rafmacalaba/data-use-mentions-extended huggingface dataset:
NSSO|||||||
|Education|NSSO|||||||
|Wages|NSSO|||||||
|Wealth|NSSO|||||||
|Labour Regulations|Besley and Burgess (2004)|||||||
|Riots|Marshall and Marshall (2008)|||||||
|Population|NSSO/Census|||||||

Note: ASI: Annual Survey of Industries; NSSO: National Sample Survey Organisation. The values assigned to different states regarding their labour regulations, and those for road density assigned to districts do not vary over year, and the value does not vary over time.

# **4 Results: The Extensive Margin**

## **4.1 Across Firms**

The results of the econometric specification are provided in Table 4. The dependent variable is ‘start’ i.e. a dummy variable that equals one if the firm starts exporting and zero otherwise. All columns include year and industry (2-digit NIC level) controls. From left to right, the columns present estimations that include an increasing number of variables and then finally also include location-specific effects. Model specification (1) controls for firm-level characteristics only; in addition to these, model (2) includes agglomeration variables; model (3) adds first-nature geography variables; model (4) adds further infrastructure variables, and model (5) adds institutional variables. Model (6), which includes all variables, firm, second and first nature, infrastructure and institutional, also includes state fixed effects. Owing to limited data availability for 14
- Radermacher, W. J. (2021). Governing-by-the numbers–résumé after one and a half years. _Statistical Journal of the IAOS_ , _37_ , 701–711.

- <mark>Reddy, T., Nsubuga, R. N., Chirwa, T., Shkedy, Z., Mwangi, A., Awoke, A. T., ... & Janssen, P. (2023). Sustainable Statistical Capacity-Building for Africa: The Biostatistics Case.</mark> _<mark>Annual Review of Statistics and Its Application</mark>_ <mark>,</mark> _<mark>10</mark>_ <mark>, 97-117.</mark>

- Reporters without Borders. (2023). World Press Freedom Index. Available at <u>https://rsf.org/en/methodology-used-compiling-world-press-freedom-index2023?year=2023&data_type=general</u>

- Roseth, B., Reyes, A., & Amezaga, K. Y. (2019). The value of official statistics: Lessons from intergovernmental transfers. In _IDB Technical Note 1682_ . Inter-American Development Bank Washington, DC. http://dx.doi.org/10.18235/0001883

- Sachs, J., Kroll, C., Lafortune, G., Fuller, G., & Woelm, F. (2021). _Sustainable development report 2021_ . Cambridge University Press. https://doi.org/10.1017/9781009106559

- Sachs, J., Kroll, C., Lafortune, G., Fuller, G., & Drumm, E. (2023). _Sustainable development report 2023_ . Cambridge University Press. <u>https://doi.org/10.25546/102924</u>

- Sandefur, J., & Glassman, A. (2015). The political economy of bad data: Evidence from African survey and administrative
household budget or welfare surveys. While unit values might be useful in cases where price data are lacking, the problems related to unit values have been reported in several studies (Gibson and Kim 2019; McKelvey 2011). Another 26 percent of the countries use market price surveys, which are often collected in parallel to the official household budget or welfare surveys. A small number of countries use their Consumer Price Index (CPI) data for spatial price adjustment. The methodology of spatial price adjustment is more divided with countries applying different approaches (Panel B), including the Paasche index (25 percent), Laspeyres index (15 percent), Fisher index (13 percent), and so on.

> 2 Some countries rely on household income, instead of consumption, for poverty measurement (Ferreira et al. 2016).

> 3 ‘Region’ refers to subnational regions in this paper unless otherwise noted.

> 4 For example, as the cost of living in African cities is high relative to their income levels (Nakamura et al. 2019), urban poverty can be underestimated without adequate spatial price adjustment.

> 5 Some approaches to circumventing the data limitation include the Engel curve method (discussed in Appendix B of this paper) and price data collection from local experts (Gibson and Le 2018).

> 6 Africa refers to Sub-Saharan Africa in this paper unless otherwise mentioned.

