rafmacalaba/data-use-mentions-extended huggingface dataset:
households, and the gender agricultural productivity gap result in higher tax burdens for women than for men. A more progressive perhectare tax schedule with exemptions for smallholders from paying agricultural income tax would reduce the tax burdens for women, but it would continue to be regressive.

We also found a substantial difference between self-reported tax payments and tax liabilities imputed based on land area and the tax schedules. The discrepancies in reported tax payments and estimated tax liabilities point to the importance of administrative tax data and land registries to complement survey data so as to ensure that 21
**Figure 4 – Income Gains: Production during Years Lost, by Decile, by Decile**

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1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>Deciles<br>Low-bound elasticity Medium elasticity Upper-bound elasticity<br>Income Gains (%)<br><!-- End of picture text -->

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Estimates assume a price shock of 25 percent.

# **Net effects: Total distributional impact**

In this subsection, we add the previous results to estimate the aggregate effect of an increase in tobacco taxes (see equation 1). The aggregate effect of an increase in tobacco taxes is positive and progressive; in the long run, poorer deciles benefit more than richer ones. The positive effect of reduced medical expenses and years of life gained offsets the negative price effect.

Under any tobacco price elasticity scenario, the overall effect for cigarettes is always positive and progressive (table 9 and figure 5). Moreover, the benefits are amplified if we compare the lower-bound elasticity with the upper-bound elasticity. Apparently, a population in which medical expenses are high and which is sensitive to tobacco price changes will reduce consumption sufficiently to allow work and health benefits to offset cost increases. This is exactly what happens in Russia. Furthermore, the assumptions in this model do not include other possible policies, such as smoking cessation programs, antismoking advertising, youth outreach, or policies financed through the new tax revenue. Therefore, these results are in line with the literature, showing the important role that
costs (e.g. Feyrer 2009), (ii) information frictions (e.g. Chaney 2011), or (iii) trust frictions.<sup>2</sup> In order to appreciate what is driving the distance-reducing effect brought about by eBay, we need to isolate these frictions.

We start by controlling for bilateral shipping costs, which are included in our eBay dataset. Somewhat surprisingly, we find that adding shipping costs barely affects the distance coefficient. As seen in Figure 4, this is because shipping costs are uncorrelated with distance. Furthermore, ad-valorem shipping costs seem higher online than offline, probably as there are less bulk-shipping scale economies for online shipments. It is thus highly unlikely that a reduction in shipping costs is driving the online death of distance.

To explore the trust and information channels we interact the distance coefficient with indicators of corruption and information frictions at the country level. We find that the distance-effect reduction is largest for exporting countries with high levels of corruption and which are relatively unknown to consumers, as measured by Google search results. This suggests that online markets reduce the distance effect by providing both trust and information.<sup>3</sup> We obtain the same result when we interact distance with indices of information intensity at the product level, namely Broda and Weinstein’s (2006) trade elasticities as well as indices we build using the WIPO Global Brand Database and eBay’s trademark-infringement alert system. We also test whether the distance effect is reduced by the eBay seller-rating mechanism, which increases importer trust in exporters. As predicted, we find that distance matters significantly less for sellers with higher ratings. This confirms that distance captures trust and information frictions which are reduced by technology.

As highlighted earlier, different demographics could also be driving the different distance effects. To control for these differences, all our specifications include importer and exporter fixed effects that are specific to online and offline flows. Yet it could be argued that country characteristics that drive the selection
start exporting apparel to the EU also allowed them to export to other markets as firms overcame fixed costs of production and exporting. The strict ROO requirements of EU’s EBA and its potential encouragement of greater local value added may have helped explain those benefits. 14 The US database is a companion to a comparable EU database that we mention later in this section. More details and the databases are available at: https://datacatalog.worldbank.org/node/144174/revisions/365082/view. 15 We compute ad-valorem equivalents for duty variables expressed as specific tariffs or combined tariffs (with an ad-valorem and a specific component) by dividing the specific tariff (or specific component) by the import unit price, itself computed as the median across the unit values of all US imports of a given HS 8-digit product in a given year across partner countries.

