rafmacalaba/datause-encoder-data huggingface dataset:
# 6.  References

Acosta, Pablo, Cesar Calderón, Pablo Fajnzylber, and J. Humberto López. 2008. “Do Remittances Lower Poverty Levels in Latin America?” in P. Fajnzylber and J. Humberto López (eds.): “Remittances and Development: Lessons from Latin America.” The World Bank, Washington D.C.

Aggarwal, Reena and Horowitz, Andrew W. 2002. “Are International Remittances Altrusim or Insurance? Evidence from Guyana Using Multiple-Migrant Households,” World Development, 30(11): 2033-44.

Aggarwal, Reena, Asli Demirgüç-Kunt and Maria Soledad Martinez Peria. 2006. “Do Workers’ Remittances Promote Financial Development?” World Bank Policy Research Working Paper 3957. Washington, DC.

Black, Richard, Savina Ammassari, Shannon Mouillesseaux and Radha Rajkotia. 2004. “Migration and Pro-Poor Policy in West Africa.” Working Paper C8. Sussex Centre for Migration Research. University of Sussex.

Bouhga-Hagbe, Jacques. 2004. “A Theory of Workers’ Remittances with An Application to Morocco.” IMF Working Paper 04/194. (Washington: International Monetary Fund).

Buch, C. and A. Kuckulenz. 2004. “Worker Remittances and Capital Flows to Developing Countries,” Discussion Paper No. 04-31. (Mannheim: Centre for European Economic Research).

Bugamelli, Matteo and Francesco Paternò. 2005. “Do Workers’ Remittances Reduce the Probability of Current Account Reversals?” World Bank Policy Research Working Paper 3766. Washington, DC.

Chami, Ralph, Connel Fullenkamp and Samir Jahjah. 2005. “Are Immigrant Remittance Flows a Source Capital?” _IMF Staff Papers, 52(1)_ . (Washington: International Monetary Fund).

Clarke, George and Scott Wallsten. 2004. “Do Remittances Protect Households in Developing Countries against Shocks? Evidence from a Natural Disaster in Jamaica.” Unpublished paper. (Washington: World Bank).

Elbadawi, Ibrahim A. and Robert de Rezende Rocha. 1992. “Determinants of Expatriate Workers’ Remittances in North Africa and Europe,” World Bank Policy Research Working Paper 1038. Washington, DC.

El Sakka, M.I.T. and Robert McNabb. 1999. “The Macroeconomic Determinants of Emigrant Remittances.” _World Development: Vol. 27(8)_ , pp. 1493-1502.

18
|Percentile<br>0-10<br>Percentile 10-<br>25<br>Percentile 25-<br>50<br>Percentile 50-<br>75<br>Percentile<br>75-90<br>Percentile<br>90-100<br>Females<br>Low<br>Educated<br>Workers<br>Younger<br>Workers<br>Share Individuals Out of the Labor Force, by Income and Vulnerability Groups|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>(7)<br>(8)<br>(9)<br>Log Inspected Firms per firm in city<br>0.008<br>0.005<br>0.001<br>-0.001<br>-0.005<br>-0.001<br>0.002<br>0.001<br>-0.001<br>[0.002]***<br>[0.001]***<br>[0.001]<br>[0.001]<br>[0.001]***<br>[0.001]<br>[0.002]<br>[0.002]<br>[0.001]<br>Observations<br>5,240<br>5,131<br>5,238<br>5,240<br>5,240<br>5,240<br>5,240<br>5,240<br>5,240<br>Log Inspected Firms per firm in city<br>0.115<br>0.096<br>0.052<br>0.006<br>0.002<br>-0.010<br>0.074<br>0.083<br>0.065<br>[0.037]***<br>[0.034]***<br>[0.023]**<br>[0.018]<br>[0.018]<br>[0.019]<br>[0.034]**<br>[0.033]**<br>[0.029]**<br>Observations<br>5,240<br>5,131<br>5,238<br>5,240<br>5,240<br>5,240<br>5,240<br>5,240<br>5,240<br>Panel A: OLS<br>Panel B: IV|Standard errors in brackets, * significant at 10%; ** significant at 5%; *** significant at 1%. Panel A reports the estimated coefficients of the regression of the city variables reported at the top of each column on|the log of the number of inspected firms per firm in the city, controlling for state level dummy variables and several city level characteristics. Panel B reports the instrumental variable estimates using city distance|to the nearest labor office interacted with the number of state level inspectors per firm in the state as an instrument for enforcement. City level characteristics include distance to the nearest labor office, its square|and interactions with state level measures of institutional quality, distance to the state capital city, its square and interactions with the number of inspectors per firm in the state and interactions with state level|measures of institutional quality, city transportation costs, its square and interactions with the number of inspectors per firm in the state and interactions with state level measures of institutional quality, city|altitude, city latitude and city longitude. It also includes the city level controls for income per capita, population, average schooling and share of urban population in 1991, 1980 and 1970.|
|---|---|---|---|---|---|---|---|
Agriculture Survive Climate Change?” World Bank Economic Review **20** : 367388.

