rafmacalaba/datause-encoder-data huggingface dataset:
home-consumption including owner-occupied housing,<sup>18</sup> treatment of publicly-provided health and education benefits, and the use of top-coding of high incomes (where all incomes above a certain ceiling are coded as equal to that ceiling).<sup>19</sup> For different countries and at different levels of development, differences in the treatment of these categories are not equally important. For poor countries, it is the problems of valuation of own consumption and self-employed income that are the most difficult and that can make individual incomes often move up by a factor of 2 or more; for the rich countries, it is the treatment of publicly-provided health and education benefits that is of most concern. Swedish disposable income with them or without them is quite different. For countries with extravagantly rich individuals it is the underestimation of capital incomes which is of concern.

But there is no agreement that “income” in global inequality studies should be income at all. Many people think that rather than income, one should look at consumption or expenditures as the true indicator of the standard of living. This debate mirrors the debates that are waged in individual countries since obviously inequality can be measured using either income or consumption. What lends this debate an added importance in the case of global inequality is that in many countries household surveys ask income questions only, while in other countries they ask for both, or for expenditures only. Then, a global study of inequality has to do what all national studies try to avoid, that is to mix household survey data that use two different concepts of “income”: disposable income and consumption. This introduces an error whose direction and magnitude cannot be estimated. Although in the last few years, there has been a trend toward the use of consumption measures (not the least through the efforts of the World Bank and the influence it exerted on the choice of survey instruments in the former communist countries and Africa), we are still far from unanimity on this issue. In the study of global

> 18 For example, in 1990 Chinese statistical office changed the valuation of grain output produced by rural households from state-mandated to market prices. This generated a large change in the calculated poverty rates and a break in the rural mean income series (see Ravallion and Chen, 2005).

> 19 For example, the US Current Population Survey top-codes all very high wage and capital incomes. Similarly, the maximum capital gain that can be recorded in the survey is $149,999 per household annually.

11
- Sala, E., O. Aburto-Oropeza, M. Reza, G. Paredes, and L. G. López-Lemus. 2004. “Fishing Down Coastal Food Webs in the Gulf of California.” _Fisheries_ 29 (3), 19–25.

Sanchez, A., J. J. C. Nieto, I. M. Osorio, B. Erisman, M. Moreno-Baez, and O. Aburto-Oropeza. 2015. Caracterizacion de las cadenas productivas pesqueras – baja california sur, mexico (primera fase la paz). Database, Centro para la Biodiversidad Marina y la Conservacion (CMBC) and Scripps Institution of Oceanography.

- Schaefer, K. M., D. W. Fuller, and B. A. Block. 2007. “Movements, Behavior, and Habitat Utilization of Yellowfin Tuna (Thunnus albacares) in the Northeastern Pacific Ocean, Ascertained through Archival Tag Data.” _Marine Biology_ 152 (3), 503–25.

- Scott, A. 1956. “The Fishery: The Objectives of Sole Ownership.” _The Journal of Political Economy_ 63 (2), 116–24.

- SEPESCA 1992. Legal Framework for Fisheries 1992, Technical report, Secretaria de Pesca, Mexico City.

- Sievanen, L. 2014. “How do small-scale fishers adapt to environmental variability? lessons from Baja California Sur, Mexico.” _Maritime Studies_ . 13:9:doi:10.1186/s40152-40014-4000940152.

- Velarde, E., E. Ezcurra, M. A. Cisneros-Mata, and M. F. Lavín, M. F. 2004. “Seabird Ecology, El Niño Anomalies, and Prediction of Sardine Fisheries in the Gulf of California.” _Ecological Applications_ 14 (2), 607–15.

- Villa, A. 1996. “A Review of Recent Changes in Mexico’s Fishing Policy,” thesis, College of Marine Studies at the University of Delaware.

- White, C. and C. Costello. 2011), “Matching Spatial Property Rights Fisheries with Scales of Fish Dispersal.” _Ecological Applications_ 21, 350–62.

- Wilen, J. E., J. Cancino, and H. Uchida. 2012. “The Economics of Territorial Use Rights fisheries, or TURFs.” _Review of Environmental Economics and Policy_ (62), 237–57.

- Wooldridge, J. 2010. _Econometric Analysis of Cross Section and Panel Data, Second Edition_ , Cambridge, MA: The MIT Press.

- Young, E. 2001. “State Intervention and Abuse of the Commons: Fisheries Development in Baja California Sur, Mexico.” _Annals of the Association of American Geographers_ 91 (2), 283– 306.

