rafmacalaba/data-use-mentions-extended huggingface dataset:
farmers or construction workers have similar (educational, occupational and income) prospects—one where a parent’s income, occupation, education, or status does not fully or substantially predetermine the son’s (or daughter’s). The origin-independence axiom, originally formulated by Shorrocks (1978) requires that the measure of mobility rise when the correlation coefficient between of the vector of outcomes for the father and the vector of outcomes for the son (or daughter) goes down. Or to be precise, if �� denotes the vector of incomes of all fathers in society and �� the vector of incomes of all sons, the mobility measure could be written as 1 � ��� where ��� signifies the correlation between father and son’s income vectors. Thus a society in which father’s position in society perfectly predicts the position his children might be able to attain is the mark of an immobile society. It is easy to see that this idea is intimately linked to the concept of equality of opportunity.

Measuring the extent of mobility along these lines however is quite challenging. This is partly because data required for this kind of analysis are hardly available in most countries, not least Nepal. Even in the most advanced economies with fairly sophisticated data, for example the United States, we are only recently beginning to see some work emerging in this area. Chetty et al (2010) have analyzed tax and administrative records of about 40 million individuals and their parents in an ambitious multi-year undertaking to come up with measures of intergenerational mobility for the United States. Similar information is not available in most developing countries.

A close second best is to study and analyze intergenerational mobility along the lines of educational attainments and occupational status of grown adults and their fathers. This is the approach that has been taken in several prominent regional studies of intergenerational mobility conducted by the World Bank (e.g., Ferreira, 2013; World Bank, 2015) and it is the approach we will follow here
.

Data published by Damodaran on listed company effective tax rates according to companies’ financial reports supports this relatively high level of profits taxation. The different versions of effective tax rates calculated by him for the profitable mining companies as a sector varies between 25.8% for _average cash tax rate_ and 40.5% for the _aggregate effective tax rate_ .<sup>7</sup> These numbers do not include precious metal miners, where he observes higher rates than for the non-precious miners.

Because surplus is only a portion of profits, the tax on surplus and thus resource rent will be greater than these relatively high rates of profit taxation. A review of 17 mining projects in selected Latin American countries estimated that governments take on average more than 60% of mining project rents over the life of the project, leaving less than 40% to go to other claimants.<sup>8</sup> In other words, to the extent that mining projects contain resource rents, there is evidence that most of it is being taxed away under existing tax instruments.

# E. Mining Rents and Surplus Are Relatively Large

Rents are not observable in the market. What is observable is the miners’ after-tax profit margins. If resource rents were large, they should be observable on the bottom lines of mining companies’ financial

> 5 <u>https://www.imf.org/en/Blogs/Articles/2021/11/05/blog-countering-tax-avoidance-sub-saharan-africa-miningsector.</u>

> 6 _ICMM Members’ Tax Contribution_ - _Focusing on Corporate Income Tax and Royalties – 2021 Update_ . ICMM, December 2022.

> 7 ~ Retrieved in August 2023 from https://pages.stern.nyu.edu/ <u>adamodar/New_Home_Page/datafile/taxrate.htm.</u> 8 According to Davis and Smith (2020), governments in Latin America take 60% of project value at the development stage. Because value at this stage includes quasi-rents as a return to past
between the second quarter of 2008 and the first quarter of 2009. Since intra-GPN trade is especially sensitive to changes in the trade costs (Section 5; Yi, 2003), this can explain the magnified fall in trade compared to GDP.

A final explanation that has received less attention is the role of bullwhip effects in global supply chains on the trade collapse (Escaith and Gonguet, 2009; Escaith, Lindenberg and Miroudot, 2010; Ma and Van Assche, 2010). The bullwhip effect is one of the most researched and documented symptoms in the field of supply chains management (Lee, Padmanabhan and Wang, 1997; Cachon, Randall and Schmidt, 2007). It states that, when a downstream firm is confronted with a drop in demand for its final products, its first reaction is to run down its inventories. Thus a slowdown in downstream activities transforms itself into an amplified reduction in the demand for inputs that are located upstream.<sup>20</sup> Since fluctuations in final demand get amplified as one moves upstream along the supply chain, this provides an alternative explanation of the magnified fall in trade compared to GDP.

Due to the data limitations highlighted in Section 2, most of the above-mentioned studies have relied on indirect measures to evaluate the role of vertical specialization on the trade collapse. In this section, we will use data on China’s processing trade regime to evaluate if there is direct evidence of a compositional effect, trade cost effect and bullwhip effect in GPNs during the Great Recession.