2
largely placed authority in the hands of local officials and thus worked within the institutional confines of the New Order regime – with the associated negative externalities of corruption, cronyism and graft. KDP’s logic was to attempt to retain the spirit of _svadaya gotong royong_ but create new spheres of authority within SPGs that were more associated with reform: such as elected village committees and watchdogs drawn from local journalists and NGO workers<sup>15</sup> . Thus, an SPG optic was consciously employed to remake the approach of local development so that it was better aligned with the spirit of the

> 14 Note that while I am focusing on divisions within Islam, in some parts of Indonesia churches, both Catholic and Protestant, may also play a role, and in other parts Hindu temples and Buddhist viharas may provide alternative sources of authority.

> 15 A quantitative analysis of the LLI data show that a household’s participation in village government SPGs has an adverse effect on the voice and participation of neighboring households – demonstrating the “chilling” effect of SPGs that have their origins in the New Order (Alatas, Pritchett and Wetterberg  - 2002).

16
this, one can use nightlights. This methodology has been used in several papers already, with significant results (Henderson, Storeygard and Weil 2012) (Gillespie, et al. 2014) (Hodler and Raschky 2014) (Michalopoulos and Papaioannou 2014). In Henderson, Storeygard and Weil (2012), Indonesia is used as an example of using nightlights to capture an economic downturn following the Asian financial crisis in the late 1990s. Their results show that swings in GDP change can generally be captured. Nevertheless, one has to account for factors such as cultural differences in light usage, latitude and gas flares. In our case this is unlikely to affect our results since nightlights are used to capture exposure within a country rather than across countries.

All the previously mentioned studies use a prior iteration of nightlight images, the DMSP satellite images. The approximate cell grid size of these images is 1 by 1 kilometer at the equator and the data provided are annual.

This paper utilizes a newer nightlight data set, the VIIRS Day/Night Band (DNB) provided by The Earth Observations Group (EOG) at NOAA/NCEI. The data are produced following the methodology of Elvidge, et al. (2017). These data are provided starting in April 2012 and go till the present, making the time series much shorter than the DMSP data which start in 1992 and go through 2013. However, the VIIRS DNB images from EOG are monthly, whereas the DMSP data are annual. Furthermore, the images have 15 arcseconds grids (approximately 450 meters at the equator) compared to the 30 arcseconds of DMSP. The VIIRS coverage spans from 75N latitude to 65S around the entire globe, meaning that all of Southeast Asia is included. The VIIRS data have also seen usage in the economics and disaster literature. Firstly, Chen and Nordhaus’s (2015) analysis finds promising results for VIIRS as an economic and population indicator, also when comparing to the DMSP product. Secondly, Zhao, et al. (2018
further damages of USD 7 billion to total USD 126.7 billion in 2018.<sup>20</sup> We assume that the conflict intensity index in the first three months of 2018 is at the 2017 level as estimated by the ESCWA, but then improves to somewhere between the 2011‐2012 level. Given the calculated average conflict intensity index for the year, we then derive an estimate of physical damage that is proportionate to the 2017 estimate of physical damage of USD 18.5 billion (see Appendix 2).<sup>21</sup> Such an assumption of improvement in conflict intensity is not inconsistent with the dread factor<sup>22</sup> in World Bank (2019), which reached new heights in 2017, continuing into the first quarter of 2018 before appearing to taper off to around the 2012 level by May 2018, the last data point. We also consider that the SOHR notes that a record low 20,000 lives were lost in 2018, and that big data (conflict‐related events) collated and analyzed by the Global Data on Events, Location and Tone (GDELT) project suggests that conflict intensity in 2018 was lower than in 2011.

