rafmacalaba/data-use-mentions-extended huggingface dataset:
Latin America, 43% in China and 57% in India. These estimates are from a variety of sources and are of varying quality, but the bottom line is that the top of the distribution receive a very large share of total income. The next section discusses the challenges of accurately capturing the top tail using survey and administrative data.

# Challenges to Accurately Capturing the Top Tail in Household Surveys

In this section we document the challenges faced by researchers wanting to use household surveys to measure the top tail of the income distribution. Following this, we focus on challenges when using other types of data, mostly administrative data such as tax or social security records or house prices. Administrative data have been used precisely because of the challenges encountered when using household surveys. However, these types of administrative data are very rare in low-income countries and are still unusual in middle income countries. Thus, household surveys are still very important, which is why we spend some time documenting the challenges associated with using them. In doing so we follow recent papers by Lustig (2020) and Ravallion (2022), although we complement these papers in a variety of ways.

4
- Lokshin, Michael, Kathleen Mullan Harris, and Barry M. Popkin. (2000). "Single mothers in Russia: household strategies for coping with poverty." _World Development,_ 28(12): 21832198.

- Long, J. Scott. (1997). _Regression Models for Categorical and Limited Dependent Variables_ . Thousand Oaks, California: SAGE Publications, Inc.

- Lukiyanova, Anna and Aleksey Oshchepkov. (2012). “Income mobility in Russia (2000–2005)”. _Economic Systems_ 36: 46–64.

- Milanovic, Branko. (1998). _Income, Inequality and Poverty during the Transition from Planned to Market Economy_ , Washington DC: The World Bank.

- Mu, Ren. (2006). "Income Shocks, Consumption, Wealth, and Human Capital: Evidence from Russia," _Economic Development and Cultural Change,_ 54(4): 857-892.

- Novokmet, Filip, Thomas Piketty and Gabriel Zucman. (2018). “From Soviets to Oligarchs: Inequality and Property in Russia 1905-2016”. _Journal of Economic Inequality_ , 16(2): 189223.

OECD. (2002). Economic Surveys- Russian Federation 2002.

- ---. (2009) “ _What are equivalence scales_ ?” Retrieved on July 1, 2018 at www.oecd.org/dataoecd/61/52/35411111.pdf

- ---. (2018). _A Broken Social Elevator? How to Promote Social Mobility._ Paris: OECD Publishing. <u>https://doi.org/10.1787/9789264301085-en.</u>

- Piketty, Thomas, Emmanuel Saez, and Gabriel Zucman. (2018). “Distributional National Accounts: Methods and Estimates for the United States”. _Quarterly Journal of Economics,_ 133(2), 553–609.

- Powell, David. (2017). "Quantile treatment effects in the presence of covariates." _RAND Labor and Population Working
_5_ any level below 1000 parts per million, a near-quadrupling of the pre-industrial level.<sup>9</sup> Thus, without intervention for abatement, the carbon emissions driving climate change will continue rising so that the severity of climatic, ecological, and social impacts will surpass those generally discussed in the context of a doubling of CO2 concentrations. This confirms the necessity of enacting a policy of reducing emissions from their predicted trajectories if climate change is to be mitigated.

Although the level of global emissions is likely to rise over time, the inequality in per capita emissions is likely to recede. The model includes two factors that drive emissions inequality downward. First, the decreasing MPE as income increases implied by the negative coefficient of _yjl_<sup>_2_</sup> in (1) tends to reduce emissions of very high income countries. Second the income convergence implied by the GDP projections enables low income countries to grow faster and progressively gain ground on higher income countries. How quickly does emissions convergence proceed? Figure 2 provides the answer. While business-as-usual per capita mean emissions rise steadily, inequality in per capita emissions as measured by the Gini index decreases slowly, albeit faster than income inequality because of the diminishing MPE.

# _2.3. Emissions by groups of countries_

Section 3 uses a group decomposition of the Gini index to analyze the contribution of groups of countries to emissions inequality. In order to understand the forces underlying future inequality trends, it is important to analyze the baseline distribution at the start of the period.

