rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4.4 Does air pollution cause a reduction in income levels?**

Air pollution has been documented to be detrimental to health and productivity, and thus indirectly people’s incomes. In this section, we use a difference-in-difference design to test the potential impact of air pollution on income levels. We compare pollution levels and economic indicators in downwind and upwind areas, before and after the activation of new coal plants. First stage results confirm that differences in pollution levels are insignificant before activation, but post-activation pollution levels are significantly higher in downwind areas. However, reduced form results do not confirm meaningful differences between the income levels in up- and downwind locations. Thus, these results do not yield robust evidence that air pollution causes reductions in income levels. This result is documented in this section for the sake of completeness, and to inform future efforts for a causal identification with global coverage. Possible factors that can explain this null result are the relatively short time series of data (which may not allow long-term health and productivity effects to materialize), and the fact that health-related expenditures are included in indicators of economic activity thus diluting the impact.

To evaluate the causal effect of power plants’ operation on local economic indicators, we use a difference-in-difference design that compares upwind and downwind pixels before and after coal plant activation. The econometric specifications are as follows: where the sample is at the year-plant-pixel level. The time range is the same as the gridded income dataset, i.e. 1990-2015. To ensure that pre- and post-activation periods are included, we restrict the sample consists to 2,380 coal power units that were activated between 1992-2013 (i.e. disregarding 3,941 units that were activated outside this time frame or have missing commission years). Besides, we include up- and downwind pixels that are located within a 0.5-degree radius around plants, and drop those in crosswind directions. Upwind pixels are defined as those located -45<sup>_◦_<
waves 2 and 3, as the GHS only asks in wave 1 about the mother’s education of all individuals, but in the later waves only for new household members. As the GHS data is unfortunately not an individual level panel, it is not possible to merge information from wave 1 to the respective individuals in wave 2 and 3. Controls including the proxy information are marked with an *.

# 6 Conclusions and Policy Implications

Motivated by the lack of evidence on the poverty situation specifically of children in Nigeria, this child poverty analysis aims at providing a broad overview on the extent of severe monetary and multidimensional child poverty, its recent trends and its overlap which could potentially serve as a base for more targeted policy making.

The analysis is based on the past four waves of the General Household Surveys (GHS-1 2010/11, GHS-2 2012/13, GHS-3 2015/16 and GHS-4 2018/19) and the latest wave of the Multiple Indicator Cluster Survey (MICS 2016/17). Using the GHS surveys has the advantage that it allows the calculation of the overlap between monetary and multi-dimensional poverty estimates. The MICS data on the other hand only allows the estimation of multi-dimensional poverty measures, but provides external validity to the 56
us that such a sub-aggregate will exist only under very special (and highly implausible) conditions. In particular, even if one can identify goods with linear Engel curves given prevailing prices, under different prices this linearity may fail.

However, from a practical point of view it may be possible to identify goods with linear Engel curves in one period, and then we may get lucky: in a subsequent period perhaps relative prices will not have changed in such a way that linearity will be compromised, and our sub-aggregate will work for measuring poverty.

4.1. **Data.** In the remainder of this paper we test our luck using data from expenditure surveys in Rwanda, Uganda, and Tanzania.<sup>5</sup> All surveys are large multipurpose household consumption surveys, representative at both national and urban/rural levels with large sample sizes. In Uganda about 3000 households were interviewed each year, in Tanzania between 3000 and 4000 each year, and in Rwanda the number of households increased substantially from 6900 to 14300 household observations per year.

> 5. Rwanda: Enquete Int ́egrale sur les Conditions de Vie des m ́enages de Rwanda (EICV1) 2001 and (EICV2) 2006 . Uganda: Uganda National Household Survey (UNHS) 2005/06 and 2009/10. Tanzania: Tanzania National Panel Survey (NPS) 2008/09 and 2010/11.
in June 2020 painted a gloomier picture, predicting that the number of newly poor individuals would range from 71 million to 100 million by the end of 2020 (Mahler et al., 2020b).

The second strand of literature uses interviews of representative parts of the population to understand socio-economic impacts, also as input to parameterize modeling and forecasting efforts. In the context of the pandemic, most interviews were conducted by phone<sup>2</sup> and – to a smaller extent – by using online surveys. Khamis et al. (2021), for example, use data from the high-frequency phone survey (HFPS) to estimate the early impact of COVID-19 on the labor markets of 39 countries. Their findings show that the pandemic has negatively affected labor market outcomes in these countries with respect to job and income losses, lack of payment and job changes. In a related paper, Kugler et al. (2021) focus on the distributional implications of the crisis, showing that female, less educated, and younger workers were initially more severely impacted. Bundervoet et al. (2021) demonstrate that the pandemic’s initial effects were both widespread and highly regressive, with the most vulnerable segments of the population being disproportionally affected.

