rafmacalaba/data-use-mentions-extended huggingface dataset:
# **References**

- Alegana, V. A., Atkinson, P. M., Pezzulo, C., Sorichetta, A., Weiss, D., Bird, T., ErbachSchoenberg, E., and Tatem, A. J. (2015). Fine resolution mapping of population agestructures for health and development applications. _Journal of The Royal Society Interface_ , 12(105):20150073.

- Anderson, W., Guikema, S., Zaitchik, B., and Pan, W. (2014). Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru. _PloS one_ , 9(7):e100037.

- Athey, S. (2017). Beyond prediction: Using big data for policy problems. _Science_ , 355(6324):483–485.

- Biljecki, F., Ohori, K. A., Ledoux, H., Peters, R., and Stoter, J. (2016). Population estimation using a 3d city model: A multi-scale country-wide study in the netherlands. _PloS one_ , 11(6):e0156808.

- Centre for International Earth Science Information Network (2018). High resolution settlement layer. Retrieved from https://www.ciesin.columbia.edu/data/hrsl/ on August 4th 2018.

- Department of Census and Statistics (2015). Household income and expenditure survey 2012/13. final report. Department of Census and Statistics, Ministry of Policy Planning Economic Affairs, Child Youth and Cultural Affairs, Sri Lanka.

- Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., Blondel, V. D., and Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. _Proceedings of the National Academy of Sciences_ , 111(45):15888–15893
percent level. Hence technology may also reduce the distortions caused by historical legacies. We find no significant difference in the effect of free-trade agreements, borders, or languages.

Columns (3) and (7) of Table 1 add shipping costs to the set of explaining variables. Since these are not available for offline data, they are not usually included in gravity equations. But since our eBay data include shipping costs, we include this bilateral ad-valorem average as a control both online and offline where it may also be a valid proxy for shipping costs. Surprisingly, we find no significant effect for shipping costs,<sup>13</sup> and our results are unaffected by this inclusion, which can be explained by the fact that shipping costs are not necessarily correlated with distance.<sup>14</sup> Columns (4) and (8) provide the results using the Poisson pseudo-maximum likelihood estimator which was suggested for gravity models by Santos Silva and Tenreyro (2006) to control for heteroscedasticity. Again we find that distance matters more offline. The estimated distance elasticity is around 55 percent smaller online.

To check that our result is not driven by a composition effect within the online and offline bundles, we estimate gravity equations for each eBay category using the specification of column (2) of Table 1. The estimated coefficients, using both LSDV and Poisson pseudo maximum likelihood estimators, are summarized in Figure 5 which shows that distance always has a bigger effect offline. It is on average 2.5 times bigger. Pooling the product regressions together and estimating an average effect using importer-category and exporter-category fixed effects yields distance coefficients of -0.287 online and -1.167 offline (columns 1-2 of Table 5).

In Table 3 we include the results of various robustness checks. As an important part of eBay trade is in used goods (25 percent) or occurs through auctions (65 percent) we replicate Table 1 disaggregating imports into used vs. new goods (this is done on
ings, schooling, age, gender, etc. We use some these variables as controls or ancillary outcomes in our baseline regression.

To obtain measures of health, we use the Demographic and Health Survey (DHS) for 20072009.<sup>16</sup> This survey also includes geographical coordinates of each household’s residence. However, the location is randomly displaced by up to 3 kilometers. This introduces an additional measurement error. The DHS contains self-reported information on children’s health conditions in the last 2 weeks. Based on these data, we construct indicators of having an acute respiratory disease (i.e., cough accompanied by short breath), fever, diarrhea, or anemia.<sup>17</sup>

**Pollution data** We obtain daily measures of key pollutants such as PM2 _._ 5, PM10, nitrogen dioxide (NO2) and sulfur dioxide (SO2) for years 2007-2011. These measures provide average levels of pollutants for a 24 hour-period and were collected every 2-3 days each week.<sup>18</sup> The air pollution data come from five monitoring stations installed and operated by an office of the Ministry of Health (Direcci ́on General de Salud - DIGESA). The monitoring stations were located in the four cardinal points of the city plus one in the city center (see Figure 3).

