rafmacalaba/data-use-mentions-extended huggingface dataset:
Wealthy households whose population weight is small but whose economic weight is large tend to dampen significantly the importance of home consumption in the macroeconomic accounts. Since wealthy individuals tend to engage in very little home consumption as a share of total consumption and have large economic weight, their presence drives down the share of home consumption in the macroeconomic data. When home consumption shares are derived using population weights (e.g., what is the share of home consumption for the average household), the share of home consumption grows considerably. At the national level, the average household obtains 45 percent of the value of total consumption from home consumption. The average rural household share remains considerably higher than the urban household share at 58 percent and 16 percent respectively.

The population categorized as poor tends to home consume proportionately somewhat more than the national average. Nevertheless, in terms of share of goods home consumed, households characterized as poor are not all that different from the population average. This not surprising when one considers the fact that the poor represent more than half the population. In addition, a further large fraction of the population consumes at levels above but still near the poverty line. For example, 90% of the population consumes at levels less than twice the poverty line. The tendency to home consume apparently remains relatively constant across these basic levels of income.

# **2.4 Inequality**

James, Arndt, and Simler (2005) conduct a detailed analysis of inequality based on the 2002-03 Household survey for Mozambique. They estimate a national Gini coefficient of 0.42, which represents a fairly high degree of inequality, though not out of 14
**Figure 1. Corporate Income Tax Rates around the World (%)**

<!-- Start of picture text --> 40<br>35 35 34 33.2<br>30<br>30 28 28 27.9 27.2<br>24.2 25 25 25 24.1 23.6<br>25 22 22.5 21.3<br>20 19 19 20 20 20 18.7<br>20 17<br>15 15<br>15<br>10<br>5<br>0<br><!-- End of picture text --> Source: Author’s Calculations based on KPMG.2014. Corporate Tax Rates Table and Bangladesh National Board of Revenue. 2014.

_Notes: The rate for India is for domestic companies; the rate for Bangladesh is for companies not listed on stock exchange_

**Table 1. Corporate Income Tax Rates in South Asia in 2004 and 2014, %**

|Country|2004|2014|% change|
|---|---|---|---|
|Bangladesh|37.5|35|6.7|
|India|35|30|14.3|
|Pakistan|35|34|2.9|
|Sri Lanka|35|28|20|
|Global Average|29.5|23.6|20|

Source: KPMG. 2014. Corporate Tax Rates Table; KPMG Corporate Tax rates Survey 2004; Pomerleau, K, 2014 and India Ministry of Finance 2004

# _<u>Preferential treatment of certain sectors and activities</u>_

All four South Asian countries provide tax incentives to specific economic activities, often without a clear rational. Agricultural income receives special tax treatment throughout the region (Table 2). In Bangladesh, select agricultural activities, such as poultry farming and production of corn and sugar beet,
nutrient availability. While of great relevance for agricultural production, this focused only on one of several elements of a market-aligned land valuation system.

Subsequent regulation put in place ‘normative monetary valuation’ using data of the 1988 Soviet economic evaluation of land.<sup>16</sup> Valuation of agricultural land was based on rental income from grain crop production whereas zoning and capital investments in infrastructure and public utilities were relevant for urban land. Albeit intended as a temporary measure pending the establishment of a market, this valuation, indexed to which is annually updated based on the index of consumer prices, became entrenched, forming the basis for various taxes and fees, including land tax, rent for state and communal lands, state duties, and simplified taxation systems for farmers. Attempts by government to refine this methodology in 2011, 2016, and 2021 did not correct its fundamental flaws.

The discrepancy between normative and market-based valuation leads to economic distortions, violates the principle of tax fairness, and may encourage inefficient land use. As local revenue is not linked to increases in land values, local governments’ ability to benefit from and their incentive to undertake actions such as planning and infrastructure or public service provision to promote economic development, is limited. Ukraine’s local governments also hold a large amount of land as state or communal property and transparent valuation would ensure that lease revenues reflect the true economic value of the land, maximizing public income and discouraging undervaluation or corrupt practices.