- Lotsch A, 2006. “Sensitivity of cropping patterns in Africa to transient climate change” CEEPA Discussion Paper No. 14, University of Pretoria, Pretoria, South Africa.

- Mendelsohn, R. and L. Williams. 2004. “Comparing Forecasts of the Global Impacts of Climate Change” Mitigation and Adaptation Strategies for Global Change **9** : 315333.

Parry, M.L., C. Rosenzweig, A. Iglesias, M. Livermore and G. Fischer. 2004: Effects of climate change on global food production under SRES emissions and socioeconomic scenarios. Global Environmental Change **14:** 53-67.

- Pearce, D. et al. 1996. “The Social Costs of Climate Change: Greenhouse Damage and Benefits of Control” in Climate Change 1995: Economic and Social Dimensions of Climate Change, J. Bruce, H. Lee, E. Haites. Eds. Cambridge Univ. Press, Cambridge, UK, pp.179-224.

- Ramankutty, N. & Foley, J. A. 1998. “Characterizing patterns of global land use: an analysis of global croplands data” Global Ecology & Biogeography **12** : 667-685.

- Reilly, J. et al. 1996. “Agriculture in a changing climate: Impacts and adaptations”, in R. Watson, M. Zinyowera, R. Moss and D.Dokken (eds.), Climate Change 1995: Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical Analyses, Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK.

- Rosenzweig, C. and M. Parry. 1994. “Potential Impact of Climate Change on World Food Supply” Nature **367** : 133-38.

- Seo, N., Mendelsohn, R., Dinar, A., Kurukulasuriya, P., Hassan, R. 2008. A Ricardian Analysis of the Distribution of Climate Change Impacts on Agriculture across Agro-Ecological Zones in Africa, _Environmental and Resource Economics_ (forthcoming)

- Strzepek, K. and A. McClusky. 2006. “District level hydro-climatic time series and scenario analyses to assess the impacts of climate change on regional water resources and agriculture in Africa” CEEPA Discussion Paper #13, University of Pretoria, Pretoria, South Africa.

- Tol, R. 2002. “Estimates of the damage costs of climate change. Part 1: benchmark estimates”. Environmental and Resource Economics **21** : 47-73

- Washington,W., Weatherly, J., Meehl, G., Semtner, A., Bettge, T., Craig, A., Strand, W., Arblaster, J., Wayland, V., James, R., & Zhang, Y. 2000. “Parallel Climate Model (PCM): control and transient scenarios” Climate Dynamics **16** : 755–774.

- Wood, S., Sebastian, K., & Scherr, S. 2000, “Pilot Analysis of Global Ecosystems: Agrosystems” World Resources Institute and International Food Policy Research Institute, Washington D.C.

- World Bank. 2003. “Africa Rainfall and Temperature Evaluation System (ARTES)” World Bank, Washington DC.
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## **Artisanal and small-scale mining**

To investigate the relationship between employment and a broader set of mines, we use the USGS and CSCW datasets. The results show that living within 20 kilometers from an USGS mine is associated with roughly a one percentage point increase in the probability of working in sales or services and a 2.6 percentage point decrease in the probability of working in agriculture (see Panel A of Table A.15 in the Appendix). For diamond mines, we find that the probability of working in agriculture is 5.3 percentage points lower, the probability of working in sales is 2.8 percentage points higher, and the probability of working in services is 0.6 percentage points higher if the woman lives within 20 km of a diamond mine (Panel B of Table A.15 in the Appendix). The results using these other datasets are in line with the findings using the main dataset.