4
# Effects of Income Inequality on Aggregate Output

by Markus Brueckner and Daniel Lederman* _Key words:_ Income Inequality, Economic Growth _JEL codes:_ O1

> * University of Queensland (Brueckner), and World Bank (Lederman). We gratefully acknowledge insightful comments from Antonia Díaz, Marcelo Olarreaga and other participants in the inequality and growth workshop held at the Carlos III University in Madrid, Spain, June 10, 2015.
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**5. Domestic VAT Refunds for Companies Located in the Hainan Economic Development Zone.** One of Gold East’s cross-ow ned companies was a qualifying manufacturing enterprise in the Economic Development Zone of Hainan.

**6. Other Subsidies.** Chenming reported four additional programs in which it participated. Due to Chenming’s request that the DOC treat information about these four programs as business proprietary, and determ ine that these four programs constitute countervailable subsidies.

**Benchmark and Subsidies Calculation.** As discussed in the proceeding section, the DOC determined that loans receiv ed by the pa per industry from Chinese banks, either policy bank, SOCBs, or foreign banks, constitute countervailable subsidies. According to U.S. law, the benefit for a preferential loan is the “difference be tween the amount the recipient of the loan pays on the loan and the am ount the recipient would pay on a comparable commercial loan that the recip ient could actually obtain on the m arket.” Normally, the DOC uses com parable commercial loans reported by the com pany for benchmarking purposes. However, the DOC argued that the Chinese banking sector does not operate on a commercial basis, and that the interest rates of the Chinese domestic banking sectors do not provide a suitable ba sis for benchmarking. Then the DOC created a market-based benchmark outside of China by a simple average of the national len ding rates for countries with comparable gross national income with China.

**1. Domestic Benchmark.** The DOC determ ined that th e Chinese dom estic banking sector does not provide an appropriate be nchmark because the Chinese banking sector dose not operate on a commercial basis and is su bject to significant distortions, primarily arising out of the continued dominant role of th e government in the sector. (1) The GOC’s ownership: the GOC continues to c ontrol the vast m ajority of financial intermediation in the banking sec tor; (2) SOCBs: they continue to be plagued by functional and operational problem s, and fund s continue to be a llocated in a m anner consistent with the government policy; (3)  Foreign banks: foreign investment in banking sector is tightly constrained, and foreign banks are subject to the same restrictions as the SOCBs. Therefore, foreign bank lending does not provide a suitable benchmark.

**2. External Benchmark.** The DOC uses a cross-country average lending rate as the external benchmark in this proceeding. Th e countries are select ed based on sim ilar income levels to China: (1) China is a lo wer-middle income country according to the country classification of the World Bank, 37 countries have been selected; (2) The DOC computed a sim ple average of these 37 lend ing rates from _International Financial Statistics_ in 2005, as the external benchm ark for short-term loans; (3) The DOC developed a ratio of short-te rm and long-term lending for 2005, by using the short-ter m lending drawn from London Interbank Offered Rate (LIBOR) data and long-term interest rate from the interest rate swap market reported by the Federal Rese rve, to transfer the short-term lending rate into long-term one.

27
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# **Wage/profit equation for non-agriculture unskilled**

Number of obs = 42813743 F( 16, 6514) = 1710.86 Prob > F = 0.000 R-squared = 0.423 Root MSE = 0.67388

||_Ro_|_bust Std._<br>|||||
|---|---|---|---|---|---|---|
||_Coef._|_Err._|_t_|_P>|t|_|_[95% Co_|_nf. Interval]_|
|edu|0.008|0.003|2.840|0.004|0.003|0.014|
|edu2|0.006|0.000|33.350|0.000|0.006|0.007|
|exp|0.069|0.001|88.200|0.000|0.068|0.071|
|exp2|-0.001|0.000|-68.950|0.000|-0.001|-0.001|
|gend|-0.590|0.006|-102.830|0.000|-0.601|-0.579|
|preta|-0.148|0.011|-13.460|0.000|-0.169|-0.126|
|parda|-0.143|0.007|-21.910|0.000|-0.156|-0.130|
|dun|-0.190|0.013|-15.020|0.000|-0.214|-0.165|
|drn|-0.501|0.106|-4.710|0.000|-0.709|-0.293|
|dune|-0.450|0.011|-40.590|0.000|-0.472|-0.428|
|drne|-0.710|0.033|-21.800|0.000|-0.774|-0.646|
|drse|-0.229|0.029|-7.960|0.000|-0.286|-0.173|
|dus|-0.069|0.011|-6.470|0.000|-0.090|-0.048|
|drs|-0.171|0.029|-5.950|0.000|-0.227|-0.115|
|ducw|-0.123|0.013|-9.420|0.000|-0.149|-0.098|
|drcw|-0.383|0.054|-7.040|0.000|-0.490|-0.276|
|_cons|4.494|0.020|227.760|0.000|4.455|4.533|

Source: Authors’ calculations. Note: edu: years of schooling edu2: edu squared exp: age minus schooling exp2: experience squared gend: female 1 preta: black parda: mixed black nnonrem: number of non-remunerated household members d*: regional dummies with r (u) for rural (urban), n north, ne northeast, s south, cw centerwest.