The third strand of studies makes use of remote sensing indicators to monitor economic and other human activity during the COVID-19 pandemic. For example, the literature has long recognized that satellite images can be used to monitor the state of economic development in places where this information is difficult to obtain by other means (e.g., Henderson, Storeygard and Weil, 2012; Donaldson and Storeygard, 2016; Beyer et al., 2018; Kim, 2022). Studies have documented

> 2 For example, the World Bank conducted phone surveys in dozens of countries and published a harmonized dashboard available at https://www.worldbank.org/en/topic/poverty/brief/high-frequency-monitoring-surveys.

2
*</sup>|
||(0.164)|(0.206)|(0.163)|(0.207)|(0.163)|(0.203)|(0.163)|(0.204)|
|Assistance received (private)||-0.014||-0.010||-0.013||-0.029|
|||(0.027)||(0.027)||(0.034)||(0.038)|
|Assistance received (government)||0.053||0.083||0.023||0.064|
|||(0.079)||(0.067)||(0.090)||(0.077)|
|Individual FE<br>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|No|No|Yes|Yes|No|No|
|Round × Ind/HH characteristics|No|Yes|No|No|No|Yes|No|No|
|MOB × Ind/HH characteristics|No|No|No|Yes|No|No|No|Yes|
|Obs.|5235|5016|5235|5016|3614|3466|3614|3466|

_Note:_ Cluster-robust standard errors in parentheses.<sup>*</sup> _p_ < 0.1,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01. MOB indicates the Facebook mobility index with the mobility level in February 2020 as the baseline. Individual and household characteristics include age, sex, education, household size, dependency ratio, self-employment, industries, internet access, and consumption quintiles.
(Nor’wester). As described in section 2.2.2, these storms can be proxied by data on lightning strikes. The seasonal pattern of lightning strikes resembles strongly the occurrence of power outages, thus confirming that most power outages that occur in Bangladesh due to natural causes, are indeed caused by Nor’wester storm in the months April and May. This observation is consistent across the entire data period from 2000 to 2014, as the high correlation between outages and lightning strike time series confirms (Figure 4.6).

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**4. Conclusion**

Around the world, governments are producing more and more data, yet it is highly doubtful that the full potential value of the data is being reaped. In this paper, we have presented a theoretical and empirical argument for why the returns to existing government data might not be maximized. We show that for data to yield returns for governments they must live up to certain conditions, these being that they are of adequate spatial and temporal coverage (complete, frequent, and timely), of high quality (accurate, comparable, and granular), easy to use (accessible, understandable, and interoperable) and safe to use (impartial, confidential, and appropriate). Through case studies covering a wide range of countries and topics, we illustrate how these 12 features matter in practice for generating value from data.

Too often governments are concerned with producing a particular data set, with less of an eye to how those data will generate value. It is imperative for governments not only to invest in more data, but also the right data, and working towards the safe use of data. Often the value derived from data is determined by the features least present, emphasizing the need for a comprehensive analysis of which of the 12 conditions elicited in this paper may be lacking. This can ensure that the whole chain from data production to data use is in place and the data can come closer to realizing its maximum value for development.

22
, so the program ended up being poorly targeted.

A new targeting approach pioneered in Togo used mobile phone usage patterns to identify poor households. While this approach does not reach the level of targeting effectiveness of the ideal unified social registry approach, it did compare favorably to broad-based geographical targeting (Aiken et al. 2022).

# **5.2 Evidence on targeting performance during the pandemic**

Targeting analysis of the COVID-19 social protection response is already available for some selected countries in spite of the constraints on collecting household level data and subject to the caveats noted above that the ideal response would not necessarily target the household which were poorest pre-COVID. Labor market monitoring surveys in Tunisia, Morocco, and Egypt in February 2021 show that social protection targeting was fairly progressive in Egypt with the poorest (pre-COVID) income quartile having almost double the average probability of receiving assistance. On the other hand, in Tunisia, the poorest quartile only had 1.5 times the average probability of receiving assistance and in Morocco, the poorest quartile was only slightly more likely to receive assistance than average. Beyond income quintiles, in Egypt and Morocco, informal workers were two to four times more likely to have lost their work during the pandemic than formal sector workers. Egypt’s social protection response specifically targeted informal workers, and 26
21

**Figure 10**

<!-- Start of picture text --> Simulated changes in poverty<br>using regional rates with inequality adjustment<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text -->