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De Neve, J. E., Ward, G., De Keulenaer, F., Van Landeghem, B., Kavetsos, G., & Norton, M. I. (2018). The asymmetric experience of positive and negative economic growth: Global evidence using subjective well-being data. _Review of Economics and Statistics_ , 100(2), 362-375.

Deaton, A. (2008). Income, health and well-being around the world: Evidence from the Gallup World Poll. _Journal of Economic Perspectives_ , 22, 53–72.

Diego-Rosell, P., Tortora, R., & Bird, J. (2018). International determinants of subjective wellbeing: living in a subjectively material world. _Journal of Happiness Studies_ , 19(1), 123-143.

Diener, E., Lucas, R.E., Schimmack, U., & Helliwell, J.F. (2009). _Well-being for Public Policy_ . Oxford University Press, USA.

Diener, E., Tay, L., & Oishi, S. (2013). Rising income and the subjective well-being of nations. _Journal of Personality and Social Psychology_ , 104(2), 267.

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Easterlin, R. A. (1974). Does economic growth improve the human lot? Some empirical evidence. In _Nations and Households in Economic Growth_ (pp. 89-125). Academic Press.

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Easterlin, R. A
deeper information on truck driving patterns. A sample of trucks is tracked over the course of several days, and the loaded or empty status tracked over that period. Finally, digitization of the trucking economy has generated new datasets of mobile app transactions, as studied in Heilmann (2020). App data has had limited penetration into the economics and transportation literature, but holds potential for future work which takes advantage of detailed histories of trips. However, app data also depends on the market share of the particular application or marketplace, and may generalize poorly to the rest of the market.

Most surveys aggregate over all segments of the trucking freight market within a country, but there are exceptions. Studies may distinguish between short haul and long haul trips, as in Osborne et al. (2014). Beilock and Kilmer (1986) present figures for specific trailer types: refrigerated (reefer) and specialized trailers. Lam et al. (2019) separately measure shares of empty miles by firm organization, finding that logistics services providers have lower rates of empty trips than (owner) operators. Finally, a few studies explicitly cover international trips, including Nathan Associates Inc. (2013)’s study of corridors in West and 5
variables that may explain these outcomes. Administrative-level information and data (such as advertising strategy and resources and the level and distribution of program budgets) that can be mapped to household-level data may be extremely valuable for identifying policy levers that can improve participation outcomes without adverse effects.

# **REFERENCES**

Ashenfelter, O. 1983. “Determining Participation in Income-Tested Social Programs.” _Journal of the American Statistical Association_ 78 (383): 517-25.

Blundell, R., V. Fry, and I. Walker. 1988. “Modeling the Take-Up of Means-Tested Benefits: The Case of Housing Benefit in the United Kingdom.” _Economic Journal_ 98 (390): 58-74.

Coady, D. and S.W. Parker. 2009. “Targeting Performance under Self-selection and Administrative Targeting Methods.” _Economic Development and Cultural Change_ 57 (3): 559-87.

Currie, J. 2006. “The Take-Up of Social Benefits.” In _Public Policy and the Income Distribution_ , ed. A. Auerbach, D. Card, and J. Quigley, 80-148. New York: Russell Sage.

de Janvry, A., F. Finan, E. Sadoulet, and R. Vakis. 2006. “Can Conditional Cash Transfer Programs Serve as Safety Nets in Keeping Children at School and from Working when Exposed to Shocks?” _Journal of Development Economics_ 79 (2): 349-73.

Dubois, P., A. de Janvry, and E. Sadoulet. 2011. “The Effects on School Enrollment and Performance of a Conditional Cash Transfer Program in Mexico.” CEPR Discussion Paper No. 6069.

Duclos, J.-Y. 1995. “Modeling the Take-up of State Support.” _Journal of Public Economics_ 58 (3): 391415.

