rafmacalaba/data-use-mentions-extended huggingface dataset:
final imputation for a price element _x_ ˆ _t ∈_ ( _A, B, C_ ) is generated by calculating the ensemble average Since there are stochastic elements to the iterative algorithm, each prediction at iteration _I_ will be generated from a different prediction model. Increasing _M_ improves both the stability of the stochastic result as well as the accuracy of the ensemble prediction. This is similar to bootstrap aggregating, a prediction improvement technique central to the Random Forest algorithm of Breiman (2001), in which multiple random simple base learners are combined to improve stability and accuracy by canceling out random prediction errors. The key difference is that bootstrap aggregation produces multiple learners by taking random draws of the training data, while the randomization in learners at iteration _I_ result from different stochastic simulations of chained model predictions, see section B.B2 for more details on the derivation of the stochastic ensemble predictor.

# _C. The regression specification_

The methods are applied to each country individually, so the desired flexibility of the prediction models varies. Moreover, the properties of the data change throughout the simulation, thus the desired flexibility of the model should change
outcomes.

## **2.2 Main Data Source: RAIS**

Our analysis uses the _Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais_ (RAIS) that provides matched employer-employee data on the near universe of formal jobs in Brazil between 2006 and 2021. Employers must report annual information on all formal jobs in the last year to the Brazilian Labor Ministry ( _Ministerio do Trabalho_ , MTE) which uses the data for administering various social security programs. Firms’ compliance with reporting requirements is high as they have to fear large penalties when failing to submit complete records. Each record in the data entails information on a contract between an establishment and a worker in a given year, including average monthly earnings, contracted hours, type of contract, occupation, the establishment’s location and industry, and hiring and separation dates. We also observe the type of separation: layoff, quit, termination of temporary contract, death, retirement, or transfer. Individual and establishment identifiers allow us to track separations of workers from an establishment.<sup>7</sup> Moreover, the data includes worker characteristics, i.e. their age, gender, education, and race, as reported by the employer. Cornwell _et al._ (2017) document that a substantial share of workers have different races reported by different employers and that changes in race are not entirely random as they are correlated with wage changes upon job transition. To address these inconsistencies, we follow Gerard _et al._ (2021) and assign each worker the modal race as reported in all their contracts within the RAIS across all years.

# **3 Empirical Strategy**

The racial composition of the workforce is likely influenced by a variety of firm and job characteristics, such as firms’ management practices, hiring policies, training and promotion opportunities, and local labor market conditions, which could also affect worker retention. Omitting such unobservable characteristics would thus bias the retention effects of coworker similarity. Focusing on
in an occupation and the share of female employees in that occupation.<sup>11</sup> Last but not least, the SI data contain information on a rich number of firm characteristics such as their sector, material input usage, output, capital stock,<sup>12</sup> ownership structure and whether or not they export (see appendix A for more details).

> 10 The census treats establishments (plants) rather than firms as their units of interest.  Less than 5% of establishments in the census are owned by multi-plant firms (see Blalock and Gertler 2004 for a thorough discussion).

> 11 Ideally one would control for individual characteristics in testing for differential treatment effects. This information is not available, but we do include firm-level proxies for productivity and wages.

> 12 Information on the capital stock was not recorded in 1996 and missing for some firms in other years. Concerns about possible selection biases have led us not to control for capital per worker in our regressions. The qualitative pattern of

8
peer countries in terms of the distributional impact of its fiscal policy? and (d) how can the analysis be used to inform policy reform?

The approach of the paper is largely based on the Commitment to Equity (CEQ) methodology (Lustig 2018). As such, the paper builds a comprehensive incidence analysis by sequentially quantifying the poverty and inequality impact of direct and indirect taxes, noncontributory direct transfers, contributory pensions, and social spending on health and education.<sup>3</sup> To do so, we use the 2016 Household Budget Survey (HBS), and the 2016 Survey of Income and Living Conditions (SILC) collected by Turkey’s Institute of Statistics (TUIK), together with administrative information on transfers and taxes from Turkey’s Ministry of Treasury and Finance (MoTF). The data allow for relatively good coverage of fiscal interventions for this class of studies. The analysis covers about 46 percent of total revenues—including 74 percent of social contributions and 45 percent of tax revenue—and about 50 percent of government spending— including 87 percent of social spending.