Table 1 summarizes the state of affairs by income group in the first year (1993) and highlights the prevailing disparities in carbon emissions between groups of nations.'" Although more than threequarters of the world's population reside in the two lower income groups, these countries produce less than one-third of gross world product and a slightly smaller share of global carbon emissions. On a per 9Cumulative emissions and corresponding atmospheric concentrations are provided by IPCC (1996).

10Income groups correspond to those
externalities and informational asymmetries that prevent credit reallocation leading to self‐fulfilling equilibria (Bebchuk and Goldstein (2009), Dagher et al, 2016). Banks’ lending practices are influenced by their expectations of overall banking behavior. For example, if large banks expect overall lending to be lower because of deteriorated financial conditions in

> 61 In Spain, for example, overall credit was still declining (IMF, 2015c) in 2014, but the pace has slowed and new credit is being extended outside real estate and construction.

> 62 Didier et al (2015), show that in the median country, only about 20 listed firms per year issue securities in either their domestic capital market or in an international financial center. Moreover, the median equity issuing firm is twice as large as the median non‐equity issuing firm and the median bond issuing firm is more than 36 times as large as the median non issuing firm. Of the few debt and equity issuers, the top 5 firms receive 66% of the funds raised through bonds and over 77% of the funds raised through equity.

> 63 For example, SMEs in Spain (for loans below 1 million euros) since end 2013.

> 64 IMF (2015g).

> 65 The NPL data are reported on a consolidated basis for banking groups and include the value of collateral; the NPE data use locational data on impaired loan exposures and are weighted by total assets.

> 66 This is also true for some other EU countries, but the ratio in Greece is a multiple of these other countries’ ratios.

> 67 9 more banks were assessed at end‐2015.

29
Guam, Isle of Man, Kiribati, Liechtenstein, Marshall Islands, Micronesia, Fed. Sts., Monaco, Nauru, Northern Mariana Islands, Palau, San Marino, Seychelles, Sint Maarten (Dutch part), St. Kitts and Nevis, St. Lucia, St. Martin (French part), St. Vincent and the Grenadines, Tonga, Turks and Caicos Islands, Tuvalu, Virgin Islands (U.S.). Encarnacion et al (2022) note that the poorest performers in terms of lowest SDG-gender indicators are small islands and nations. These countries have, on average, very low reporting rates for SDGs, including, but not only, those related to gender. In general, small islands and nations are under-performers in terms of statistical performance conditional on their income and human captial index level (Dang et al 2021).

> 4 The SDG database includes actual (survey/census or other primary data source estimates) as well as additional modeled estimates for indicators when primary sources are not available for the country. We do not use modeled estimates.

5 This is our own calculation. Dang and Serajuddin (2020) report lower rates of SDG indicator reporting in part because they focus on an earlier period (2012-2016) and because they include small islands and nations.

5
2 to document three stylized facts on the impacts of the ban. To assess whether the ban was binding, we examine its impact on fertilizer imports, combining high-frequency granular customs data covering the universe of import transactions in Sri Lanka with import ban data hand-digitized from regulatory gazettes. Since the ban was ultimately reversed amid widespread social unrest, we employ the dynamic difference-in-differences (DID) estimator of De Chaisemartin and d’Haultfoeuille (2022) that corrects for bias when treatment is reversed within the study period. Our first stylized fact is a near-total collapse in fertilizer imports – driven primarily by quantity reductions – with volumes falling by more than 99% within four months of the ban and remaining at similarly depressed levels thereafter.<sup>6</sup> We then examine the ban’s impacts on agricultural production using government production data, with a focus on Sri Lanka’s primary food crop—rice—and its major export crop—tea— both of which are highly fertilizer-intensive. Our second stylized fact documents a reduction in rice yields of more than 30% between 2021 and 2022.<sup>7</sup> After adjusting for weather abnormalities, we estimate yield declines of 24.4% for rice and 28.7% for tea in 2022 relative to historical norms.<sup>8</sup> To capture the ban’s impact on downstream agricultural exports, we construct a firm-level exposure metric linking customs data on agricultural firms’ pre-ban crop export portfolios with agronomic data on crop-specific fertilizer requirements. We then employ the dynamic DID estimator of De Chaisemartin and d’Haultfoeuille (2022) to compare the export performance of highly exposed firms to less-exposed firms over time. Our third stylized fact is a relative decline of 35% in the exports of exposed firms within four quarters after the ban, even after accounting for seasonality. These stylized facts are robust to potential confounders arising from the COVID-19 shock and the broader economic crisis that unfolded in Sri Lanka.