Note that these monitoring stations were installed as part of the first project to systematically measure air quality in Peru.<sup>19</sup> We complement this dataset with data on monthly average temperature and humidity collected by SENAMHI.

**Matching labor, health and pollution data** To construct the measure of pollution for a given household, we first define the reference period for labor outcomes. This corresponds to the seven days prior to the interview date. We call this reference period week _t_ . Then we obtain the average level of a pollutant from monitoring stations in week _t −_ 1.<sup
the MVP sites. But the explanation in the above paragraph heightens rather than alleviates concerns that the MVP sites differed systematically from other potential sites, in particular in that they were apparently seen to have greater levels of political buy-in and community ownership, which might well be important factors for the intervention’s success. There is no indication that the project insisted on equal levels of political buy-in and community ownership in the comparison villages. This makes differences in outcomes between the treated and comparison villages harder to attribute to the effects of the intervention. It is also an important reason why even the differences-in-differences estimates we report above could greatly overstate the effects of a scaled-up version of the project.

A second weakness of the protocol concerns the process of selecting the comparison villages. The comparison village selection process is described as follows: The choice of candidate villages for comparison was at random, and informed by both scientific and operational issues. Scientifically, a detailed matching process is undertaken to adequately characterize village attributes. DHS data are not sufficiently disaggregated to the degree of granularity such that potential local variations in levels child mortality between MV and comparison candidates are not known in advance. The matching process therefore involves collecting data on village-level parameters with the potential to influence child mortality and related MDG outcomes including: agro-ecological zone, accessibility (distance to tar roads and markets), principal livelihood strategies, crop types, levels of electrification, numbers of clinics and schools, and the presence of other development projects in the area. A full inventory of village matching criteria is detailed in Appendix 2. This inventory is collected both for the intervention and comparison villages and updated in parallel with each round of surveys. Operationally, comparison villages had to be logistically accessible to evaluation field teams, yet far enough away from the intervention site that ‘spillover’ from the MVP would be unlikely. The same field teams will conduct assessments in both intervention and comparison sites to ensure a standardize approach to the evaluation, as well as to minimize and evenly distribute potential bias.

This
Our evidence is also supportive of this indirect mechanism, but only in countries with certain types of commodity exports. Isham, Woolcock, Pritchett and Busby (2005) argue rent-seeking associated with the resource course is greater in fuel exporting economies because fuel extraction is point-based, meaning revenues typically transit directly through government coffers, in contrast with economies exporting commodities with a more diffuse production base. Consistent with this, we find that control of corruption measured by expert surveys is weaker in economies dependent on fuel exports compared to economies specializing in exports of ores and metals or food commodities. In our sample, the association between tariffs and import market concentration is stronger for economies that are dependent on fuel exports than for those that are dependent on ores and metals or food. Using a measure of tariff evasion based on the underinvoicing of imports relative to exports reported by partner countries, we find a positive relationship between the tariff rate and tariff evasion in economies dependent on fuel exports, but not in economies dependent on other commodity exports. This evidence suggests that tariff evasion explains import market concentration especially in fuel exporting economies.

Our paper is the first to systematically explore differences across countries in import market structure, contributing to several literatures beyond that about the resource curse. While the export sector has been the traditional focus of the trade and development literature, in developing countries the value of imports is about as large as that of exports, and many exported goods are produced using imported inputs (UNCTAD, 2021). We identify patterns in import market structure that contrast starkly with those in studies examining export market structure. Fernandes, Freund and Pierola (2016) use the same customs transactions data on the export side to document that higher-income economies have more exporting firms, but also more concentrated export markets dominated by “superstars,” or firms with especially large market shares, whose characteristics are described by Freund and Pierola (2015, 2016).<sup>4</sup> The pattern in import markets is the opposite. Higher-income economies

> 4Freund
(Lehner et al. 2006) for a model of flow accumulation and direction. Since impoundments and/or diversions affect the amount and timing of water reaching the gauge, we used GIS to display the geo-referenced dam data

> 5 We have used data until 2009 in this analysis.