World Bank (2017b, in their Figure 2.9) estimates a decline in private investment as a share of GDP �I<sup>�</sup> ⁄Y� from 12% in 2010 to 4% in 2015, and for the public investment‐to‐GDP ratio �I<sup>�</sup> ⁄Y� – a decline from 9% in 2010 to 1% in 2015. On the other hand, ESCWA (2018) sees a smaller decline from 12% to 9% for private investment as a share of GDP (averages for 2006‐2010 and 2011‐2016 respectively) and from 10% to 7% for public investment as a share of GDP. In our simulations, we use the World Bank estimates for our central
**Estimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka**<sup>∗</sup>

Ryan Engstrom<sup>†</sup> David Newhouse<sup>‡</sup> Vidhya Soundararajan<sup>§</sup>

**Keywords:** Population density predictions, Satellite imagery, Machine learning.

**JEL Code:** J19, C52, C53.

> ∗ We thank Sarah Antos for help with the data, and Jonathan Hersh for comments and discussions, and presenting an earlier version of this paper. We also thank Sarosh Sattar, Ani Silwal, and Walker Bradley for detailed comments, and the participants of Geo4dev symposium 2017, Population Association of America Annual conference 2018, and seminar participants of the World Bank for comments and suggestions. We acknowledge the funding for this project from the World Bank group. All remaining errors are our own.

> † George Washington University, Washington, DC. Email: rengstro@gwu.edu

> ‡ The World Bank, Washington DC. Email: dnewhouse@worldbank.org

> § Indian Institute of Management Bangalore. Email: vidhyasrajan@iimb.ernet.in
, electricity expenditures should be imputed for each eligible household and in each month. The imputation model predictors include housing type, household income decile, area of location (i.e., urban/rural), square meter of the dwelling, number of household members, and the number of electronic assets in the household (e.g., TV, radio, refrigerator). Once electricity expenditures are calculated for each month, kWh usage per month can be backed out. To calculate kWh in each month, the fitted electricity expenditure is divided by the price of kWh as given by the pricing schedule assigned to the household. Households are assumed to receive more subsidies in the winter months since there is higher usage of electricity. Electricity subsidies are calculated as the difference between the estimated production cost (the upper bound of 213 MNT/kWh) of electricity minus the tariff assigned to a household, multiplied by the estimate of monthly consumption. and the household cost. The total subsidies over the year are added together to yield the total amount of electricity subsides received by a household.

Heating subsidies are applied to households reporting the survey to be connected to central heating, which almost completely exclude Ger households. Heating prices and subsidies are applied according to the size of the dwelling in terms of square meters. The subsidy is the difference between the estimated production cost and household average expenditures on heating for a given dwelling size.

> 15 The average outstanding loan is derived from official data provided by the Ministry of Finance in September 2017, indicating that there are 89,156 mortgage borrowers in the banking system, of which approximately 70 percent have preferential loans (i.e., 62,409 cases) with an average outstanding mortgage of 49.9 million MNT as of December 2016.

16 Assumptions about the percentage of households from (Schlirf, Johansen, Georgieva, & Wu, 2018). MONGOLIA Distributional impact of taxes and transfers / May 2018 9
separate out the potential effects of adaptation from the immediate welfare effects from current events, the regressions also control for the frequency with which the different events occurred.

# **5 Welfare Effects of Natural Disasters in Vietnam**

To benchmark the results, Table 1 (column 1) presents the estimated OLS coefficients<sup>36</sup> of a standard consumption model augmented with information on the present and lagged occurrence of

> 36 These were obtained from the pooled 2002, 2004, and 2006 VHLSS data with standard corrections for heteroskedasticity and clustered survey design using the Hubert‐White estimator provided in STATA.
6 is not complete. Households that had spent more than 30 percent of their total expenditures on electricity are considered outliners and dropped.

All questionnaires except the one in South Africa asked for the primary sources of energy for lighting and cooking; the South African survey asked if the household was connected to the grid, and the response to that question substitutes the question about the primary energy source for lighting in the rest of this paper.