While these stylized facts demonstrate the
Trade and Development.

Portugal-Perez, Alberto and Wilson, John S., 2009. "Why trade facilitation matters to Africa," World Trade Review, Cambridge University Press, vol. 8(03), pages 379-416, July.

- Rodrik, Dani. 1997. “Trade Policy and Economic Performance in Sub-Saharan Africa.” National Bureau of Economic Research Working paper no. 6562. Cambridge, M.A.

- Santos Silva, J. M. C. and Silvana Tenreyro. 2006. "The Log of Gravity," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 641-658, 09.

- Subramanian, Arvind, and Natalia T. Tamirisa. 2003. “Is Africa Integrated in the Global Economy?” _IMF Staff Papers_ 50 (3): 352–72.

- Teravaninthorn, S and G Raballand, 2008, Transport Prices and Costs in Africa: A Review of the Main International Corridors, The World Bank, Washington.

- Wang, Z. and Winters, L. A. 1998. “Africa‟s role in multilateral trade negotiations: Past and Future,” Journal of African Economies 7 (Supplement 1): 1-33.

- Wilson, John S., Catherine Mann, and Tsunehiro Otsuki. 2005. “Assessing the Benefits of Trade Facilitation: A Global Perspective.” _World Economy_ 28 (6): 841–71.

- World Economic Forum (2009). Global Enabling Trade Report 2009. Geneva: The World Economic Forum.

World Trade Organisation (2009) World Tariff Profiles 2009, World Trade Organisation, Geneva.

22
(see Porto, 2003, 2005a or 2005b or Nicita 2004a for a similar approach).

The interaction terms that are used in both the selection and wage equations imply that the elasticities of wage and labor market participation with respect to sugar prices vary from one individual to another, according to her level of education, her geographic location, the sector where she works and the type of employment. This proves to be important in estimating the impact of changes in sugar prices on household income at different points of the income distribution.

The Heckman selection model is estimated using maximum likelihood. The estimates from this model allow us not only to calculate how wages would change following a change in the price of sugar, but also to measure the impact that changes in sugar prices may have on the labor market participation of each individual. Thus, we can estimate the impact of changes in local sugar prices on both wages and employment.

In order to correct for possible correlation of the error terms within survey sample strata in each year, we use cluster robust error terms, where the clusters are defined by sample strata/year. All regressions include year and state dummies.

# **3.3 Simulating the impact on household’s labor income**

The third step consists of simulating the impact of a 10 percent increase of sugar world prices on households’ labor income. For this task we use the 14
- Gibbard, A. (1973), ‘Manipulation of voting schemes: a general result’, Econometrica 41, 587– 601.

- Gyourko, J. (2009), ‘Housing supply’, Annu. Rev. Econ. 1(1), 295–318.

- Hanemann, W. M. (1984), ‘Welfare evaluations in contingent valuation experiments with discrete responses’, American Journal of Agricultural Economics 66(3), 332–341.

- Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T. et al. (2013), ‘High-resolution global maps of 21st-century forest cover change’, Science 342(6160), 850–853.

- Hellerstein, D. M. (2017), ‘The US Conservation Reserve Program: The evolution of an enrollment mechanism’, Land Use Policy 63, 601–610.

- Jack, B. K. (2013), ‘Private information and the allocation of land use subsidies in Malawi’, American Economic Journal. Applied Economics 5(3), 113.

- Jack, B. K., Kousky, C. & Sims, K. R. (2008), ‘Designing payments for ecosystem services: Lessons from previous experience with incentive-based mechanisms’, Proceedings of the National Academy of Sciences 105(28), 9465–9470.

- Jayachandran, S., de Laat, J., Lambin, E. F., Stanton, C. Y., Audy, R. & Thomas, N. E. (2017), ‘Cash for carbon: A randomized trial of payments for ecosystem services to reduce deforestation’, Science 357(6348), 267–273.