60
Manual vacuum aspiration (MVA) is a variation of vacuum aspiration. It employs a portable, nonelectric, single- or double-valve syringe that lias been demonstrated to produce a vacuum as effective as that produced by an electric aspirator [21]. For nearly two decades, MVA has been successfully utilized in a variety of healthcare settings worldwide.

The costs and resource utilization of different methods of uterine evacuation have been neither documented nor compared basea on actual obsen ations of patients treated for incomplete abortion; however, many of the hospital resources associated with the treatment of incomplete abortion (for example, hospitalization ,ime, procedure time, blood transfusions and anesthesia) have been identifled and their costs calculated based on hospital records [5,22]. A Colombian study examined the clinical and managerial aspects of hospital treatment of incomplete abortion with vacuum aspiration (VA) and dilation and curettage (D&C) [23]. Two of the study's findings were: 1) that outpatient management using either VA or D&C resulted in decreased patient stay and 2) that the use of anesthesia was greater for D&C prima ly because of long-established pattems of practice.

# Purpose of the Study

The attributes of D&C and MVA described above led to the hypothesis that MVA would be less costly to healthcare systems than D&C, primarily because MVA does not involve heavy sedation, operating rooms, or overnight hospital stays, and therefore requires fewer staff and less staff time per patient. This study was a demonstration project designed to identify and, where possible, explain the factors that contribute to the cost differences between use of the two clinical procedures for treatment of first-trimester incomplete abortion. To achieve these objectives, researchers observed patient management and documented use of financial and other resources in an attempt to assess and, where appropriate, compare the type and number of resources required for each procedure. The study was implemented at selected hospital sites in Kenya, Mexico and Ecuador.

This study was designed to help researchers examine the _actual practice_ of D&C and MVA. It was not intended to produce comparative data for statistical applications; however, a limited amount of comparative analysis can enable policymakers and managers to identify trends in resource utilization and abortion-patient management. All comparisons that follow are made with the cautionary note that many clinical, administrative, economic and geographic factors vary among different hospitals and may affect resource consumption and, hence, costs.

Following a brief methods section we present the results of data collected in Kenya, Mexico and Ecuador and discuss the implications of the study's results for policy and practice within hospitals and healthcare systems with similar abortion-management protocols.

2
**Table 3.5  Kenya functional code mapping**

|**Vote**|**Sub vote**|**Head**|**Description**|**GFS**<br>**Code**|**Description:**<br>**(COFOG)**|
|---|---|---|---|---|---|
|**R13**|Recurrent<br>Budget|**Ministry of Public Works and**<br>**Roads**||||
|**R13**|130|381|Provincial Administration Services|70451|Road transport|
|**R13**|130|382|District Administration Services|70451|Road transport|
|**R13**|130|419|Supplies Branch|70451|Road transport|
|**R13**|133|505|Mechanical and Transport Department|70451|Road transport|
|**R13**|133|506|Material Branch|70451|Road transport|
|**R13**|136|384|Major Roads|70451|Road transport|
|**R13**|136|392|Headquarters Roads Department|70451|Road transport|
|**R13**|136|393|Road Works Inspectorate|70451|Road transport|
|**R13**|136|482|Provincial/District Administration and Technical Services|70451|Road transport|
|**R13**|136|483|Extra-ordinaryRoad Maintenance|70451|Road transport|
|**D13**|Development<br>Budget|**Ministry of Public Works and**<br>**Roads**||||
|**D13**|133|505|Mechanical and Transport Department|70451|Road transport|
|**D13**|133|506|Material Branch|70451|Road transport|
|**D13**|136|**Roads**||||
|**D13**|136|384|Major Roads|70451|Road transport|
|**D13**|136|385|Other Roads|70451|Road transport|
|**D13**|136|488|Extra-ordinaryRoad Maintenance|70451|Road transport|
|**D13**|136|489|Miscellaneous(RA.RP and G.B.c)|70451|Road transport|
|**R14**|Recurrent<br>Budget|**Ministry of Transport**||||
|**R14**|140|**General Administration and**<br>**Planning**||||
|**R14**|140|572|Shippingand Maritime Affairs Department|70452|Water transport|
|**R14**|140|574|Aircraft Accident Investigation|70454|Air transport|
|**R14**|141|**Information Communication**<br>**Technology Services**||||
|**R14**|141|613|Information Communication Technology Services|70460|Communication|
|**R14**|144|**Railways **||||
|**R14**|144|457|Kenya Railways Corporation|70453|Railwaytransport|
|**R14**|148|**Road Transport**||||
|**R14**|148|475|Transport Licensingand Registration|70451|Road transport|
|**D14**|Development<br>Budget|**Ministry of Transport**||||
|**D14**|140|**General Administration and**<br>**Planning**||||
|**D14**|140|572|Shippingand Maritime Affairs Department|70452|Water transport|
|**D14**|144|**Railways **||||
|**D14**|144|457|Kenya Railways Corporation|70453|Railwaytransport|