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stoves indicated that the Mirt stoves were used at least twice per week (i.e. regularly used) during the first monitoring period.

Figure 1. Stove Use Monitors (SUMs) Source: Abebe D. Beyene Using signal processing and analysis (O’Haver 1997), we measured frequency of stove use for a monitoring period by simply counting the number of times the stove surface temperature exceeded a critical value. The average stove use in a period is calculated as the number of cooking events/total days temperature was measured. Because injera is not baked daily, we express this value per week and define regular use of the stove as an average use of at least twice per week.

Based on readings from the SUMs, we identify a cooking event as having taken place if the recorded temperature exceeded 40 degrees Celsius. We chose 40 degrees as our cutoff, because it is about 14% greater than the maximum in-home ambient temperature measured using thermometers, which was 35 degrees Celsius. We therefore reduce the possibility of counting normal temperature variations as cooking events by using the 40-degree Celsius cutoff. We also conduct robustness checks with 45 and 60-degree Celsius cutoff points and find that the results do not change (see Appendix B).

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within the villages increased or decreased compared to five years ago. Information was also collected on whether the time to collect firewood from forests increased or decreased compared to five years ago. If forests were depleted faster than they were replenished through afforestation or regrowth, then given the reliance of these rural households on firewood as a primary source of energy, it is imaginable that the time to collect firewood would increase. In order to test the effect of remittances on forests that we found through our satellite data, we run a probit model with a binary variable indicating the increase and decrease of forest area as well as time to collect firewood. We restrict attention to responses in 2004 and run these specifications using 2004 levels as well as the change from 1995 to 2004 in the continuous independent variables. The marginal effects are reported in Table 8.

Controlling for the change in population, rainfall, road density and the presence or absence of community forestry user groups in the villages, we find that the remittances have a significant negative effect on the probability of forested area declining in villages. Similarly, point estimates also seem to indicate that remittances are positively associated with forest area increasing and time to collect firewood decreasing compared to five years ago. This gives us additional corroborative evidence on our earlier finding that rural economic growth caused by remittances may have ended up having a positive effect on forests.

An additional dimension that needs to be addressed given the time period we are looking at in Nepal is the possible biases due to conflict. Nepal experienced a Maoist insurgency that escalated into a low-level civil war between 1996 and 2004. A total of 13,000 people lost their lives and many more were displaced. To the extent that the intensity of this conflict was correlated with migration rates, the attendant inflow of remittances and forest area, failure to account for this may bias our results. In particular, we may be attributing to remittances what could actually be driven by conflict. There have been numerous efforts to study the proximate causes of conflict in Nepal
because the data on FDI inflows are still missing for a few countries.

8 Unfortunately, the Competitiveness Indicator developed by the World Economic Forum is not available for most of the countries covered in this note. However, Namibia and Mauritius were also well ranked in their 1998 ranking, but South Africa was perceived as much more competitive, while Mozambique much less than reported in this note.

9 For example, Namibia has been traditionally perceived as a secure country, with satisfactory macroeconomic indicators, a good and reliable judiciary system and access to the large South African market. Similarly, the weak performance of Sierra Leone and Congo has been well publicized with their unstable political climate and multiple economic problems.

10 In fact, the coefficient correlation between our indicator and the ICRG and II indexes is negative for the period 1996-1997 (see more details in the next section).

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positive impacts from limiting or shortening employment bans for asylum seekers (Marbach et al., 2018; Slotwinski et al., 2019) and reducing wait time for recognition of refugee status (Hainmueller et al., 2016; Hvidtfeldt et al., 2018), as well as a positive correlation between acquiring citizenship and better economic outcomes (Bakker et al., 2014; Bevelander and Pendakur, 2006; Govind, 2021). At the same time, results for policies to disperse populations of refugees geographically within high-income countries consistently show that not allowing refugees freedom of movement and settlement has negative impacts on their economic outcomes (Aksoy et al.,

> 5The results are from the Developing World Refugee and Asylum Policy (DWRAP) data set, which covers de jure policies in 92 low- and middle-income countries from 1952 to 2017

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, Russell Sage Foundation, New York, 132-189.