- Jindal, R., Kerr, J. M.
Table 5 reports the regression results for equation 3, separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services.” An increase in the size of employment in an average firm in heavy manufacturing if it is located in a mining district in the south (compared to firms in other districts) is higher by 23.2 percent, followed by 13.7 percent higher in “other industry,” 12.1 percent higher in “other services” and 6.2 percent higher in WRT services. The result is statistically significant only for heavy manufacturing and “other services.” Finally, we extend our model in equation 3 to include the effect of changes in the mining employment shock over time, as follows: Equation 4 estimates the difference in average employment of a firm in the manufacturing sector if it is located in a mining district in the south compared to other manufacturing firms between 2003 and 2013. Since we use both 2003 NIC and 2014 IBES data, to estimate δ _DDD_ , the sample is restricted to only manufacturing firms. _Xi_ , _t_ in equation 4 controls for various firm-level characteristics other than employment.

Table 6 reports the results of the regression results for equation 4 separately for light and heavy manufacturing. We do not find a significant difference in employment in light manufacturing firms that are located in mining districts in the south and light manufacturing firms located in other districts between 2003 and 2013. Among light manufacturing industries, average firm employment in the food industry grew by almost 26 percent in mining districts in the south over this period compared to those in other districts. Meanwhile, average firm employment in the paper industry fell by 22 percent in mining districts in the south compared to those in other districts during the same period.

Average employment size in heavy manufacturing firms in mining districts in the south grew by 31 percent compared to firms in other districts between 2003 and 2013. The firm-level evidence suggests that firm growth in heavy manufacturing over this period is primarily driven
Freedom of the World of the Fraser Institute and the indices of Civil Rights and Political Liberties published by the Freedom House (Annex 1). These are among the few available databases that cover those concepts for a large sample of countries and a relatively long period of time in a comprehensive and consistent way.

The index of <u>economic freedom</u> _(EF)_ used in this paper is the simple average of four of the five areas of the Fraser Institute’s Economic Freedom of the World (EFW), namely the legal structure and security of property rights, access to sound money, the freedom to trade internationally, and the regulation of credit, labor and business.<sup>15</sup> In turn each area consists in a number of sub-indicators (Annex 1). The legal structure and security of property rights measures the degree of judicial independence, impartial courts, protection of property rights, military inference in rule of law and the political process, integrity of the legal system, legal enforcement of contracts, and regulatory restrictions on the sale of real property. Access to sound money measures the money growth, standard deviation of inflation, rate of inflation, and freedom to own foreign currency bank accounts. Freedom to trade internationally measures the taxes on international trade, regulatory trade barriers, the size of the trade sector relative to expected,

> 15 <u>http://www.freetheworld.com/</u>

11
**|||||
|Female|20.4|**19.1**|**19.9**|**18.8**|
|Male|**13.7**|14.9|**_15.5_**|**_15.8_**|
|**Age65**|||||
|Female|17.4|**15.6**|**16.0**|**15.3**|
|Male|**11.5**|12.2|**12.5**|**13.3**|

_Source:_ World Bank data and Kyrgyz Republic estimates.

**15**
_Figure 6. Low-income States: Licensing and Royalties Balance of Payments, 1980-2009_ Low-income states: licensing & royalties balance of payments, 1980-2009 <!-- Start of picture text --> $1<br>($1)<br>Morocco<br>Bolivia<br>($3)<br>Kenya<br>Philippines<br>($5)<br>Indonesia<br>($7) India<br>China<br>($9)<br>($11)<br>Billions<br><!-- End of picture text --> Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files (Source: International Monetary Fund, Balance of Payments Statistics Yearbook and data files) Data from our sample suggests that important gaps in IP commercialization persist. This is the case both in terms of the volume of licensing and royalty revenues and the direction of cash flows associated with innovation. Developing countries seem to be significantly marginalized in this aspect of the international system. Analysis of the entire World Bank dataset for all countries and income categories in 2009 (including those outside our sample) confirms this hypothesis (Table 2).<sup>6</sup>

> 6 A variety of factors may impact these differences in IP commercialization. The level of FDI, the technological skill base within the country, education, inflation and exchange rates are only a few. Time and resource constraints meant that these factors could not be isolated and analyzed independently. This is also not necessary for the purposes of this paper, which restricts its focus to the various IP monetization mechanisms that can be used to alleviate this gap. However, this is an important topic for future research. The IMF and World Bank datasets present a time series of patent information and licensing fees that could form the basis for more detailed econometric analysis. Panel estimation methods could be used to isolate the different country characteristics that might impact divergence between
and Bangladesh. The figure shows that in these other countries, registration also typically occurs over the size range considered in our study – not with the smallest microenterprises.