Source: World Bank (2006) _Economic code mapping_ . There are numerous ways of generating budget lines and/or recording expenditures according to their use. When filling in fiscal data template 10, the data collector needs to group (and in some cases delineate) spending categories so as to map them to the GFSM 2001 economic categories. By way of example, table 3.6 presents an economic code mapping for Rwanda in which 43
and West Bengal) and rose by more than 7 percentage points in some landlocked states (Bihar, Haryana, and Madhya Pradesh).

Central transfers, state expenditures, and fiscal deficits are not influenced by geographical location; hence the distinction between coastal and landlocked states is not relevant in analyzing the movement of these variables. Central transfers are made up of mandated shares in central taxes and central grants.

**Table 11 Real Annual Changes in Central Transfers in India, by State, 2009–10** **<u>(percent)</u>**

||_Shared c_<br>_taxe_|_entral_<br>_s_|_Central_|_grants_|_Total ce_<br>_Precrisis_|_ntral transfers_<br>_Postcrisis_|
|---|---|---|---|---|---|---|
|_State_|_2009_|_2010_|_2009_|_2010_|_2003–07_|_2009 and 2010_|
|Andhra Pradesh|–4.8|–3.7|10.4|–20.5|12.8|–5.1|
|Bihar|–3.3|23.7|–10.7|20.4|15.0|7.6|
|Gujarat|–2.9|6.4|–21.1|6.4|8.7|–2.5|
|Haryana|–4.3|21.0|65.1|–4.0|10.7|17.4|
|Karnataka|–4.6|20.5|37.1|–18.7|16.4|6.5|
|Kerala|–1.5|0.8|–20.4|–15.2|12.5|–6.7|
|Madhya Pradesh|–4.0|16.0|6.2|17.4|15.9|7.7|
|<br>Maharashtra|–6.7|33.9|–11.1|–3.4|32.6|1.0|
|Odisha (Orissa)|0.0|13.6|7.7|9.8|13.0|7.4|
|Punjab|–2.2|34.5|30.1|–2.3|23.2|13.8|
|Rajasthan|–5.3|28.2|–15.9|7.8|12.6|4.7|
|Tamil Nadu|–4.6|20.5|–28.3|19.9|13.9|0.9|
|Uttar Pradesh|–6.0|29.5|36.2|–14.2|17.3|9.7|
|West Bengal|–4.3|18.1|–10.9|13.3|10.9|4.3|
|Small and special<br>category states|–4.5|30.4|20.9|–2.0|16.5|9.8|
|All states|–4.7|21.2|8.0|–2.2|16.1|5.4|
|Major coastal states|–4.5|15.6|–4.6|–2.3|14.5|1.4|
|<br>Major landlocked|||||||
|states|–5.1|25.9|12.7|1.9|17.9|8.6|

_Sources_ : Reserve Bank of India state finances; individual state accounts; Central Statistical Organization.

The share of states in aggregate central tax collections remains fixed for five years at a time, as recommended by the Finance Commission. As 2009 was the last year of the award period of the 12th Finance Commission, all states experienced a similar movement in shared central taxes. In 2010, the first year of the new award period, when the formula for interstate distribution is determined afresh, growth over the previous year varied widely across states, ranging from close to zero to 30 percent (table 11).