# **6 Conclusion**

The discovery of natural resources across the African continent brings hope for millions of poor people, but there are also fears that the resources will be a curse rather than a blessing (e.g., Collier, 2010; Frankel, 2010; van der Ploeg, 2011). In particular, one fear spelled out in The Africa Mining Vision is that gender inequality in economic opportunities may increase with mining. Using detailed data on industrial mining in Sub-Saharan Africa, we explore whether mining generates local employment opportunities for women and men. Based on GPS coordinates, we merge individual level data with mining data, which enables a highly localized analysis of spillover effects. We then employ a difference-in-difference estimation strategy to compare areas that are close to mines with areas farther away, before and after the production has started.

The results show a localized structural shift where a mine opening offers new employment opportunities for women. There is a decrease in agricultural employment and an increase in service sector employment, and an overall drop in labor force participation. The changing local economy brings secondary effects for women with more cash employment and non-seasonal work. For men, we see a structural shift focused on increasing work in skilled manual jobs and decreasing self-employment in farming.

The effects of mine closing are not symmetrical for women; mine suspension leads to reduced service sector employment, yet women do not shift back to agricultural production to the same extent as they left it when the mines opened. Instead, the employment rate remains low, indicating that the localized structural shifts are not reversible. For men, the effects of closing are almost symmetrical. This indicates that mining works as a boom-bust economy on the local level in Africa, but with permanent effects of women’s labor market participation.

The results are robust to a wide battery of checks, such as using different distance cut-offs and different classifications of the control group, including different types of fixed effects and exlusion of migrants. The results are quantitatively important. We calculate that more than 90,000 women may have gained a service sector job as a result of mine openings, but, in parallel, 24
# <u>2.6.  Mutual support at its best:  an illustration</u>

Mattoo et al. [2009] provide calculations giving a sense of the vastly different impacts of alternative definitions of carbon border tax. Table 2 assumes that developed countries enforce unilaterally a climate change policy cutting their carbon emissions by 17 percent (roughly their average current commitments). In other words, the various policy options described below deliver exactly the same cuts of carbon emissions in the developed countries (it is assumed that there is no climate change policy in the developing countries).

These four options are: trade-based border tax (the specific carbon tax of the _importing_ country is applied on the carbon content of the _exporting_ country) on imports from the developing countries; specific-based border tax (the specific carbon tax of the _importing_ country is applied on the carbon content of the _importing_ country) on imports from the developing countries; a “two-way” border tax which combines the imposition of a specificbased border tax on imports from developing countries with the elimination of the carbon tax imposed by the exporting countries on their exports; and no border tax on imports.

**Table 2.  Impact of alternative border tax regimes on total industrial exports, percent changes**

|Border tax regimes [a]|U.S.|EC|Developing<br>countries[b]|Brazil|China|India|
|---|---|---|---|---|---|---|
|1. BT only on imports (trade‐based)|‐10.1|‐23.2|‐14.8|1.9|‐20.8|‐16.0|
|2. BT only on imports (specific‐based)|‐6.5|‐6.6|‐3.2|‐2.5|‐3.4|‐3.2|
|3. Two‐way BT (specific‐based)|0.0|0.5|‐2.0|‐0.6|‐1.8|‐2.1|
|4. No BT|‐2.3|‐2.1|‐0.1|1.0|‐0.9|‐0.3|

Source: Mattoo et al. [2009]. Developed countries are assumed to reduce unilaterally their emissions by 17 percent. Notes: [a] BT: border taxes. Two-way BT: elimination of the carbon tax imposed by the exporting country combined with the imposition of the carbon tax imposed by the importing country. Trade-based and specific-based: see Table 1. [b] Low and middle income developing countries.

Table 2 provides three main results:

- clearly, mutual destruction is a possibility:  trade-based border taxes on imports deteriorate massively the situation of almost all the countries, to the point to put at risk world growth, hence the willingness and/or capacity to pursue climate change policies.

- the impact of specific-based border taxes may be less dramatic, but it hurts notably most developing countries, ensuring political international turmoil.

15
Construction and building depreciation however, take time so that the capital invested does not translate immediately into new buildings or the destruction of old ones. We here follow Viguié et al. (2014) in supposing that financial investments are transformed into buildings with a time lag �� , which corresponds to the time required to achieve the construction. We also suppose that a decrease in current built floor space ratio to land ( h(�) ) can happen through depreciation only, with the timescale ��� . It follows that in a dynamic perspective housing evolution at each time step is given by: <u>Step 2: At each time step the other model variables (rents, population densities, dwelling sizes...) adjust</u> perfectly to the new socio-economic conditions and to the new housing stock. Indeed households adjust their locational and consumption choices in a way that ensures that utility levels are equal for all households throughout the urban area, consistently with the static version of the model. This second step of the dynamics of NEDUM-2D is equivalent to the maximization of households’ utility in a static framework with an exogenous housing stock.