Land valuation is also key for valuing land pledged as collateral to compute reserve adequacy ratios for banks and pivotal for the effective functioning of land and credit markets. Not having a way to identify the market value of land also makes it impossible to tax capital gains from land appreciation, an issue that is particularly important in Ukraine where both agricultural and non-agricultural land is likely to appreciate after the war, creating a potential revenue source that could provide an important and incentive-compatible source of funding for reconstruction. Land valuation would also help identify areas where public and private
>PRT<br>LBN IRN<br>CHN DZA<br>IDNLKA TUNEGY JOR<br>LBY<br>BTN MAR<br>VNM INDLAO<br>0 1 2 3<br>log of real PPP GDP per capital relative to the U.S. in 1975<br>2.5<br>2<br>1.5<br>1<br>.5<br>0<br>log of real PPP GDP per capital relative to the U.S. in 2017<br><!-- End of picture text --> Sources: International Monetary Fund, World Economic Outlook database, and authors’ calculations. Note: Data labels use International Organization for Standardization (ISO) country codes. Regions follows World Bank country groups.

> 6 A country is defined as low-income if its per capita GDP is lower than or equal to 10 percent of that of the United States; middle-income if between 10 percent and 50 percent of U.S. GDP, and high-income if above 50 percent.

4
**18**

resident of Grodno should not be expected to pay for maintenance of housing which has been privatized in Minsk, for instance.

72. Objections to user cost recovery for housing and utilities often rest on assumptions that users in transition economics will not be able to afford the price levels entailed, given their current reduced incomes. Such arguments ignore the fact that shortfalls in cost recovery are now being paid for somehow-by taxpayers, including those who do not use these utilities, or by "inflation taxes" arising from printing money to finance government deficits. Cost recovery from users leads to savings which can be applied to reduce these taxes or government deficits.

73. It is, in fact, counterproductive to subsidize aggregate utility use for distributional reasons-betteroff people benefit more from such subsidies since consumption normally rises with income.<sup>43</sup> Belarus appears to be no exception to this rule. The 1995 Household Budget Survey shows that average monthly spending on rent and utilities rose from 57,000 rubels per family member in the lowest quintile of the income distribution to 67,000 rubels in the highest income quintile.44 The Government of Belarus is attempting to deal with income distributional problems arising in payment for housing and utilities in a more appropriate manner than by subsidizing all users. As described in the World Bank's recent Poverty Assessment for Belarus, low-income households receive housing allowances if payments exceed 15 percent of household income.<sup>45</sup> This approach targets assistance to families in greater need and would also seem to be appropriate for dealing with distributional considerations which arise in provision of services other than housing and utilities, such as daycare.

# **_Property and_** _Real_ **_Estate_** _Taxes_

74. Property and real estate taxes are appropriate means for financing municipal services to property, such as street lighting and road maintenance. Indeed. Appendix B indicates that property taxes are the principal form of autonomous finance of local governments in OECD countries. Keen (p
- Browning, M., T.F. Crossley and J. Winter, “The Measurement of Household Consumption Expenditures,” _Annual Review of Economics_ 6, 475–501, 2014. doi:10.1146/annurev- <u>economics-080213-041247</u>

- Bourguignon, F. and C. Morrisson, “Inequality Among World Citizens: 1820-1992,” _American Economic Review_ , 92, 727–744, 2002. doi: 10.1257/00028280260344443

- Browning, M., T.F. Crossley and J. Winter, “The Measurement of Household Consumption Expenditures,” _Annual Review of Economics_ 6, 475–501, 2014. doi:10.1146/annurev- <u>economics-080213-041247</u>

- Buddenberg, D., and W.A. Byrd, _Afghanistan’s Drug Industry: Structure, Functioning, Dynamics, and Implications for Counter-Narcotics Policy_ , United Nations Office on Drugs and Crime and World Bank, New York and Washington, DC, 2006.