- Stifel, D., and H. Alderman. 2003. “The "Glass of Milk" Subsidy Program and Malnutrition in Peru.” Policy Research Working Paper No. 3089. World Bank, Washington, D.C.

- Strauss, J., and D. Thomas. 1998. “Health, Nutrition, and Economic Development.” _Journal of Economic Literature_ 36: 436-82.

- Taylor, B., E. Dearing and K. McCartney. 2004. “Incomes and Outcomes in Early Childhood.” _Journal of Human Resources_ 39(4):980-1007.

- Todd, P.E., and K.I. Wolpin. 2003. “On the Specification and Estimation of the Production Function for Cognitive Achievement.” _The Economic Journal_ 113: F3-F33.

- Umbel, V.M., B.Z. Pearson, M.C. Fernandez, and D.K. Oller. 1992. “Measuring bilingual children's receptive vocabularies.” _Child Development_ 63(4): 1012-20.

- Victora, C., F. Barros, and J. Vaughan. 1989. _Epidemiologia da Desigualdade_ , Sao Paulo: Hucitec.

- Waldfogel, J., W. Han, and J. Brooks-Gunn. 2002. “The Effects of Early Maternal Employment on Child Cognitive Development.” _Demography_ 30(2): 369-92.

- Walker SP, S.M. Grantham-Mcgregor, C.A. Powell, S.M. Chang. 2000. “Effects of Growth Restriction in Early Childhood on Growth, IQ, and Cognition at Age 11 to 12 Years and the Benefits of Nutritional Supplementation and Psychosocial Stimulation.” _The Journal of Pediatrics_ 137(1): 36-41.

- World Bank. 2005a. _World Development Report 2006: Equity and Development_ . The World Bank and Oxford University Press, New York.

- World Bank. 2005b. Development Database Platform. Washington, D.C.. Available
Similarly, to investigate financial constraints, we need to know not only whether some types of firms (by ownership, size, or the owner's gender) had more difficulty obtaining external finance, but also the consequences of the lack of funds. Estimating production functions would allow us to learn whether, holding other characteristics constant, financially-constrained firms are indeed less productive. If such firns are not less productive, then there is less need to worry about financial constraints.

**2. Importance of panel data:** Most Bank surveys are cross-sectional, leaving the need for panel data unfulfilled (again, with some exceptions, notably, RPED and ICS). As a consequence, we have much less confidence in inferring whether productivity differences reflect unmeasured firm heterogeneity or the variable of interest.

3. **Need to address endogeneity:** Surveys can be most effective when they deal explicitly with the endogeneity of policy interventions. Many researchers find that _ex post_ it is difficult to demonstrate any causal effects because they lack of good instrumental variablesthose variables uncorrelated with the error term but reasonably good predictors of the endogenous variables. It will likely be too late to derive instrumental variables at the data analysis stage. Instead, at the survey design stage researchers should consider the endogenous policy variables of interest and think about potential instruments to identify those variables.

# III. IDEAL DATA VS. EXISTING DATA

In each of the following subsections we give a brief overview of some questions that have yet to be empirically investigated in the context of developing countries, the data that would allow hypothesis testing, and an evaluation of whether existing World Bank survey have addressed these issues. In particular, we discuss research topics such as the firm itself, market structure, the business environment and its impact on firmn's performance, the role of the state, and analysis of the macro-economy through microeconomic data.

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. The income variable is however characterized by strong measurement errors as it is based on large income bands. Moreover, only 1 percent of the Peruvian population earns above U$100 a day, which questions the representativeness of the population sample they use to estimate the values of the upper class.