# 10.1.5 Discussion of the mono-centric assumption

In the present study, we use this model with the assumption that all households commute every day to the center of the _Ciudad Autónoma de Buenos Aires_ (CABA), where jobs are assumed to be located. This mono-centric hypothesis is important, and can seem a strong assumption as secondary employment centers exist within the urban area of Buenos Aires. However, the Buenos Aires region displays a very strong mono-centric structure. For instance, in 2012 more than 40%<sup>14</sup> of all jobs in the Buenos Aires region are localized in CABA even though CABA only represents 5.3% of _Region Metropolitana_ ’s area. Map 9 represents the employment densities for each census tract or “radio” in the Buenos Aires region. The use of employment densities instead of absolute employment numbers can correct a statistical “radio size” bias. Indeed, as radios have different sizes, larger radios would appear as very “employment-rich” while smaller radios will display only small employment numbers.

> 14 Projections based on Censo Nacional Economic 04/05 (CNE 04/05) and Encuesta Permanente de Hogares, 2011 (EPH, 2011).

41
# **EXECUTIVE SUMMARY**

This study reviews the Chilean Government’s substantial investment in small and medium enterprise (SME) development programs and institutions. Given the role of SMEs in employment and the possibility of improving their integration into the national economy and export-oriented production and marketing chains, this study further identifies areas where SMEs can more effectively leverage government programs to create more productive, efficient, and innovative SMEs.

The analysis is based on research completed in 2003. Research included interviews with key policymakers, managers and other staff of the Government institutions responsible for most of the programs, secondary reports, discussions with small business managers, business association leaders, academicians and financial intermediaries.

The report is organized as follows: Section 1 reviews the evolution of the macroeconomic and business environment in Chile, and provides the theoretical arguments upon which the government’s intervention in favor of SMEs has been based. Section 2 examines the characteristics of the SME sector and key determinants of productivity and growth. Section 3 describes the obstacles to SME development and compares Chilean venture capital markets with international experiences. Section 4 describes recommendations for a streamlined portfolio of private sector assistance projects and provides an institutional analysis of CORFO, INDAP and SERCOTEC. Section 5 focuses on programs that promote innovation, technology and networks to improve the effectiveness of the Government’s investment in SME programs.
# Box 4. **Additional Principal Criteria For a Tax Benchmark**

(an example for a middle income country case) The normative tax benchmark should

- Represent a consistent tax treatment of similar activities or classes of taxpayers and neither favor nor disadvantage similarly placed activities or classes of taxpayers.

- Include certain tax provisions (such as exemptions, deductions, tax credits, and other tax preferences) to adjust taxable income in order to:

- comply with the ability-to-pay principle;

- enhance the economic and collection efficiency of taxation;

- simplify or make feasible tax administration with respect to a class of taxpayers or type of activity.

- Ensure that tax expenditure report provides sufficient information for policy formulation.

# **Building block 2. Estimating and projecting tax expenditures**

The estimating/accounting tax expenditures gives a quantitative measure of the size of tax expenditures and their impacts on revenue loss, direct budget expenditures, and budget balance, in addition to providing data for cost-effective analysis in tax expenditures. Projections of tax expenditures provide similar information for the medium term budget framework (MTBF).

Methods of estimating and projecting tax expenditures used include revenue foregone, revenue gain, and outlay equivalent methods. However, In the OECD and most countries’ experience, the revenue foregone method is usually recommended for estimating tax expenditures.

The revenue foregone method estimates the cost of a tax expenditure as the deviation between the tax paid by taxpayers affected by a specific tax provision and tax paid by similar taxpayers not affected by that provision. The estimated value of tax expenditure is the economic value on an accrual basis and is consistent with the principal of the Government Finance Statistics (GFS) and harmonized with statistics of the 1993 System National Accounts (SNA) for revenue estimates (on accrual basis) in GFS. However, that method does not take into account changes in taxpayers’ behavior.