The dataset that we will use in this section differs from the rest of the paper in an important way. Instead of using annual data, we will rely on processing trade data for the first quarter of 2008 and the first quarter of 2009. Since the Great Recession hit China’s exports in the last quarter of 2008, the quarterly data allow us to analyze the initial impact of the Great Recession on China’s processing trade. As it is shown in Table 8, the
non-response and thus could not examine the importance of unit non-response relative to underreporting.

The extent of item non-response to income or wealth household survey questions has not, as far as we can tell, been the subject of any cross-country research. Campos-Vazquez and Lustig (2019) showed that in Mexico the labor force survey has shown dramatic increases in item non-response to labor income questions, from around 6% in 2002 to 30% by 2017, whereas the income and expenditure survey item non-response has been roughly constant around 3%. The South African Labour Force survey item non-response rate for labor income was around 5%-8% in the 2000s. The publicly available Quarterly Labour Force Survey data from 2010 onwards does not allow for the creation of reliable item non-response rates due to imputation but non-public data shows it also increased to close to 30% in 2020. Further research on the broader trends in middle income countries especially, where item (and unit) non-response rates are much higher, seems like an important gap, as does the extent to which imputation improves the estimates of inequality and the top tail of the income distribution.

# A Research Agenda

Using Existing Data _Household surveys_ Since we have noted that there is very limited income data in the public domain for some LMICs (e.g. African countries), one crucial part of the research agenda measuring the top of the income distribution would be to harmonize and make easily and publicly available the surveys that already exist. The World Bank’s publicly available Statistics Online platform is one example of making data public and more easily available, although this currently seems to include only data on consumption and not income. It also seems to have been entirely ignored outside the World Bank.<sup>6</sup> The World Bank is well placed to lead research that harmonizes the data that does exist, and ideally makes it publicly accessible.

The Rural Income Generation Activities (RIGA) project is one example of a project that did make available and harmonize household surveys with
, ad-valorem equivalent measure of applied protection across the world: The MAcMap-HS6 database,” CEPII discussion paper 2004–22, December.

- Brown, Drusilla K. 1987, “General Equilibrium Effects of the US Generalized System of Preferences _,” Southern Economic Journal_ 54: 27-47.

- Candau, Fabien and Sébastien Jean. 2005. ”Are EU Trade Preferences Under-Utilised?”, CEPII, mimeo.

- Candau, Fabien Lionel Fontagne and Sébastien Jean. 2004. “The utilization rate of preferences in the EU,” CEPII, mimeo.

- Carrère, C. and de Melo, J., 2004, “Are Different Rules of Origin Equally Costly? Estimates from NAFTA,” CEPR Discussion Paper 4437.

- Commission for Africa 2005, “Our Common Interest”, Report of the Commission, March.

- Francois, J, B. Hoekman and M. Manchin, 2005, “Quantifying the Magnitude of Preference Erosion due to Multilateral Trade Liberalization”, background paper for the DFID Global Trade Architecture project, May.

- Francois, J., D. Spinanger, and J. Woertz, 2005, “The impact of ATC quota elimination on LDC exports,” background paper for the DFID Global Trade Architecture project.

27
areas along a main road, expanding electricity access has a larger effect in rural areas; bringing an electric grid 10 km closer to areas with close road networks marginally increases the probability of working by 16.1 percentage points in rural areas, and by only 1.3 percentage points in urban areas. Additionally, the joint marginal effect of road and electricity access is positive and larger in rural areas. The rationale for such a result could simply be that rural areas have a larger percentage of unemployed laborers among their residents than is the case in urban areas; thus, the expansion of infrastructure creates employment opportunities to a greater degree for this unemployed and underemployed rural labor force.

Results based on the LSMS sample differ. While the estimated effects of roads and electricity are positive in rural areas (as in the DHS), both the marginal effect of roads and the joint effect of roads and electricity are negative. We should note that this discrepancy could be explained by the fact our LSMS sample is drawn mostly from rural areas. The representation of urban areas might not be sufficient. In Appendix Table 13, we present the OLS results, which reinforce the idea that roads and electricity play complementary roles in job creation in rural areas. In contrast to the IV estimates, the OLS estimates show complementarity for infrastructure investment impacts in urban areas.

