childcare on women’s labor market outcomes suggest that improving access to childcare, especially for small children, can lead to more effective labor policies and also to women’s empowerment.

This paper consists of six sections. We describe the data sets in the next section, before presenting in Section 3 descriptive analysis of childcare and maternal employment in

> 3 Unless otherwise noted, our estimates are based on the Vietnam Household Living Standards Survey (VHLSS) in 2016.

4
the ability of assistance programs to buffer workers’ crisisinduced earnings losses. Section VII examines responses in a different labor market, using data from Ecuador. Section VIII concludes.

# **II. Data and Summary Statistics**

The analysis in this paper draws on rich administrative longitudinal worker-firm data for Brazil over the 2004-2017 period from several main sources. The first source is a longitudinal linked employer-employee database covering the universe of formal workers and firms. The second is a longitudinal manufacturing firm census. The third is firm customs data on exports and imports by firm-partner country. Importantly, we are able to link the employer-employee database with the worker registry of unemployment insurance, the national registry of lowincome households providing data on payments from the cash transfer program Bolsa Familia, and the beneficiary records of training activity from Brazil’s main training provider. Overall, our data cover the universe of formal workers in Brazil along with their firms’ exposure to the GFC through their export market participation and also all the benefits disbursed to mitigate the worker-level impacts, including support received from income assistance and job training. We supplement these data with information from the Brazilian census. Next, we 6
additional data cleaning was necessary because the Facebook Messenger chatbot ensures that only one survey is recorded per Facebook account and discards responses that deviate from the questions asked.

# **2.3. Study Procedures**

Participants who clicked on the study advertisement were redirected to Facebook Messenger, where the survey was conducted via chatbot. Participants selected their preferred language and gave informed consent at the beginning of the survey before proceeding to the survey modules.

First, we asked participants’ COVID-19 vaccination status. Those who were unvaccinated were included in the survey experiment that randomized participants to different messaging arms and asked for their vaccination intentions, our main outcome. We then asked participants about their perceptions of the main benefits of or concerns about COVID-19 vaccination, additional questions about attitudes and beliefs related to COVID-19, use of different information channels (e.g., social media, radio, etc.), and trusted information sources. Lastly, we asked participants to provide information about their demographics. The study took approximately 10 minutes, on average. See Figure 1 for a summary of participant inclusion and exclusion at each step of the studies.

**_Randomization._** After answering a question about their COVID-19 vaccination status, unvaccinated participants were randomized to study arms with different behaviorally-informed message framings about the COVID-19 vaccine. Randomization was stratified such that participants had a greater probability of being allocated to the control message compared to other arms, and all treatment message arms had an equal probability of assignment. See Appendix B for more detail on the proportion of samples randomized to each arm.<sup>5</sup>

> 5 More participants were assigned to the control condition to provide a subsample for diagnostics to understand attitudes and beliefs about a country’s social media population with potential impacts of framing. These diagnostics were used to inform the government’s COVID-19 strategy along with results from the survey experiments.

7
Friedemann-Sánchez and Lovatón (2012) likewise find similar results using the Colombia DHS, as do Svec and Andic (2018) using the Peru DHS. Using the Ethiopia DHS, Ebrahim and Atteraya (2019) find that no decision making by the woman and sole decision making by the woman are similarly linked to IPV, but that women who made decisions jointly with their spouses had lower risk of domestic violence. Finally, Zegenhagen et al. (2019) find that women's reporting of decision making did not predict their experience of IPV, whereas men's reporting on decision making over major household purchases and expenditure of husband's earnings predicted the likelihood of women experiencing IPV. Using husbands’ reports, joint decision making and women's decisions alone in both of these domains were associated with a lower probability of IPV compared to husband's making the decisions alone.

However, none of these studies has examined couples’ agreement (or disagreement) over who makes the decision, whether sole or joint, and its relationship to violence. Moreover, the theoretical mechanisms underlying the relationship between joint decision making and lower IPV are not systematically explored in this literature—though Svec and Andic (2018) posit that joint decision making may be capturing more equal gender beliefs, while Zegenhagen et al. (2019) discuss how violence may be used when husbands perceive that their status within the household contradicts social norms. We suggest that joint decision making allows spouses to share responsibility and mitigate conflict if the decision is later regretted. Though not explored quantitatively in the context of couples, this link between joint decision making and reduced conflict has been discussed extensively in the management, political science and conflict resolution literature.

Political science has conceptualized joint decision making as a way for politicians to deflect blame and defuse conflict if a chosen strategy is later regretted (Thompson 1980, McGraw 1991). Collective decision making allows these officials to argue that the decision was the joint product of a group of individuals and 8
# **III.4. Efficiency Change Over Time**

To examine the evolution of input and output efficiency over time, we computed the efficiency scores in two different time periods: 1975-1980 and 1996-2002 for education study, and 1997-99 and 2000-02 for health study, the construction of which is driven by data availability. Appendix D reports the results on a regional basis<sup>14</sup> .

Comparison of different input-output bundles in different time periods has to be done carefully because the frontier can be shifting outward through time. In some cases the frontier displacement can be parallel (such as in the life expectancy case of Figure 9). In others, the frontier displacement can be very uneven (biased frontier shift in Figure 9) reflecting biased technological change (see Appendix D for detailed discussion).

<!-- Start of picture text --> Frontier Shift: Life Expectancy Frontier Shift: Gross Secondary Enrollment<br>LVA KOR BRB<br>CRI URY<br>KWT<br>CRI KWT<br>KWT<br>ARGIDN<br>GTM<br>KOR<br>OMN<br>KOR<br>600 800 1000 1200<br>350 400 450 500 550 600<br>Orthogonalized Public Expenditure on Education<br>Orthogonalized Public Expenditure on Education<br>Eff. Frontier 1975-80 Eff. Frontier 1996-02<br>Eff. Frontier 1997-99 Eff. Frontier 2000-02<br>Data Source: World Bank WDI<br>Data Source: World Bank WDI<br>Parallel Shift Biased Shift<br>78 100<br>80<br>76<br>60<br>40<br>Life Expectancy 74<br>20<br>Gross Secondary Enrollment<br>72 0<br><!-- End of picture text -->

**Figure 9. Efficiency Frontier Shift Over Time**

The detailed comparison between observed input-output combinations in different time periods distinguishes whether variations in