- Chandy, L. and B. Seidel, “How Much Do We Really Know About Inequality Within Countries Around the World? Adjusting Gini coefficients for missing top incomes,” Brookings Institution, 2017a. https://www.brookings.edu/opinions/how-much-do-we-really-know- <u>about-inequality-within-countries-around-the-world/</u>

- Chandy, L. and B. Seidel, “Allocating Income from Survey-National Accounts Gap to Top Incomes According to Pareto Distribution,” Brookings Institution, 2017b. <u>https://www.brookings.edu/wp-content/uploads/2017/02/technical-appendix-02-13-17.pdf</u>

- Charmes, J, “The Informal Economy Worldwide: Trends and Characteristics,” _Margin: The Journal of Applied Economic Research_ 6, 103–32, 2012. doi:10.1177/097380101200600202

- Chen, S. and M Ravallion, “The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty,
export and import). The relationship between corruption and country size is less clear, in part due to little research work so far in this area. To ensure that our measures of income and/or country size are not spuriously picking up the effect of corruption on the dependent variable, we control for _Documents*Corruption_ , where _Corruption_ is measured by the “freedom from corruption” sub-index of the Index of Economic Freedom, Heritage Foundation. We use log of the average values of the freedom from corruption sub-index, where the average is taken over 2001-2005.

Next, we control for social, cultural and political factors interacted with _Documents_ . It is argued that one disadvantage of being large is that large countries are also more diverse such as along ethnic lines. The greater diversity makes it more difficult to closely cater to individual preferences over public goods and in reaching consensus over reforms. Independently of country size, studies have shown that greater ethnic fractionalization has a direct adverse effect on various aspects of overall development and the quality of institutions. To ensure that neither our income nor the population measure is picking up the effect of ethnic diversity, we control for the degree of ethnic fractionalization ( _Ethnic_ ) interacted with _Documents_ , where the measure of ethnic fractionalization is taken from a recent study by Alesina et al. (2003). The remaining controls include dummy variables for the largest religious group in the country (Catholic, Muslim, Protestant and the residual category of all other religions) and a measure of the quality of democracy taken from the Polity IV database, average over 2001-2005 ( _Polity_ ). The controls for the main religious group (interacted with _Documents_ ) are in the nature of robustness checks, although theory provides little guidance on how religion correlates with trade facilitation, income or country-size. For _Polity_ , one might suspect it to be higher (better democracy) in the richer countries. Further, reflecting better governance, higher values of _Polity_ may be correlated with 10
# **2 Data and measurement**

This paper uses data on online job postings from Lightcast to capture jobs related to emerging technologies, and matches this at the firm level with data on firm-to-firm linkages from Factset. In this section, we first describe the online job postings dataset, the classification of new technologies, and the matching process with FactSet firms. We then discuss additional data cleaning and complementary sources. Finally, we provide different measures of technology concentration including the Gini coefficient and the coefficient of variation.

## **2.1 Online job postings**

Our primary dataset includes 573 million scraped online job postings from Lightcast (formerly Burning Glass Technologies). Our sample covers 17 advanced economies from 2014 to 2022.<sup>2</sup> We use the job postings data to identify jobs relating to emerging technologies for several reasons. First, it allows us to identify jobs related to specific and granular technologies over time. In the absence of detailed data on the deployment of multiple technologies across firms, regions, and countries, real-time rich text data sources have been increasingly used as a barometer for the diffusion of new technologies, as reflected through their skills demand footprint or extent of discussion in company boardrooms (see for example Acemoglu et al. (2022) and Bloom et al. (2021)). These datasets are particularly useful for studying rapid advances in new technologies that have not yet been clearly defined or captured by statistical agencies using traditional data sources. Moreover, Lightcast provides not only the raw text of each job description, but also additional information about the industry, occupation, and location of each job posting, allowing us to trace diffusion through each of these channels.

The main potential shortcoming of the Lightcast data on online job postings concerns the representativeness of actual vacancies. First and most notably, it only includes jobs posted online and this subset of vacancies may not be fully representative of all vacancies, while vacancies are also a flow measure of labor demand, rather than
between India and Bangladesh has the potential to grow, since trade in similar product lines has grown. This will deepen production networks between the two countries.

The scope for production networks and vertical trade between the two countries can be judged by the intensity of intra-industry trade (IIT) at the disaggregated (6-digit HS) level. IIT occurs when a country simultaneously imports and exports similar types of products within an industry or sector. There are two types of IITs––horizontal and vertical.<sup>20</sup> IIT has been described also as a measure of the degree to which

> 18 In some of the literature, it is observed that the lack of complementarity and high degree of competition in export structures imply daunting prospects for expanding regional trade in South Asia. Refer, for example, Pitigala (2005).