The main findings of the paper are that: (a) fiscal policy significantly decreases inequality in Turkey, and (b) among low-income households the magnitude of targeted transfers does not fully mitigate the effect of taxes, resulting in net poverty increases. In driving these changes, social spending on education and health have the largest impact on inequality, complemented by direct taxes and direct transfers that counter the inequality-increasing impact of indirect taxes. Turkey’s relative performance, however, is below median compared to peer countries. In particular, Turkey’s indirect taxation is comparatively more inequality-and-poverty-increasing, while direct transfers and taxes are less inequality-and-poverty-decreasing than in the median upper-middleincome economy. From a forward-looking stand, the diagnostic identifies some potential entry points for policy change. The large-scale Minimum Subsistence Allowance program ( _AGI_ in Turkish) presents an opportunity if its design were more targeted to the poor and vulnerable, while keeping a neutral fiscal position
import protection resulting from the 2008‐2009 crisis beyond that predicted from pre‐crisis data raises important questions for research. If the world trading system does ultimately escape the 2008‐2009 crisis relatively unscathed with respect to new and extraordinary protectionist initiatives, an open and fundamentally important question is, why?

The facts that I have generated from the use of TTBs during the 2008‐2009 crisis suggest many potential contributing causes that should form the basis for future research. One possibility is that the WTO system’s architecture was well constructed to handle the crisis. I.e., perhaps by having a system in place which allowed for TTB use, domestic political pressure for new trade barriers was allowed to escape via a “safety valve.” These nontrivial, though relatively small (in product coverage terms) increases in import protection could have prevented greater market‐closing forces from emerging and the overall multilateral system from falling apart. On the other hand, the lack of a major protectionist response may be unrelated to WTO rules. It could be the result of the nature of the political economy of trade policy changing in a way which actually makes the WTO redundant (Blanchard, forthcoming).

32
2016 are over-represented in the top quintiles of the income distribution in their countries of origin (World Bank Group, 2018). As for voluntary migrants, the costs of the journey, admission criteria, and networks at the destination are factors shaping these selection effects, in addition to opportunities to earn income at the destination versus the origin.

# **3.2 Shocks to physical and human capital and mental health**

Refugees or IDPs migrating to seek protection most likely differ from other migrant populations because their decision to migrate is also associated with shocks to their physical and human capital and/or mental health. Such shocks either triggered their moves in the first place or affected them during transit.

**Loss of physical capital** Those forcibly displaced tend to have lower levels of assets compared to hosts and compared to their situation before displacement, cross-sectional data for IDPs and refugees from different low and middle-income countries show (Fiala, 2009; Krishnan et al., 2020; Ib ́a ̃nez and Moya, 2010; Mejia-Mantilla et al., 2019; Pape et al., 2018; World Bank, 2019b). They usually cannot take their assets and seldom have enough time to sell them before their move; their assets are seized or they are not able to access assets left behind anymore.