We also analyze the differential effects of roads and electricity on low-skilled and high-skilled employment in urban and rural areas. The results are reported in Table 8 in the Appendix. The findings from both the LSMS and DHS samples show that low- and high-skilled employment respond differently to the expansion of infrastructure. Low-skilled employment decreases in rural areas but tends to increase in urban areas. By contrast, high-skilled employment tends to increase in both urban and rural areas. In areas along the grid, bringing road networks 10 km closer to a rural location marginally decreases the likelihood of working 56 percentage points in the DHS sample (column 4); in urban areas, bringing road networks 10 km closer
latter shows the effect of central excise tax incentives on the employment outcomes of the treated sectors.<sup>6</sup> It is worth noting that a short negative list was also issued for the central corporate income tax exemptions. However, the list is much more restrictive with only bitumen, petroleum, polyester and a few motor vehicle production sectors being included. And, it has limited overlap with the negative list for the central excise tax incentives. We expect much smaller impact of this negative list for corporate income tax on firm’s investment behavior and do not control for these sectors explicitly in the baseline analysis. However, as robustness checks, we explore the impact of this negative list in two ways: first, we exclude the sectors from the analysis, and second, we add these sectors to the treated group.

# _4.3 Spillovers: Coagglomeration and Marshallian mechanisms_

In theory, the existence of agglomeration economies can have ambiguous impact on the effects of a placebased policy because of multiple equilibria. On one hand, agglomeration forces can strengthen the policy. They can continue attracting business investments to the targeted area, even after the policy is phased out. On the other hand, agglomeration economies can weaken the policy. When economic activities are initially concentrated in other areas, they can result in spatial concentration of firms that is hard to be reshaped. For this reason, a “big push” type of policy has a greater potential to overcome the threshold effect of development and turn a backward place around. The New Industrial Policy could be such a case because of its scale. In this paper, we empirically examine this hypothesis.

To quantify the impact of agglomeration economies, previous studies have used long-term data with postintervention observations (Kline and Moretti 2014a). In the absence of such data, it is challenging to separately identify agglomeration economies from the general impact of place-based policies because both could lead to the concentration of firms in the same sector in the targeted area.

We circumvent this issue by
HS classification to the ISIC classification, too much information is lost when trying to aggregate one classification with around 5,000 products to another with 56 industries. An alternative approach that is sometimes used in the literature is to draw on more detailed input-output data that are only available for certain countries and assume these relationships hold for other countries. For example, a more detailed input-output table (covering 400 industries) is available from the Bureau of Economic Analysis (BEA) for the United States, and this has been mapped to estimate value chain relationships for HS trade data5. However, aggregation issues remain in mapping 400 industries to 5,000 products, and it is questionable whether the input-output relationships of the US are generalizable to other countries.

12
,523|28.7%|8.4%|Yes|
|Contribution to Resident's System|337|1.3%|0.4%|Yes|
|**_Total Non-Tax Revenue_**<br>**_Notes:_**The government budget in China has four categories|2,696<br>:|**10.3%**|3.0%|No|

**_Notes:_** The government budget in China has four categories:

(i) The General Public Budget (GPB) which includes tax and non-tax revenue. This category is included in this table and represented 52.6% of total government revenue* in 2020.

(ii) The social security fund budget (SSFB) which includes contributions of firms and individuals to social security funds and pays for social security benefits. This category is included in this table and represented 18.7% of total government revenue in 2020 (after deducting the subsidies transferred from the other budgets).

(iii) The Government Fund Budget (GFB) which includes revenue from the 24 government funds of which land lease fund revenue is the largest, local government special bonds and others. This category is _<u>not</u>_ included in the table; it represented 23.3% of total government revenue in 2020.

(iv) The state-owned enterprise (SOE) operation fund budget which included revenue accrued through the SOEs. This category is _<u>not</u>_ included in the table; it represented 1.1% of total government revenue in 2020.

* Data for total revenues reflect a small amount of double-counting data because of the transfer of funds among the budget categories and the lack of exact double-counting data. (Qiao, Fan, Rahemtulla, van Rijn & Li, 2022)

**_Sources_ :** Statistical Yearbook of China 2019, National Bureau of Statistics (2019), Finance
Policy Research Working Paper 9397