> 6 HydroSHEDS is a dataset in the public domain of conditioned Shuttle Radar Topography Mission (SRTM) elevation data (90m resolution) that used a series of processing steps that alter the elevation values in order to produce a surface that drains to the coast (except in cases of known internal drainages). Further steps include filtering, lowering of stream courses and adjacent pixels, and carving out barriers to streamflow.  Flow accumulation and flow direction grids (30 arc seconds) were downloaded at: http://gisdata.usgs.net/Website/HydroSHEDS/viewer.php.

9
# **Introduction**

A central driver of economic growth over the past century has been the increased role of women. This growth in the role of women comes in many forms: increased female labor force participation generally,reduced discrimination and wage differentials that encourage greater effort, improved advancement practicesthatpromote talented women into leadership and managerial roles, and similar. Simply put, empowering half of the potential workforce has significant economic benefits beyond promoting gender equality (Duflo 2005, World Bank 2012). This growth stimulus is particularly true for female entrepreneurship and the economic dynamics that entrepreneurship promotes. Yet, as Klapper and Parker (2011) review, we are still just beginning to identify the local business and social factors that unlock female entrepreneurship.

This paper usesdetailed micro-data on the unorganized manufacturing and services sectors of India in 2000-2005 to explore the spatial factors that promote female entrepreneurship and business ownership. The micro-data are a representative sample of the Indian economy, and the establishment records identify the gender of the owner for proprietary establishments (which account for greater than 95% of establishments). For most surveys, we can also identify establishments that are new entrants. From these micro-data, we develop relative rates of female entrepreneurship and business ownership at the district-industry-year level.

The central focus of our paper is on identifying and quantifying the importance of existing female business networks for promoting subsequent entrepreneurship among women. We primarily evaluate these local conditions using conditional estimations that include industryyear and district-year effects. These estimations isolate variations inincumbent industrial conditions by district-industry-year. We prepare measures of the overall incumbent female business ownership in the district-industry and indices of how favorable the district’s broader industrial structure is to new entrants in terms of typical agglomeration factors: the suitability of local labor markets and the strength of input-output markets for buying and selling goods. These indices link measures of how related two industries are on these two dimensions with the relative presence of industries in each district.

Our estimations emphasize that favorable incumbent industrial conditions increase the subsequent relative rate of female entrepreneurship and business ownership
considerably more vulnerable than others. In a country where dependency on men ─ first one‘s father and then one‘s husband ─ is embedded in the law and culture, one might expect shocks such as losing a father or becoming a widow to be closely associated with worse living standards and current poverty. The paper presents empirical evidence that confirms this hypothesis. However, the paper also presents evidence suggesting that the disadvantages and poverty faced by widows have longer lasting deleterious welfare and growth consequences by way of their dependents, even when the widow remarries.

Drawing on the literature, the following section details the various forms gender differentiation within and across households can take and how this may result in heightened vulnerability. In this context, the role and existence of mitigating social safety nets in Mali is also discussed. Sections 3 and 4 then turn to the quantitative data for this study with analyses of the 2006 Enquête Légère Intégrée Auprès des Ménages (ELIM) and Demographic and Health Survey (DHS) for 2006, respectively. As one way to get at these issues with existing household level data, Section 3 first examines female headed households and whether they are poorer than male headed households and under what circumstances. This leads me to focus on a specific shock, namely widowhood. Section 4 first addresses the possibility of selection into widowhood and presents evidence that this is not an important source of bias. Using the DHS, the paper then provides evidence that gender differentiation has a particularly significant and negative effect for ever-widowed and less-so for ever-divorced women. A final section concludes.