**Loss of human capital and skills** Education and skills acquisition are likely to be disrupted through conflict and forced displacement. Children in Colombia recently internally displaced, for example, have significant gaps in educational accumulation and enrollment compared to other migrants and non-migrants. These gaps are larger than those of children who live in Colombian municipalities with high conflict (Wharton and Oyelere, 2011). Disruptions of work experience and longer periods of forced inactivity further deteriorate human capital. Refugees often face legal constraints to access the labor market (see later discussion). They also face more difficulties to access the labor market because they do not choose their destination primarily based on labor demand, usually have little time to
5.2|6.2|
|**Total factor employment (index)**|100.0|5.9|3.2|3.9|
|**Total factorproductivity (index)**|100.0|2.9|2.0|2.2|
|**GNI**|1,686.0|8.4|5.0|5.9|
|**GNDI**|2,807.3|3.1|4.7|4.2|
|**GNI per capita (2009 US$)**|423.4|5.8|2.7|3.6|
|**GNDI per capita (2009 US$)**|704.9|0.7|2.4|1.9|
|**Real exchange rate(index)**|100.0|-0.7|0.8|0.4|
|**Unemployment rate (%)**|12.8|10.9||8.0|
|**MDG 1 -- poverty rate (%)**|60.2|54.2||38.6|
|**MDG 4 -- U5MR (per 1000)**|90.2|75.3||65.5|
|**MDG 7 -- Water access (%)**|70.8|75.7||77.6|
|**MDG 7 -- Sanitation access(%)**|19.6|23.7||25.4|

**Note:**

**Unless otherwise noted, the unit for the 2012 column is millions of US$ (at 2009 prices). Rows for unemployment and MDG indicators show levels in 2012 and at end of each period.
# **1 Introduction**

Domestic revenues in several African countries are scant and below the minimum level of 20 percent allowing them to achieve development goals (UNDP, 2010). In those countries, low domestic revenue limits the ability of governments to invest in development programs, improve economic efficiency, and reduce poverty (Baunsgaard and Keen, 2010; Agénor et al., 2008). Although developing countries are the hardest hit by taxpayer non-compliance (Fuest and Riedel, 2009), little is known about the determinants of tax compliance and tax morale in those countries, especially in Africa (D’Arcy, 2011; Fjeldstad and Semboja, 2001).

Tax morale is strongly associated with tax compliance, as highlighted by Luttmer and Singhal (2014). The authors define tax morale as "... an umbrella term capturing nonpecuniary motivations for tax compliance as well as factors that fall outside the standard expected utility framework". In addition to capturing citizens’ intrinsic motivation to pay their taxes, tax morale also reflects, the feeling of being guilty or shame for failure to comply with tax obligations. This paper examines the role of trust in public institutions and the neighborhood on tax morale in four African countries: Algeria, Ghana, Morocco, and Nigeria. First, I discuss the theoretical foundations of the relationship between both dimensions of trust and tax morale based on the standard model of tax evasion from Allingham and Sandmo (1972). Further, to isolate the effects of trust in public institutions and the neighborhood on tax morale, I use individual-level data from the sixth wave of the World Value Survey (WVS) covering the 2010-14 period. Lastly, I employ an empirical methodology that accounts for potential endogeneity. The identification strategy relies on the instrumental variables (IV) method and takes advantage of the historical data on slavery at the tribe level and the literature on the cultural heritage of trust.

The slavery data are from Nunn (2008a) and Nunn and Wantchecon (2011) who document the number of slaves
0.575***|
||(0.118)|(0.105)|||||||(0.090)|(0.069)|
|Obs|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|20,886|

> 24Egger et al. (2020) argue for the use of annual data for the estimation of the dynamic trade effects of TAs. Their results nonetheless confirm that using annual or time-interval data does not affect substantially the contemporaneous effects, which are the focus of our paper. Future work might investigate the anticipated and lagged effects of trade agreements on cross-border government procurement.

23
While the ideal research design would incorporate all European Union (EU) member states; in this research, we limit the analysis to Bulgaria where we have a comprehensive and good quality dataset on public tenders as well as participating companies. Bulgaria can be considered as the least likely case for championing GPP, as the country is particularly challenged in achieving traditional procurement objectives such as competition, integrity, and efficiency (Fazekas et al., 2022). It is one of the poorest and highest corruption risk countries in the EU and is converging to the EU average only very slowly (World Bank, 2022).