29
# **Fiscal Responsibility Laws for Subnational Discipline: The Latin American Experience**

by Steven B. Webb World Bank

### ABSTRACT

This paper discusses fiscal responsibility laws in Latin America, with special attention to their provisions for fiscal discipline by subnational governments. It discusses why and when such laws might be useful—to help resolve the coordination problem in getting diverse governments to avoid overusing the common national credit market and to help individual governments make a timeconsistent commitment for fiscal prudence. It examines the cases of Brazil, Colombia, Peru, and Argentina, as well as the case of Mexico where other types of laws and regulations aim to achieve the same objectives of solidifying incentives for fiscal discipline at all levels of government. Fiscal responsibility laws are found to be useful in some cases, although the experience is not long enough to be certain, but they are clearly not necessary in every case, nor always sufficient to assure fiscal stability.

## World Bank Policy Research Working Paper 3309, May 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The paper evolved from an earlier one of which William McCarten and Mariano Tommasi were coauthors, and their contribution is gratefully recognized without any responsibility for errors that remain here. It was inspired and supported in part by a Subnational Thematic Group research project on Subnational Fiscal Adjustment. Comments and suggestions from Christian Gonzalez, George Kopits, Jonathan Rodden, David Rosenblatt, Mark Sundberg, and David Vetter are also acknowledged with gratitude.

1
Table B.3a: Changes in inequality <u>(Gini index) for the countries using phone surveys</u>

|Country|Region|2019|2020<br>(COVID-19)|Change, Gini points|
|---|---|---|---|---|
|Burkina Faso|Sub-Saharan Africa|48.10|48.14|0.04|
|Bulgaria|Europe & Central Asia|40.19|41.59|1.40|
|Central African Republic|Sub-Saharan Africa|58.48|55.83|-2.66|
|Congo, Dem. Rep.|Sub-Saharan Africa|42.42|42.21|-0.21|
|Gabon|Sub-Saharan Africa|37.89|38.10|0.21|
|Ghana|Sub-Saharan Africa|43.58|43.09|-0.49|
|Guinea|Sub-Saharan Africa|29.55|30.88|1.33|
|Gambia, The|Sub-Saharan Africa|35.88|35.67|-0.22|
|Guatemala|Latin America & Caribbean|48.13|48.74|0.61|
|Honduras|Latin America & Caribbean|48.11|48.33|0.22|
|Croatia|Europe & Central Asia|28.88|30.55|1.68|
|Kazakhstan|Europe & Central Asia|27.90|27.92|0.02|
|Kenya|Sub-Saharan Africa|40.32|41.45|1.14|
|Lao PDR|East Asia & Pacific|39.46|39.04|-0.42|
|Mali|Sub-Saharan Africa|36.21|37.87|1.66|
|Myanmar|East Asia & Pacific|31.58|30.92|-0.66|
|Mongolia|East Asia & Pacific|33.35|33.08|-0.27|
|Mozambique|Sub-Saharan Africa|54.69|53.78|-0.91|
|Mauritius|Sub-Saharan Africa|37.24|37.97|0.72|
|Malawi|Sub-Saharan Africa|39.09|38.42|-0.66|
|Niger|Sub-Saharan Africa|37.40|37.04|-0.36|
|Nigeria|Sub-Saharan Africa|35.27|34.90|-0.37|
|Philippines|East Asia & Pacific|37.92|42.45|4.53|
|Poland|Europe & Central Asia|30.25|30.24|-0.01|
|West Bank and Gaza|Middle East & North Africa|33.77|33.95|0.19|
|Romania|Europe & Central Asia|34.78|36.06|1.28|
|Sudan|Sub-Saharan Africa|34.28|34.43|0.15|
|Senegal|Sub-Saharan Africa|38.61|37.78|-0.83|
|Solomon Islands|East Asia & Pacific|37.60|36.67|-0.93|
|El Salvador|Latin America & Caribbean|38.90|38.89|0.00|
|South Sudan|Sub-Saharan Africa|44.40|45.28|0.88|
|Tajikistan|Europe & Central Asia|33.91|34.29|0.38|
|Tunisia|Middle East & North Africa|32.88|33.33|0.45|
|Uganda|Sub-Saharan Africa|42.92|42.67|-0.26|
|Uzbekistan|Europe & Central Asia|35.46|35.38|-0.08|
|Zambia|Sub-Saharan Africa|57.38|56.77|-0.61|
|Zimbabwe|Sub-Saharan Africa|50.59|50.40|-0.19|

_Note_ : This table reports the Gini index for each country using the high frequency phone surveys for 2019 and 2020 (observed COVID distribution). The last column captures the Gini points change between 2020 and 2019. Note that the inequality of the counterfactual 2020 distribution is same as the 2019 distribution for each country.

48