# **2. Understanding the sources of vulnerability for women in Mali**

Welfare relevant gender differences can be identified along a number of dimensions, including: legal protection, individual endowments, preferences, constraints stemming from social norms (for example, on options outside the household), and access to and control over household and community assets and resources. Such gender differences within the household also interact with age in influencing welfare outcomes, including inequality between men and
. _American Economic Review 110_ (6), 1821–1865.

- Nakamura, E. and J. Steinsson (2014, March). Fiscal Stimulus in a Monetary Union: Evidence from US Regions. _American Economic Review 104_ (3), 753–792.

- Renkin, T., C. Montialoux, and M. Siegenthaler (2020). The pass-through of minimum wages into US retail prices: evidence from supermarket scanner data. Working Paper.

- Rhodes, A. (2015). Multiproduct Retailing. _Review of Economic Studies 82_ (1), 360–390.

- Sato, K. (1976). The Ideal Log-Change Index Number. _The Review of Economics and Statistics 58_ (2), 223–228.

- Stroebel, J. and J. Vavra (2019). House Prices, Local Demand, and Retail Prices. _Journal of Political Economy 127_ (3), 1391–1436.

36
price gap specification produces identical results to Atkin and Donaldson (2015). That is, we get ˆ ˆ _β_ = 0 _._ 056 (0 _._ 007) compared to _β_ = 0 _._ 056 (0 _._ 008).

We can also compare our results to previous attempts to recover transport costs in Rwanda using this methodology, albeit with different data, by Bougna et al. (2020). Instead of using CPI data, Bougna et al. (2020) use market price data from November 2016 to July 2018 collected by the Government of Rwanda in collaboration with the World Bank in the context of an impact evaluation of a large feeder road rehabilitation program. They estimate a pass-through rate equal to 0.34, close to our estimate of 0.39, and _β_<sup>ˆ</sup> = 0 _._ 106, which is higher than our estimate using the full sample, but similar to our estimate using the sample of locally-produced goods only (0.120).

# **7 Conclusion**

We apply the methodology developed by Atkin and Donaldson (2015) to estimate intranational transport costs, accounting for spatial variation in markups across a sample of Eastern European and African countries. Our results suggest significant heterogeneity in the magnitude of intra-national transport costs across the countries in our sample. Moreover, we find that our estimates are sensitive to the choice of products, with most countries showing higher transport costs when we restrict the sample to locally produced goods, ex12The percentage of pass-through rates below one is slightly higher, 93.1%, and we obtain a lower fraction of estimates below zero, 12.6%, compared to 17%.

21
PBAs)|Program-based approaches (budget support + non-<br>budget support PBAs)/total assistance (%)|

> 6 <u>The Paris Declaration on Aid Effectiveness</u>

> 7 Appendix 1 has list and brief explanations of 15 indicators.

> 8 Seventy-eight countries include both Sudan and South-Sudan.  As Sudan data includes South-Sudan, this paper used data of 77 countries excluding South Sudan in the 2011 survey.

5
<!-- Start of picture text --> How costly are austerity measures in deepening recessions? To what extent<br>does a country’s degree of indebtedness affect the impact of stimulus and<br>austerity? What are the merits of using tax policy relative to expenditure<br>measures in using fiscal policy for stabilization- or debt-reduction purposes?<br>These important policy questions have stimulated a significant revival<br>of the empirical study of the effects of fiscal policy on economic activity.<br>This literature has focused primarily on the United States or other OECD<br>countries.<br>In a recent contribution, Izetzki, Mendoza and Végh (2010) (IMV) as-<br>sembled high-frequency data on government purchases for a large sample<br>of countries. IMV then estimate the effects of government expenditure on<br>output using structural vector autoregressive (SVAR) methods. We extend<br>their evidence along two dimensions. First, we present a new dataset on tax<br>revenues, tax rates, and tax elasticities for a sample of 28 countries, most<br>of which are less-developed. This new dataset allows us to jointly estimate<br>the effects of taxes and government purchases on output using panel time-<br>series econometric methods. Second, using methods introduced by Favero<br>and Giavazzi (2007), we explore the degree to which government indebted-<br>ness affects the impact of fiscal policy.<br>An outline of the paper, and its main findings, can be summarized as<br>follows. In Section 2, we outline the new dataset and discuss the methodology<br>used to calculate average marginal tax rates and tax elasticities. Section 3<br>is a preliminary exploration of the data, where we use panel data methods<br>(OLS and Arellano-Bond GMM estimates) to estimate the effects of tax<br>
u>