Some surveys listed off‐grid electricity separately (typically solar and backup generators), while some had only one category called electricity. “Solar energy”—the term used in Botswana, Burkina Faso, Ethiopia, Ghana, Senegal, Swaziland, and Uganda—could mean solar home systems only or could include solar lanterns. This lack of distinction is unfortunate because solar lamps would not fall under the category of electricity access, whereas solar home systems would. Mali, Mozambique, Rwanda, Sierra Leone, Tanzania, and Zambia asked specifically about solar panels. Seventeen surveys asked specifically about grid electricity, of which sixteen asked if the household was connected to the grid, and Togo asked about paying electricity bills over the previous two months (thereby excluding those connected to the grid but had not made payments during the recall period); several asked respondents if they had their own meters or were sharing meters with others.

Spending on electricity in this paper excludes expenditures on items not considered as part of access to electricity: batteries, candles, kerosene, and liquefied petroleum gas. While fuels for generators would be part of spending on electricity, only Rwanda and Zambia enabled isolation of spending on fuel for generators; all other countries bundled spending on gasoline and diesel for vehicles or lawn mowers with spending on either fuel for generators. For consistency across countries, spending on diesel used in generators in these countries was excluded from expenditure on electricity.

The surveys did not enable systematic analysis of access at different levels according to the recently formulated multi‐tier access framework for the Sustainable Energy for All initiative.
--|---|---|---|
|**HICES**|2003-2012|No|Consumption, income|National, urban/rural|No|
|**SILC**|2009-2012|No|Income|NUTS1|No|
|**LFS**|2009-2013|Yes|Imputed Income|NUTS2|Yes|

However, there are other issues for consideration when using the LFS. Principally, there is a low number of sample points that are migrant households. Moreover, the study cannot identify migrant households and individuals that are specifically Syrian refugees. Foreign migrants are defined as those who were born abroad and have lived abroad for at least more than 12 months. Some Turkish-born households have also lived abroad for over a year, and these individuals are not considered to be migrants. Amongst foreign-born individuals, only the ones who have been in the country for more than 12 months are included in the sample which underrepresents the actual number of foreign migrants in the region. In addition, no specific procedure is adopted by the enumerators if the household does not speak Turkish. Given that a majority of Syrian refugees do not speak Turkish, the language barrier might result in the removal of Syrian households from the sample. Finally, refugee camps are not included in the sample frame, which limits the study to only examining recent migrants who do not live in refugee camps.

> 9 Wage income is only available for regular and casual employees in the LFS which accounts for around 60% of total employment. There is no other monetary income value for the rest of the working population.

5
Policy Research Working Paper 9627

# **Abstract**

This paper estimates the macroeconomic effects of social transfer payments to individuals for a sample of 23 developed and Latin American countries. The findings show that the social transfer multiplier is 0.3 in developed countries, but 0.9 in Latin American economies. The paper studies the role of hand-to-mouth consumers, who have no access to financial markets and a high marginal propensity to consume, as a first order factor to explain the heterogeneity in the size of social transfer multipliers. Using survey-based data from the Global Findex dataset, the paper finds that the average share of the population living hand-to-mouth is 23 percent in developed economies versus 60 percent in Latin American countries. This evidence is interpreted with a two-agent New Keynesian model. The findings show that the difference in the share of hand-to-mouth consumers explains 80 to 90 percent of the difference in the estimated social transfer multipliers. The paper also documents that the share of hand-to-mouth individuals in emerging countries is in general 47 percent which suggests that a larger social transfer multiplier may be expected for this type of economy.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. 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 gvuletin@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
e¤ects using employment data (obtained applying group-speci...c employment-population ratios) relative to the baseline case calculated with population data. The new estimates are hardly distinguishable from the baseline case. This is because while the employment rate is somewhat di¤erent between highly and less educated, they are very similar for native and immigrants for the same education levels in most countries. In some countries immigrants have in fact slightly higher employment rates than natives. In most countries less educated immigrants have a somewhat smaller employment rate than corresponding natives. This is true in particular for Spain and Sweden where the low employment rate of highly skilled immigrants (relative to natives) reduces the positive wage e¤ects of immigration but only slightly.