The results show that GPP amounted to about 10%-20% of total public procurement spending in Bulgaria in 2011-2019. Most GPP descriptors and requirements are found in titles, technical requirements and product descriptions. Green criteria in award criteria texts, which are mainly used for flagging GPP in the literature, are marginal in comparison. This means that GPP is about what is bought rather than how it is bought; for example, buying electric cars, rather than buying cars by assigning extra scores for lower emissions. Overall, we find that GPP has a beneficial impact on key procurement objectives and the allocative efficiency of resources in the economy. GPP improves competition through encouraging new firm entry and hence lowering the market share of winning suppliers by 3-7 percentage points. Moreover, it also lowered the share of local winners by about 2 percentage points, pointing at a stronger nationwide marketplace. GPP contracts are associated with lower corruption risks, such as lower single bidding probability of 0.6-1.5 percentage points. Finally, GPP contracts are awarded to more productive firms. Among all firms obtaining public procurement contracts, GPP contracts are awarded to firms with 14% higher labor productivity. The productivity effect is even stronger among GPP contracts that have a low risk of corruption: 19% higher labor productivity and 6% higher total factor productivity. The results can either be driven by the more competitive procedures applied to green contracts or
poor are likely to suffer disproportionately, poverty rates can be expected to increase as a result (Hallegatte et al., 2020). This is particularly important to consider in the context of the current global pandemic which is affecting urban populations disproportionately. These are important issues which need to be addressed in combination.

# **3 Data and methodology**

## **3.1 The surveys**

The analysis relies on integrated and harmonized data from four surveys, covering the four cities of Accra, Antananarivo, Dar Es Salaam, and Addis Ababa. For Accra, Dar Es Salaam and Addis Ababa the Disaster-poverty survey data is used, which contain information on how households experience floods, their perceived risk of future floods, and characteristics of household members, housing conditions and public services, and proxy for household expenditure, rent and housing costs. For Antananarivo, a Living Standards Measurement Survey (LSMS) is used, which covers similar variables and was therefore added to the study. The surveys were conducted in 2016 (Antananarivo), 2017 (Accra, Dar es Salaam, Addis Ababa), and 2018 (follow-up in Dar es Salaam).

To define flood exposure, self-reported information from the surveys is used. The Accra and Antananarivo surveys capture information on specific disasters (severe flooding in 2015 in Accra, and Cyclone Chedza in 2015 in Antananarivo). In the other two cities, the surveys focused on frequent flood exposure rather than a specific disaster.<sup>2</sup> Households were asked also about their perceived risk of future flood 2However, in Dar es Salaam, a follow up phone survey was conducted in 2018 to capture information on the flood experienced in that year.

6
_Robustness._ We conduct three robustness tests of the investment multiplier results: (i) using raw (rather than modeled) payments, (ii) splitting total investment into public investment and private investment portions (which is only available at the country level) and (iii) using winsorized investment data (our default specification is non-winsorized).

First, if we used raw EU payments on the right-hand side (instead of using modeled EU payments), the estimated investment multipliers are substantially smaller in all cases (see Online Appendix Table A3.2). As measurement errors often lead to attenuation bias, this suggests that modeled payments may be more 16
13 25 These estimates are larger than earlier ones. If estimated vehicle ownership saturation levels change over time, they are of little use for forecasting. In fact, that seems to be the case as there is little direct evidence that saturation levels are stationary or that they have a straight forward behavioral interpretation. The income elasticity of motor vehicle ownership may decline as incomes rise. Estimates allowing income elasticities to vary with country income produce declining income elasticities but do not have greater explanatory power than constant elasticity specifications.26

# **5. Road Provision**

What are the determinants of road provision, or the increase in road networks, at the national and city level?<sup>2 7</sup> Unlike motor vehicles, roads are not usually privately provided, and road provision may not be strongly conditioned by economic considerations. Nonetheless, knowledge about the recent trends in road provision and its relation to income, population, and settlement patterns is a useful guide to future road provision and an important input to transport policy making. Although the impact of economic development on roadway networks has long been a subject of descriptive studies for several countries, cross-country empirical studies are recent.<sup>2 8</sup>

# _5.1 National road networks_

The few available studies of road provision across countries are based on data that provide information on road length but not on road width or numbers of lanes. Data available for paved roads and total roads are reported by the countries and generally include urban roads, but country-level definitions sometimes vary with respect to coverage and technical classification. Data on paved roads are more comparable across countries 25 Two of the previous estimates (or assumptions) were provided by Tanner, "Forecasts of Future Number of Vehicles;" and Mogridge, "The Prediction of Car Ownership." Tanner predicted a saturation level of 450 cars, and Mogridge, 660, per thousand persons for the UK.