# **Abstract**

Management practices matter for firm performance. As energy is one input in firm production, management practices may interact with energy use. Using a comprehensive firm-level database covering 31 countries, this study documents the link between structured management practices, energy use, and firm performance. The paper reports several findings. _First,_ although management is negatively correlated with energy expenditure, it bears a positive (or null) relationship with physical energy use, suggesting that management effort is directed toward saving costs but not reducing environmental impact. These results are primarily driven by the manufacturing sector. _Second,_ among the structured management practices examined, those relating to target-setting are associated with reduced energy expenditure intensity. _Third,_ generic management practices are correlated with greater discipline around energy management. _Finally,_ while generic practices are correlated with stronger firm performance in manufacturing and services, energy-centric practices show a positive association only in services. Vast heterogeneity in adoption and outcomes suggests that targeted approaches to encourage energy management practices in firms may be more effective than uniform ones.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
Household Budget Surveys estimate returns to education in order to explain demand for education and report OLS estimates between 0.3% to 16.3%, depending on the sub‐group, and not controlling for endogeneity. Using nationally representative cross‐section data from of the Integrated Labour Force Surveys (IFLS) for 2001 and 2006, Kerr (2011) estimate returns between 8% and 13% when using OLS. When allowing for nonlinearities the results suggest that returns are strongly convex, but when also addressing endogeneity exploiting a change in the education system in the mid 1960s, returns are concave and higher at the lower levels of education, which the authors argue reflects an ability bias.<sup>37</sup> These results also shed light on earlier findings by Söderbom, Teal, Wambugu, Kahyarara (2006), who, using data for employees in the manufacturing sector in Tanzania for 1993, 1994, 1999 and 2001, also find a convex earnings function when taking endogeneity into account. Their estimates exceed the OLS estimates, which may be a consequence of self‐selection on ability into the manufacturing sector. Our estimates also aligh well with those from other African countries. Schultz 2004 reports wage gains of 5‐20% for each year of schooling for five African countries.<sup>38</sup> Our results underline that survey methods matter for the estimation of structural parameters, such as Mincerian returns to education, and indicate that care is needed in the comparison of these returns across data sources, as is typically the case with worldwide comparisons, as well as comparisons over time

# **7. References**

Angrist, J. and A. Krueger. 1991. “Does Compulsory School Attendance Affect Schooling and Earnings?” _The Quarterly Journal of Economics_ 106(4): 979‐1014.

Anker, R. 1983. “Female Labour Force Participation in Developing Countries: A Critique of Current Definitions and Data Collection Methods.” _International Labour Review_ 122(6)
Prevalence: Old Rohingya Prevalence: New Rohingya<br>prevalence prevalence prevalence<br><!-- End of picture text --> Source: 2011 census for host population count; IPA population count for Rohingya displaced.

Source: IPA Population Count, 2018. Old Rohingya refers to displaced population arrived before September 2017. New Rohingya refers to population arrived after September 2017.

At the upazila level, Rohingya prevalence was low outside Teknaf and Ukhia, the two upazilas that contain all the camps. Cox’s Bazar Sadar upazila, which is the district capital, was the only upazila where Rohingya prevalence exceeded 5 percent; almost all of these cases came from the older cohort of Rohingya displaced (Figure 2). In Ukhia and Teknaf, which reported a significant Rohingya presence, displaced households seemed to be located almost entirely in camps. In particular, villages with no camps reported prevalence rates of 0.8 and 4.2 percent respectively (Figure 3).

> 12 The IPA count excluded Kutubdia and Maheshkhali upazilas where refugee prevalence was expected to be low as there were islands.

5
the uncertainty of the import process. We take PSI as an example of “protectionist” NTMs.

# _2.3. NTM Data Construction_

The original information on NTMs comes from the _TRAINS_ database collected by the _Economic Research Institute for ASEAN and East Asia_ (ERIA) and UNCTAD. This data is based on official regulations that were in effect by January 2015 with an in-force date by January 2016. The building block of our explanatory variables are dummies taking value 1 if in a given year, the Indonesian government applied an NTM on an HS10 digit product. Since we consider the universe of HS-10 digit products, if a particular NTM is not in our data, then it means that the Indonesian government never applied it.<sup>8</sup> The resulting yearly panel at HS-10 digits code starts in January 2008 and ends in December 2015. We adjust the data in a number of ways. First, when the same NTM appeared multiple times for an individual product, we verified whether the corresponding regulations effectively introduced a new measure. Forty percent of repeated NTM codes corresponded to regulation updating an older one, in which case we do not consider it as a separate measure (as the original ERIA dataset does). Second, we adjust the date of entry into force of certain measures when their associated regulation (as recorded in January 2015) was in fact an update of earlier regulations.<sup>9</sup> In such cases, we also record whether there are changes in the list of products the NTM regulation applies to. Finally, as the dataset is based on the coding of data collected in January 2015, there may be concerns about measures that were in force during part of the period of analysis but eventually eliminated before January 2015. To address these concerns, we check for such instances through the _Global Trade Alert_ data, which records both trade restrictive and trade liberalizing measures for all countries since 2009.<sup>10</sup> To link the NTM dataset to