The revenue foregone method estimates tax expenditures one at a time. It does not account for the change from the removal of one provision that may result in increased use of other provisions, lowering tax revenue elsewhere. Also, the revenue foregone method does not take into account that the removal of a tax expenditure may result in some taxpayers moving into a higher marginal tax bracket under a progressive income tax system. Those issues, however, could be tackled down with the development of technology.

21
# TKE 10: AIEt EFFECTS OF WBA FWVISIONISS (hu is. lo7)

|||EAN|INGS PEKR<br>S|<br>i||||O<br>V|ALIE KR9|||S|TOCK FRI(2||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||sTOCK||||**S**aCH|R|W|EM END||s**0**||*FORE|_E||
|CaW|sueom|1906|IsWE|lseeE|87E/66|6|IN&|191117|E_IO11E_|lE/5|_EMAI_|AWITION<br>lI|<br>S07|-|
|CIticorp ti||67.13|(67.25)|612.60|-2025|775|855.83|645.68|655.70|-17.65|-o.S|60.43|662.03|<br>23.701|
|brthust|MOE|3.16|_(1.25)_|6.00|-13S5|05|3.42|3S.37|39.57|-7."|3.05|36.13|44.20|13.105|
|Chow|Of|6.50|(1O0.o)|10.00|-250|**5n4**|**52.95**|40.49|46.35|<br>23.55|4.75|37.25|44.124|10.465|
|ScurityPacfic||4SPC<br>*.6|1.45|5.90|-705|211|35.25|32.90|37.00|-1.15|<br>11.3s|3e.30|<br>42.000|<br>9.4,5|
|RAlulr|A<br>R|3.41|1.75|N.A.|<br>**-4**|--|26.05|27.44|N.A.|<br>2.|--|47.755|52.50|09.ff5|
|larcatile<br>Beworp|NIAC|3.57|0.1s|4.00|**-96%**|**125**|29.50|**20.33**|**3O.9**|**-4.2**|4.65|24.50|26.7M|9.105|
|SarcasisNational|NM|2.4s|1.10|3.05|<br>-555|245|21.56|21.90|24.5S|I."|U2."|26.75|26.750|0.005|
|**Sobo**e<br>f<br>St|e|3.49|**1.00**|4.65|**-71is**|**33**|24.47|24.47|26.12|**0.05**|14.9|**34.36**|36.750|6.915|
|_fa oat Awics_|SAC|(3.74)|(6.00)|2.00|60ox|N..|21.49|15.49|17.49|-27."|-16.65|11.50|11.875|**3.m2**|
|Chbic.l<br>MmYor**Crp**|04L|7.42|(14.031|9.50|_-209_|205|56.14|**39.39**|46.17|29.65|-17."|43.60|46.500|5.905|
|First<br>InterstateBancorp|<br>I|7.50|(3.65)|<br>6.75|-151|175|56.65|52.00|57.95|-11.3|-1.2|56.13|59.20|5.575|
|Flrst Chicepo|ft|5.00|16.00)|7.00|-2605|405|36.91|27.41|32.91|25.7$|-IO."|30.00|32.000|6.675|
|Ssilbrut<br>_Corp_|SHAS|5.07|(3.65)|6.15|-1765|215|39.56|33.47|37.50|-15.05|-4.55|46.50|47.375|-2.325|
|=Saecorp||_3M_<br>3.65|3.45|4.65|-55|21n|39.245|31.4"|34.94|7.7n|19.55|36.75|56.5il|4.665|
|Slbelo Sak Corp|SEL|6.20|(10.00)|7.25|-390|175|63.66|44.79|50.13|-30.55|-21.3|35.13|56.625|4.275|
|Manufacturers<br>Ianovrs|OM|6.00|(26.25)|14.00|-390|59|74.32|44.79|55.51|-39.7|-25.35|44.00|45.000|0.26S|
|Catsrrs<br>Bancrp<br>|ClBC|3.90|3.00|5.00|-235|265|43.47|44.67|47.67|2.65|10.15|37.25|36.00i0<br>|<br>2.015<br>|
|Sekers Trust N.V.Corp.|eT|6.01|(2.151|9.00|-1365|-50|36.76|34.95|42.27|-9.91|9.05|54.25|53.750|-0.925|

Sourcse Kasf., Sruystt. and Seod Inc.. Smscen, Na York.

N.A. at pllcble (IRlr to bs ecqulred bv Security Pacific.

N.M. Nbt eseingful