Tóth and Keller (2011) analyze how the redistributive preferences of middle classes relate to actual income and to its distribution, in contexts with different levels of income inequality. Using principal component analysis, they construct a composite index of redistribution preference based on the desirability of redistribution and state involvement in providing jobs, education and social expenditures. The analysis suggests that redistributive preferences of those in the middle are higher if they live in a society where many people feel poor and only a few feel rich. In a related paper, in line with individuals’ attitudes towards redistribution, Reed-Arthurs and Sherin (2010) employ data on taxation and fairness to find that Americans’ demand for redistribution to the poor is influenced 5
During the last decades, international capital flows have grown significantly, playing an increasingly important role in the economies of both developed and developing countries alike. Many studies have analyzed the behavior of international capital flows. For instance, it is well known that investors have not been nearly as internationally diversified as their consumption and income path would imply. This literature has highlighted some factors that can explain the patterns of international cross-country investments and this lack of international diversification in particular – for example, the existence of explicit costs to international investment and the presence of implicit ones, like political or country risks and informational asymmetries.<sup>1</sup> Although somewhat successful in describing some features of international portfolio investments, only a few of these theories have been able to address the firm-level aspect of international portfolio choices. For example, there is evidence that information asymmetries play an important role in explaining differences in portfolio holdings between domestic and foreign investors within a given country.<sup>2</sup> In particular, widely documented is a preference among foreign investors for large firms if compared to the preferences of domestic investors, a pattern also known as the foreign investor bias. Kang and Stulz (1997) document these preference patterns in Japan and the results are interpreted as an indication that information asymmetries are an important explanation for this bias as a model in which foreigners know less about small firms would be able to fit their investment decisions. Furthermore, Dahlquist and Robertsson (2001)

> 1 Most arguments can be classified into two categories: deviations from the international CAPM affecting foreign asset holdings, and foreign investors facing direct or indirect barriers in foreign investments. See for example Lewis (1999) and Karolyi and Stulz (2003) for surveys of the home bias literature.

> 2 See for example, Brenan and Cao (1997), Edison and Warnock (2004), Aggarwal, Klapper, and Wysocki (2005), Bae, Stulz, and Tan (2008), Leuz, Lins and Warnock (2009
# **1.  Introduction**

Air pollution from urban traffic is a major source of health damage in developing countries. In a global study, Dora et al. (2011) find that urban road transport often accounts for more than 50% of dangerous air pollutants (e.g., fine particulates, nitrogen oxides, carbon monoxide, ozone, sulfur dioxide). The World Health Organization (2016) attributes about 3.3 million annual premature deaths to outdoor air pollution in low- and middle-income countries: 72% from heart disease and strokes, 14% from chronic obstructive pulmonary disease, and 14% from lung cancer. The World Bank (2016) estimates welfare losses from ambient fine particulate and ozone pollution at 4.8% of GDP equivalent in East Asia, 3.5% in South Asia, 2.1% in the Middle East and North Africa, 1.6% in Latin America and 1.5% in Sub-Saharan Africa.

Urban traffic and pollution problems are essentially geographic, because their incidence and impacts depend on the spatial distributions of economic activities, households, and transport links. Although urban planners, researchers and policy makers are sensitive to this spatial component, incomplete information has hindered their attempts to identify and implement cost-effective strategies. Local traffic and pollution are often poorly measured because monitoring systems are sparse, costly and difficult to maintain. Since this problem is pervasive in developing countries, rigorous empirical analyses of alternative strategies are rare. As a consequence, urban planners and policy makers have little guidance on appropriate spatial measurement techniques, spatiallyoriented methodologies for benefit-cost assessment of policy alternatives, and transferable empirical models from other urban areas. In practice, they have frequently been forced to estimate city-wide traffic volume from observations on a few traffic arteries, and air pollution from aggregate fuel consumption data, coupled with air pollutant emission factors for gasoline and diesel vehicles that may have been generated from small samples in other places. As a result, educated guesswork has often guided the placement of subway