The Bangladesh survey asked firm owners what they saw as the main advantages of being formal. Owners give responses which are similar to those in the Sri Lanka survey: links to bank financing, better reputation for the business, a lower chance of being fined, and the ability to operate visibly at a large scale without fear of being caught. Smaller and more informal firms in Bangladesh are more likely to say they see no potential benefits and all firms say the main disadvantages were paying taxes and having to deal with the cost and process of registering (McKenzie, 2010). These same channels also appear in discussions of the costs and benefits of formalizing in different Latin American countries (Perry et al, 2007; World Bank, 2009).

As a result, it seems reasonable to believe that our results are informative outside Sri Lanka about the number of firms at the margin that will be induced to formalize by relatively small changes in the costs and benefits, and also about the characteristics of those firms. Our results suggest that taking the costs of registering from the levels in Sri Lanka to zero induces few firms to formalize. However, increasing the benefits further (in our case by paying firms) induces more firms to formalize. This is consistent with recent cross-country panel data, in which Klapper and Love (2010) find that only changes in business environment reforms which involve more than a 40 percent reduction in costs are associated with changes in firm entry.<sup>11</sup>

# **7. Conclusions**

Prior to the intervention, owners of unregistered firms were either ignorant of, or vastly overestimated the costs of registration. We might therefore have expected that simply informing firms about the costs of registration would be sufficient to induce registration. But we find that information and household-based nationally representative labor survey. The Bangladesh survey data come
Table 6.7. Poverty rate transition matrix estimates by the unweighted, current UNPS, and calibrated GWSM base estimators

||Unweighted|2009/15<br>UNPS|<br>Calibrated<br>GWSM base|2<br>Unweighte<br>d|013/15<br>UNPS|Calibrate<br>d GWSM<br>base|
|---|---|---|---|---|---|---|
|In non-poor HH in 2009|||||||
|(or 2013) and 2015|63.1|64.8|63.6|65.0|66.5|65.4|
|In non-poor HH in 2009|||||||
|(or 2013) and poor HH<br>2015|12.3|12.1|13.0|8.3|8.1|8.3|
|In poor HH in 2009 (or<br>2013) and poor HH<br>2015|9.6|9.0|8.4|12.2|11.2|11.7|
|In poor HH in 2009 (or|||||||
|2013) and non-poor HH<br>2015|15.0|14.2|15.1|14.4|14.1|14.5|

Table 6.8 shows the transition matrix for the area of residence, for which the 2009/2015 panel estimates are greater than 2013/2015 estimates. The overestimations of poor people suggest that non-poor individuals, especially in rural areas, are more dynamic and tend to move out of the UNPS sample more frequently than poor individuals. In the 20013/2015 panels, figures stabilize. All statistics are consistent with the national-level estimates from the UNPS 2015 cross-sectional sample. The refresh of the
</mark> _<mark>Review of Environmental Economics and Policy</mark>_ <mark>,</mark> _<mark>12</mark>_ <mark>(1), 4-25.</mark> **<mark>[</mark> 3]** United Nations Framework Convention on Climate Change. 2012. Slow onset events. Technical Paper FCCC/TP/2012/7. https://unfccc.int/resource/docs/2012/tp/07.pdf **<u>[3]</u>** <mark>Viswanathan, B., & Kumar, K. K. (2015). Weather, agriculture and rural migration: evidence from state and district level migration in India.</mark> _<mark>Environment and Development Economics</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(4), 469.</mark> **<mark>[</mark> 1]** <mark>Wesolowski, A., Buckee, C. O., Pindolia, D. K., Eagle, N., Smith, D. L., Garcia, A. J., & Tatem, A. J. (2013). The use of census migration data to approximate human movement patterns across temporal scales.</mark> _<mark>PloS one</mark>_ <mark>,</mark> _<mark>8</mark>_ <mark>(1), e52971.</mark> **<mark>[</mark> 2]** <mark>Wodon, Q., Liverani, A., Joseph, G., & Bougnoux, N. (Eds.). (2014).</mark> _<