26 Ingram and Liu, "Vehicles, Roads, and Road Use." 27 This
||||(tea cultivators were<br>typically female).||
|---|---|---|---|---|
|DasGupta<br>(1987)|What factors are<br>associated with<br>female child<br>mortality in<br>Ludhiana district,<br>Punjab?|1984 baseline<br>census of villages<br>originally<br>surveyed for the<br>1950s Khanna<br>study. Census was<br>supplemented<br>with complete<br>maternity histories<br>for ever-married<br>women ages 15-<br>49|Descriptive<br>statistics to illustrate<br>associations<br>between covariates<br>and sex differentials<br>in child mortality<br>and health<br>(associative study)|Women’s education<br>was associated with<br>higher mortality for<br>daughters of higher<br>birth order|
|Amin (1990)|How is mother’s<br>education related to<br>sex differentials in<br>child mortality in<br>rural South Asia?|Narangwal Health<br>Project conducted<br>in rural Punjab,<br>India<br>Sample of 8,050<br>children 0-36<br>months old<br>between 1969-74|Multivariate hazard<br>model to measure<br>risk of dying<br>(associative study)|First-daughters of<br>educated mothers<br>have lower post-<br>neonatal mortality<br>compared to sons<br>than first-daughters<br>of uneducated<br>mothers. However,<br>second order or<br>higher daughters of<br>educated mothers<br>have higher post-<br>neonatal mortality<br>compared to
These assumptions on sectoral growth pin down the total growth rate for rural and urban areas, but further assumptions are needed to specify the level of the growth and declines. Note that the macro sectoral growth rates in 2020 are largely negative. For households that experienced an increase in income in 2020, we set the size of the increase to match the growth projection prior to COVID-19. Intuitively, if a household managed to grow their income in 2020 (at the same time as mean income was declining), our best guess is the growth rate that was previously projected. With this approach, we will assign each household to one of five different growth rates; two positive growth rates for urban and rural households, two negative growth rates for urban and rural households, and a zero growth rate. In the robustness section we show that our results do not change notably if we add random noise to the growth rates.

# **(iv) Estimates from the literature**

For 26 countries (covering 27% of the global population) we use estimates of distributional changes due to the COVID-19 pandemic available from various studies – including published papers and calculations available from teams in the World Bank. These studies (with the exception of India) are not based on new data collected in 2020, but instead use an econometric model to predict the distributional impact using a household survey from an earlier year. Like with the tabulated data, we often use quantile-specific growth rates from these studies and need to make strong assumptions to apply them to our data. In the case of India, we use estimates from Roy and van der Weide (2022). See Appendix B for further details.

# **(v) Countries without any other data**

For 127 countries, we were unable to use any of the sources presented thus far. Many of these countries are small, such that these countries together account for roughly 18% of the global population. For this group of countries we use three other methods in the
, India on the construction of toilets, the practice of open defecation, and beneficiaries’ hygiene-related awareness and practices. The study found that the program was successful in increasing the coverage of safely managed toilets and led to a decline in open defecation among rural households in project intervention areas. A significant reduction in open defecation was observed among households that already had toilets. The effect was less pronounced among households without toilets, which were also the primary targets of the program. Since the midline survey was conducted immediately after project completion, many households whose toilets had just been completed might not yet have had time to get habituated to using them. Moreover, many other households were still in the process of seeing their toilets constructed. So, the potential impact of the program on toilet construction and open defecation is likely to increase over time. Interestingly, GPs with intensive follow-up campaigns achieved a significant reduction in open 36