# 5.5 Accounting for physical capital sluggishness (short-run e¤ects)

While the model has, so far, evaluated the long-run e¤ects of immigrants and emigrants, by assuming full capital adjustment, we can also calculate the short-run e¤ects by accounting for imperfect capital adjustment. From the production function in (3) we can obtain the compensation to the composite factor Q, wt<sup>Q;whichcorrespondstotheaveragewage,as:</sup> where �t = Kt=Qt approximates the capital-labor ratio. In balanced growth path (or in an <u>1</u> e <u>1</u> open economy) the capital-labor ratio is given by the following expression �<sup>�</sup> t<sup>=</sup> � <u>1R�</u><sup>�</sup> <u>�</u> � � At�<sup>:</sup> Hence the average wage in the balanced growth path (aggregating immigrants and natives) depends only on total factor productivity, A<sup>e</sup> and on the rate of return of capital and not on the total labor supply Qt: In the short
the European Union, as well as in two candidate countries and in three countries of the European Free Trade Association (Iceland, Norway, and Switzerland). The survey draws on nationally representative samples; sampling rates in any country vary between 0.2 and 3.3 percent.

To assure comparability, the EU‐LFS uses output harmonization. This means that the European Union issues standards for the output of the LFS, including a list of variables and categories, minimum sampling precision, observation period, and periodicity, while the national statistical institutes across Europe are responsible for designing and conducting the survey. They must follow the standards and forward the results to Eurostat, which processes the information centrally. While some factors may make the survey less than perfectly comparable, this process of harmonization nonetheless makes it attractive for studying labor market dynamics.

This study makes use of the EU‐LFS microdata that are available in the period 1995–2013 to analyze the impact of the internet on the labor market. The list of countries and years that are covered is shown in Table A 1 in the Appendix.<sup>4</sup> All of the information is aggregated by sector of activity using the standardized NACE classification Rev. 1.1 at 1 digit.<sup>5</sup> Averages of all the variables of interest are computed for workers aged 25–64 in each sector of activity, except for those working in public administration, in extra‐territorial organizations and bodies, and households as employers, which are excluded from the sample.<sup>6</sup> Although the statistics are computed using harmonized variables, some outliers in the “working from home”

> 4 Data for Germany and the two candidate countries (Macedonia and Turkey) were not included in the 2014 release of the EU‐LFS microdata. Malta was excluded because data for this country are only available from 2009 on. 5 Unfortunately, the public version of this survey does not include a more disaggregated version
Yes|Yes|Yes|Yes|
|Country X Year FE||Yes|Yes|Yes|Yes|Yes|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching post-marriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. Husband education is the share of women in the pseudo-cohort whose husband has any post-secondary education. Women’s education is an indicator variable for any post-secondary education. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

27
category. The cutoff will vary from country to country and will depend on the purpose at hand.

The issue of a continuum versus dichotomy of urban agglomeration raises a question about another famous dichotomy in development economics, that between urban and rural. Where exactly does rural end and urban start? According to the OECD (2012) definition, a one square kilometer core cell would not be urban, and would thus be rural, if its population were less than 1,500 inhabitants. While low population density might indeed be thought of as a characteristic of rural areas, rural‐urban data for most developing countries are generated by administrative jurisdictions.
||X|

Notes: * p<0.10, ** p<0.05, *** p<0.01. _Source_ : LFS 2011 and 2013. World Bank author’s calculations.

# Table 14. OLS

||Female Hea<br>|ded Household<br>|s<br>|Male Heade<br>|d Households<br>|<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Proportion of Foreign-Born in the Population<br>Constant|0.898**<br>(0.325)<br>0.153***<br>(0.0352)|0.00125<br>(0.209)<br>0.0520***<br>(0.00296)|0.0348<br>(0.0653)<br>0.555**<br>(0.247)|0.969**<br>(0.436)<br>0.211***<br>(0.0388)|-0.107<br>(0.112)<br>0.119***<br>(0.00159)|-0.0393<br>(0.0675)<br>0.601**<br>(0.211)|
|Observations|52|52|52|52|52|52|
|R-squared|0.095|0.910|0.973|0.092|0.948|0.993|
|Year Controls||X|X||X|X