# **5.7 Simulation using “Two Survey” Approach**

Finally, simulations were carried out using the “two survey” approach. This was done in two ways. In the first, the national distribution of consumption was used, and national GDP per capita growth rates from the national accounts were applied. In the second, changes in the distributions were simulated separately for each of the three strata: Dar es Salaam, other urban, and rural areas. For this second approach, urban sector national accounts growth rates were used for Dar and other urban areas, while rural sector national accounts growth rates were used for the rural areas stratum. Results from these two simulations are shown in Appendix Tables A11 and A12, and Figures 11 and 12 below. As expected, the simulated values for 2001 closely match the estimates from the 2000/01 HBS.<sup>10</sup> These simulations show broadly the same patterns as the simulations using the Datt and Walker approach: rising or flat poverty in the initial years, followed by small declines. Unlike the Datt and Walker simulation, these are able to capture the divergence between the rate of declines in poverty in Dar es Salaam and the rest of the country.

Unlike the simulation using the national distribution and national growth rates (Figure 11), the simulation using within stratum distributions and urban/rural growth rates (Figure 12
. "Promoting Recovery and Resilience for Internally Displaced Persons: Lessons from Colombia." _Oxford Review of Economic Policy_ 38 (3): 595-624. Jouanjean, M.-A. 2019. "Digital Opportunities for Trade in the Agriculture and Food Sectors." OECD Publishing, OECD Food, Agriculture and Fisheries Papers: 122. Khanna, M. 2021. "Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications." _Applied Economic Perspectives & Policy_ 43 (4): 1221-1242. Korovkin, V. and A. Makarin. 2023. "Conflict and Intergroup Trade: Evidence from the 2014 Russia-Ukraine Crisis." _American Economic Review_ 113 (1): 34-70. Kussul, N., et al. 2022. "Biophysical Impact of Sunflower Crop Rotation on Agricultural Fields." _Sustainability_ 14 (7): 3965. Kussul, N., et al. 2023. "Assessing Damage to Agricultural Fields from Military Actions in Ukraine: An Integrated Approach Using Statistical Indicators and Machine Learning." _International Journal of Applied Earth Observation and Geoinformation_ 125: 103562. Leasure, D. R., et al. 2023. "Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data." _Population and Development Review_ 49 (2): 231-254. Lee, J. S. H., et al. 2020. "Does Oil Palm Certification Create Trade-Offs between Environment and Development in Indonesia?" _Environmental Research Letters_ 15 (12): 124064. Merkus, E. 2024. "The Economic Consequences of Environmental Enforcement: Evidence from an Anti-Deforestation Policy in Brazil." _World Development_ 181: 106646. Moffette, F. and H. K. Gibbs. 2021. "Agricultural Displacement and Deforestation Leakage in the Brazilian Legal Amazon." _Land Economics_ 97 (1): 1-26. Moffette, F., M. Skidmore and H. K. Gibbs. 2021. "Environmental Policies That Shape Productivity: Evidence from Cattle Ranching in the Amazon." _Journal of Environmental Economics
94 2004‐05 2011‐12<br>rural Urban<br><!-- End of picture text --> Source: Authors’ calculation from NSSO data.

Now, turning to the relationship between the FLFP rate and levels of educational attainment, posessing secondary and higher secondary levels of education were not found to be an incentive for women to participate in the labor market in India (Figure 3). The lowest incidence of FLFP 15
across countries encourages the use of richer data sources. Lee & Braithwaite (2022) combine daylight satellite imagery and OSM data in an iterative ML framework that has proven able to predict wealth within and across SSA countries reliably. As an attempt to verify, taking a city-population weighted average of the IWI by countries yields a correlation of 0.62 with the 2020 World Bank national GDP/capita estimate and a correlation of 0.73 with the log of GDP/capita. Using NASA’s Black Marble nightlights, on the other hand, only yields a correlation of 0.15 between city-population weighted nightlights/capita and national GDP/capita. However, using the log of nightlights/capita yields a 0.58 correlation with GDP/capita and a 0.65 correlation with the log of GDP/capita. While this appears more promising, a visual comparison, provided in Appendix Figure A10 for the interested reader, shows that the IWI is higher in larger cities in richer countries - in line with much scientific evidence. The nightlights measure appears quite noisy and imprecise.

32
with the heaviest rainfall (Idukki, Kottayam and Kollam), but had more long-lasting effects in the districts with the worst floods (Alappuzha, Kottayam, Ernakulam and Thrissur).

We argue that increased labor demand due to reconstruction efforts could have increased wage income after the floods and provide corroborating evidence: (i) rural labor markets tightened, (ii) poorer households benefited more, and (iii) wages increased most where government relief was strongest. Immediate relief provided by the Government of Kerala after the disaster was a major factor in the recovery of household income. Specifically, we found that a 1 percent higher assistance per capita contributed roughly 4 percentage points higher household income between August and December.