> 19

> The TCI measures the degree to which the export pattern of one country matches the import pattern of another. A high degree of complementarity is assumed to indicate more favourable prospects for a successful trade arrangement. The index lies on the range 0–100, with 100 indicating perfect complementarity. The latest period for Bangladesh trade data in UN Comtrade and other comparable sources was 2007. Before calculating TCI, data coordinates at HS nomenclature H2 were matched for both the countries for both years. For further details on TCI, refer Mikic and Gilbert (2007, p. 72).

> 20 Greenaway _et al_ . (1995).

8
# **Table 1**

# **Primary Sources of Support for China’s Elderly, 2005**

( Percent Reporting as Most Significant Source of Support )

|<br>|Average|Urban<br>Male|Female|Average|Rural<br>Male|Female|
|---|---|---|---|---|---|---|
|Labor Income|13.0|18.4|7.9|37.9|48.5|27.5|
|Pensions|45.4|56.9|34.6|4.6|8.1|1.3|
|_Dibao_|2.4|1.8|2.9|1.3|1.8|0.9|
|Insurance and Subsidy|0.3|0.3|0.2|0.1|0.2|0.0|
|Property Income|0.5|0.5|0.5|0.2|0.2|0.1|
|Family Support|37.0|20.7|52.3|54.1|39.3|68.5|
|Other|1.5|1.4|1.6|1.8|2.0|1.7|

Source: National Bureau of Statistics, 2005 One Percent Population Sample Data, China Statistics Press, 2006.

33
# **2.1 Actual consumption patterns in 2000 and 2016**

# _2.1.1 Consumption calculation_

Calculating average daily consumption quantities using the Bangladesh Households Income and Expenditure Surveys (HIES) in 2000 and 2016 involves calculating total consumption by food groups over a certain reference period and standardized estimation units (i.e., per household, per individual, or per AME).<sup>2</sup> The number of food items as well as food groups vary across different HIES years (i.e., HIES 2000 includes 140 items in 17 groups, whereas HIES 2016 has 150 items in the same 17 groups). To build comparable consumption patterns, therefore, we grouped HIES food items into seven broad food groups established by the FBDG, namely starchy staples, vegetables, fruits, protein foods, dairy, fats and oils, and sugars. The protein foods category was further split into pulses, fish, meat, and eggs, which composes the ten-group version with the other 6 food groups. In addition to regrouping the HIES food items, the quantities (i.e., as per household expenditure data) were all converted using the edible portion amount in Food Composition Tables (Waid et al. 2017) and were in grams. Regarding the estimation units, we converted individuals to their AMEs based on Bangladesh bodyweight and activity level as described in Waid and others’ work, and summed AME up within households (Waid et al. 2017). Dividing food consumption by number of AME and number of days during the reference period yields grams of food consumed per day per AME for each household. Then the result was averaged and weighted across all the households to produce the national average.

# _2.1.2 Nutrient intake_

Nutrient intake calculation is based on the quantities consumed by HIES food items and nutrient values are derived from from the Food Composition Table for Bangladesh (FCT) (Shaheen et al. 2013) and FoodData Central.<sup>3
**Figure 3:  Evolution of sourcing patterns**

<!-- Start of picture text --> 2002 2006 2010 2014 2018<br>domestic sourcing by MNEs Fitted (MNEs)<br>foreign sourcing by small, domestic manufacturers Fitted (small)<br>60<br>40<br>Percentage<br>20<br>0<br><!-- End of picture text --> Note: The circles show the average values for the two GVC measures averaging over the firms in all countries that were surveyed in a given year. The magnitude of the circles show reflect the number of firms surveyed. The fitted line is obtained from a firm-level regression of each GVC measure on year and year-squared, controlling for country-fixed effects.

However, we should be careful not to infer too much from country averages. In Figure 3 we plot the average values for the two GVC measures—domestic sourcing by MNEs and foreign sourcing by small, domestic manufacturers—by year, but now averaged over all countries surveyed in each year. The size of the circles indicate how many firm-level observations were used to calculate each annual average. The fitted lines are calculated by a regression at the firm level on year and year-squared, controlling for country-fixed effects. The linear coefficients show the predicted signs for both measures, positive for the first measure and negative for the second, but it is clear that the non-linear component in the evolution should not be overlooked. In both cases, the coefficient on the year-squared variable has the opposite sign.