Farnworth, C. R., Jumba, H., Otieno, P.E., Galie, A., Ouma, E., Flax, V.L., Schreiner, M-A., & Colverson, K. (2023). Gender roles and masculinities in leveraging milk for household nutrition: Evidence from two districts in Rwanda. _Food Policy_ , 118. https://doi.org/10.1016/j.foodpol.2023.102486

- Fletschner, D., & Mesbah, D. (2011). Gender disparity in access to information: Do spouses share what they know? _World Development_ , 39(8), 1422-1433. doi:10.1016/j.worlddev.2010.12.014.

- Frank, E. (1999). Gender, agricultural development and food security in Amhara, Ethiopia: The contested identify of women farmers in Ethiopia. USAID. https://pdf.usaid.gov/pdf_docs/pnacg552.pdf

- Han, X., Ding, S., Lu, J., and Li, Y. (2022). Global, regional, and national burdens of common micronutrient deficiencies from 1990 to 2019: A secondary trend analysis based on the Global Burden of Disease 2019 study. eClinicalMedicine, 44: 01299. doi: 10.1016/j.eclinm.2022.101299.

Harris-Fry, H., Prost, A., Beaumont, B., Fivian, E., Mohanty, S., Parida, M., Pradhan, R., Sahu, S., Padhan, S., Mishra, N., Rath, S., Rath, S., Koniz-Booher, P., Allen, E., & Kadiyala, S. (2023). Intrahousehold power inequalities and cooperation: Unpacking household responses to nutrition-sensitive agriculture interventions in rural India. _Maternal Child Nutrition_ , 19(3): e13503. <u>https:/
on the intercept term only marginally changes the estimated income mapping. Based on this pseudo-panel estimation, we check whether the income dynamics for the average individual in any of our cohorts give rise to a poverty trap and find they do not. Thus, while income mobility is low in Mexico, there is no evidence for a poverty trap in income. These results continue to hold once we allow for slope parameter heterogeneity across education groups, and when we estimate dynamics for full household income and expenditure using an alternative data set.

The remainder of the paper is organized as follows: Section 2 describes the econometric method used to estimate nonlinear income dynamics and detect poverty traps with panels and pseudo-panels; Section 3 describes the ENEU data; Section 4 provides the results; Section 5 examines the robustness of our results to slope-parameter heterogeneity and to the use of alternative measures of household resources than labor earnings; and Section 6 concludes.

# 2 Econometric Method

## 2.1 Panel Data without Measurement Error

We follow Jalan and Ravallion (2004) and Lokshin and Ravallion (2004) in allowing for non-linear income dynamics in the form of a cubic function of the lagged dependent variable, and in allowing for individual heterogeneity through the inclusion of unobserved individual effects, αi. We discuss below extension to higher-order polynomials. The data generating process for the true income Yi,t<sup>∗ofhouseholdiintimeperiodtisassumedtobe:</sup> If the true values of income are observed and the ui,t are serially uncorrelated, then equation (1) can be estimated via the GMM method of Arellano and Bond (1991). This is the method used by Jalan and Ravallion (2004), who difference (1) and then use Yi,t<sup>∗</sup> −2<sup>,</sup> ¡Yi,t<sup>∗</sup> −2¢2 and ¡Yi,t<sup>∗</sup> −2¢3 as instruments for ∆Yi,