Based on annual data, Raddatz (2009) concludes that most of the impacts of natural disasters materialize within the first year. Our analysis based on monthly and quarterly data shows that the negative impact can be even shorter and that there are interesting economic dynamics during and right after a disaster within the first year.

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The rest of the paper is organized as follows. Section 2 describes the data sets used and presents wage change histograms for selected years, discussing their main features and why they are suggestive of different types of rigidities. Section 3 discusses the methodology used to deal with measurement errors and the joint estimation of the different types of rigidity. Section 4 presents the main results of the paper. In section 5 we extend the analysis of the incidence of wage rigidities to differentiate across worker and characteristics. Section 6 concludes.

# 2 Data

## 2.1 Data Sources and Sample Selection

We use annual administrative employer-employee matched data from Brazil (1995-2002) and Uruguay (1996-2004). The main difference between the two data sets is that while the former is comprehensive, including information for the universe of workers in the formal sector in Brazil, the latter provides us with information on a random sample of workers in Uruguay’s formal sector.

Our data source for Brazil is the labor market census RAIS (Relação Annual de Informações Sociais), an administrative data set collected annually by the Brazilian Labor Ministry. By Brazilian law, all employers in the formal sector must report detailed information for all their workers to RAIS every year. RAIS includes information about workers (sex, age, education) and their jobs (type of contract, occupation, average monthly wage earned during the year, wage earned in December, and the amount of hours usually worked per week), as well as some characteristics of the establishment (sector, region, municipality). Importantly, RAIS also provides firm, establishment, and worker identifiers which, together with the dates of admission and separation, allow us to accurately identify year-to-year job stayers.

We restrict our Brazilian sample to the state of Minas Gerais. We do so for two reasons. First, using the whole RAIS sample, which is huge, would make data handling impracticable. Second, we have confirmed, using representative survey data, that Minas Gerais is well suited to represent the Brazilian economy
firm size distribution adapts to the business environment, and Demirguc-Kunt, Love, and Maksimovic (2006) show that certain organizational forms are better adapted to specific business environments. Nevertheless, the analysis in this paper can be seen as a way of identifying and targeting the most binding constraints for _existing_ firms, conditional on having entered, but not necessarily identifying the constraints to entry. Second, this paper examines cross-country firm-level regressions and therefore does not detail the experience of any single country in depth. However we believe that having controlled for country fixed effects, we have useful - albeit not definitive - information from the crosscountry set-up on the binding constraints to firm growth. Finally in the absence of panel data and firm fixed effects, we have to recognize that potential reverse causality concerns are endemic to the growth literature. We address these issues in detail using instrumental variables in the robustness section of the paper.

The paper is organized as follows. The next section describes the methodology. Section II discusses the data and summary statistics. Section III presents our main results. Section IV presents the conclusions and policy implications.

# **I. Methodology: Identification of Binding Constraints**

Numerous studies argue that differences in business environment can explain much of the variation across countries in firms’ financial policies and performance. While much of the early work relied on country-level indicators and firms’ financial reports, more recent work has relied on surveys of firms which provide data on a wide range of potential obstacles to growth.<sup>3</sup> 6
estimates of the multiplier increase only slightly relative to the benchmark estimates, ranging from 0.46 to 0.50.

A second way of partially addressing this concern is instead to rely on national accounts data on government consumption. This alternative spending measure excludes interest payments and transfers, and has the additional virtue of being much more readily-available than the data on government spending on which my benchmark results are based. However, the drawback of this measure is of course that it reflects government consumption expenditures only, and excludes government investment expenditures. I report results using this alternative measure of government spending in Columns (7)-(9) of Table 6. Unfortunately, using this measure of government spending results in much weaker identification, with first-stage F-statistics ranging from 6.5 to 11.0. This is only natural, to the extent that disbursements on loans from official creditors are disproportionately likely to finance public investment expenditures rather than consumption expenditures. Moreover, to the extent that government investment has a positive effect on output, the 2SLS estimates of the government consumption multiplier will be biased upwards, as the predicted disbursements instrument will be positively correlated with excluded public investment expenditures. Consistent with this observation, the resulting estimates of the multiplier are considerably higher than in the benchmark specification of Table 4, and range from 0.74 to 0.81. Naturally, they are also much more imprecisely estimated, with standard errors ranging from 0.40 to 0.54 (as compared with 0.20 to 0.25 in the benchmark specification).

A third potential concern with my results has to do with anticipation effects. I identify the government spending multiplier using fluctuations in predicted disbursements on loans from official creditors, which I have argued are plausibly uncorrelated with future macroeconomic shocks. At the same time, however, the spending plans set in motion at the time of loan commitment, as well as the associated burden of future taxes required to eventually repay the loan, are in principle both known at the time of loan