Domestic sourcing by MNEs (in blue) increases over most of the sample, but the negative square term implies that this effect gets weaker towards the end of the sample and by 2014 the square term even dominates. Once the average share of domestic sourcing reached 50% it stopped growing. The decline in foreign sourcing by small manufacturers (in red) shows an almost imperceptible negative trend in the first few years. The positive square terms starts dominating
of Reproduction and Fertility Control on Women's Health. Prepared for the Workshop on Health Consequences of Contraceptive Use and Controlled Fertility, National Research Council, Committee on Population, unpublished draft, August, 1986.

Grimes, D., Peterson, H.B., Rosenberg, N.J., Fishburne, J.I., Rochat, R.W., Khan, A.R., and Islam, R. "Sterilization-attributabld **e** aths in Bangladesh." International Journal of Gvnaecology and Obstetrics, 20 (2): 149-54, April, 1982.

Harrison, K.A. "Child Bearing, Health and Social Priorities: A Survey of 22,774 Consecutive Hospital Births in Zaria, Northern Nigeria." kBiish Journal of Obstetrics and Gynaecology, 92, (supplement 5): 1-115, 1985.

Herz, B. and Measham, A.R. "The Safe Motherhood Initiative: Proposals for Action." Staff Working PaRers (in press), The World Bank, Washington D.C., 1987.

International EncyloRedia of Ponulation. Vol. 1, J.A. Ross (ed. in-chief), New York, The Free Press, 1982.
, and highlight a five-step process for the formation of Taobao Villages: introduction of Taobao projects, primary diffusion, accelerated diffusion, collective cooperation, and vertical agglomeration. They emphasize the prerequisites for the healthy development of Taobao Villages include two factors, one is the identification of a promising e-commerce project which can naturally trigger diffusion, and the other is decent local infrastructure, such as internet, transportation, and logistics. Qi et al. (2018) analyze the determinants of the formation of Taobao Villages using county-level panel data from the statistical yearbooks of Zhejiang and Jiangsu provinces for the years 2012–2015. They find that from the array of county-level characteristics, villages with more educated populations are more likely to become Taobao Villages. The Chinese Taobao Village development report (Nanjing University and AliResearch 2018) examines the patterns of formation of the Taobao Villages and their evolution, drawing from extensive fieldwork. The report shows that, while most Taobao Villages on the coast developed spontaneously, often led by a couple of first-mover return migrants and followed by fellow villages, government support, often through subsidized service provision by experienced e-commerce service firms, has become a major force for the incubation of inland Taobao Villages.

> 3 See more details in AliResearch (2017), _Taobao Villages._

4
one’s objective were purely inequality reduction, then cash transfers in South Africa had dramatically larger access impacts on girls than on boys, despite not being gender‐targeted (Eyal, Woolard, and Burns 2014). A mother tongue learning instruction in Kenya in the Lubukusu language had no discernible impact on boys’ learning but a sizeable impact for girls (Piper, Zuilkowski, and Ong’ele 2016). However, there are no clear patterns as to which classes of interventions are inequality enhancing versus inequality reducing. For this inequality analysis, we drop girl‐targeted studies that do not report impacts for boys. If one were to assume that those girl‐targeted studies that did not report outcomes for girls had zero impact on boys, then girl‐ targeted interventions would decrease inequality more than general interventions.

## Costs

While we standardized effect sizes across interventions in this review, incorporating cost data would enhance the analysis, as the most effective programs may not be the most cost‐effective and therefore not easy to scale up. However, despite a strong demand for cost data, few studies report them. McEwan (2015) in his review stated that 56 percent of studies reported no cost details, and most of the rest reported minimal information. We encountered similar problems when we tried to collect cost data. In addition, even when cost data are reported, they are often not comparable due to different accounting methods. Taking an early childhood development program in rural Mexico as an example, the cost per child estimated by World Bank researchers was $76 (Cárdenas, Evans, and Holland 2015), but when evaluated by another group of researchers at Brookings, the cost per child almost doubled to $174‐$202 (Gustafsson‐Wright, 13