rafmacalaba/datause-encoder-data huggingface dataset:
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Gomez-Ibañez, J. and F. Ruiz Nuñez (2006), Inefficient Cities. Mimeo.

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ISDR (2009), Global Assessment Report, need full reference Jain, Sudhir (2005), The Indian Earthquake Problem, _Current Science,_ vol. 89, no. 9: 1464-1466.

Kahn, Matthew E. (2005), The death toll from natural disasters: the role of income, geography and institutions, _Review of Economics and Statistics_ , 87(2), 271-284.

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46
|ities in Colombia vs. Municipalities in Manufacturing Sample<br>Excluded from Manufacturing Sample<br>Within Manufacturing Sample|6,232.21<br>19,045.86|3,457.95<br>87,574.27|9,645.72<br>106,599.10|7,585.76<br>85,112.57|24.10<br>21.23<br>65,235.67<br>1,058,189.00|0.51<br>0.52|577.70<br>1,885.52|806<br>317|
|---|---|---|---|---|---|---|---|---|
|all Municipal<br>All sample|9,862.67|27,313.09|37,038.08|31,144.88|23.29<br>359,164.90|0.51|347.74|1123|
|Mean Values of<br>an values)|tion|ation|tion|Population|lity<br>DP|s|eceived (IDPs)|palities|
|<br>e|a|l|a|e|a<br>G|e|R|i|
|m|ul|pu|ul|Ag|rt<br>l|at|ls|ic|
|(|p|o|p||o<br>a|R|a|n|
|e|o|P|o|g|M<br>p||u|u|
|Variabl|Rural P|Urban|Total P|Workin|Infant<br>Munici|Poverty|Individ|N of M|

49
The addition of 25.9 million female workers during 1999-00 to 2004-05 was mainly due to a surge in FLFP in rural areas. The drop in FLFP during the later period of 2004-05 to 2009-10 was also mainly due to the withdrawal of rural females. There was a decline of 0.98 million workers in urban areas between 2004-05 and 2009-10, while 2.7 million joined the labor force during the period of 2009-10 to 2011-12, leading to an absolute decline of 19.16 million during the period of 2004-05 to 2011-12.

2.3 Comparison of Estimates of FLFP with Prior Studies Over the past few years, several papers have examined the puzzle of this unprecedented drop in FLFP during 2004-05 and 2011-12 in India. To begin, we first discuss prior estimates of FLFP in India and then compare our estimates with those of previous studies. We use various ‘ _EmploymentUnemployment_ ’ rounds of the NSSO as the principal data source for estimating trends in FLFP. From prior studies based on Usual Principal and Subsidiary Status (UPSS), participation is understood to have increased by 27.0 million female workers between 1999-2000 and 2004-05 (Rangarajan et al., 2011). Estimates by Himanshu (2011) and Abraham (2013) closely follow the above figure at 26.7 million and 26.5 million, respectively. Our own estimates follow suit, suggesting an increase of 25.9 million. In the following period, 2004-05 to 2009-10, female labor participation was reported to have declined to the extent of 21 to 22 million workers (Rangarajan et al., 2011; Himanshu, 2011; Kannan and Raveendran, 2012). Overall, there is consensus among various studies that there was a relatively large rise in labor participation — to the tune of about 26 million to 27 million during the period of 1999-2000 to 2004-05 — followed by a considerable decline during the period of 2004-05 to 2009-10 (Table 5). These studies corroborate our estimates in Table 4, of the rise and drop in FLFP between 1993-94 and 2004-05 & 2004-05 and 2011-12, respectively.

9
influenced by the initialization of the residual vectors. However, as _T →∞_ , the initialization effect fades and eq. (35) converges to the _true R_<sup>2</sup> . In our empirical application we work with small _T_ , and we suggest that eq. (35) provides a crude approximation that is still useful to inform the researcher on the degree of explained in-sample variance.

# **5 Application to Subnational Pollution and Household Income Data in Indonesia**

In this application we study interactions between household level expenditures and pollution. It has long been theorized that as economies develop, pollution initially increases at an exponential rate. However at some point on the development path, parts in the economy start to adopt cleaner technologies and acceleration in pollution slows down till pollution levels reach a maximum after which the entire economy enters into a state characterized by a decline in pollution. We do not aim to provide a large survey of the literature, for a progression of the debate, see (World Bank, 1992; Grossman and Krueger, 1995; Stern et al., 1996; Stern, 1998, 2004; Andree et al., 2018). For many, the central question is whether increases in wealth and income result in increasing pressure on the environment, or whether economic development provides the basis for environmental improvement. In turn, environmental degradation may negatively interact with growth and contribute to the creation of urban pollution traps. In this application we revisit the empirical issue and focus on the question whether pollution increases or decreases after income. Furthermore, we are interested in the order of effects, the presence of feedback, and distributional impacts of effects. We therefore focus our study on air pollution, average per capita household expenditures, and bottom quintile per capita household expenditures and explore the interactions in the context of multiple spatial time series in Indonesia over the period 1999-2014. We seek to distinguish between the effects of average household growth and bottom household growth on pollution and see if there is differential in potential impacts of pollution on the two different income groups.

## **5.1 Data**

Our analysis relies on two longitudinal data sets. As a proxy for air pollution, we use the global estimates of fine particulate matter developed by van van Donkelaar et al. (2016). We 22
the elasticity of trade to distance from the early twentieth century are similar to what is found today, leading Frankel to comment that “we have more than a century of gravity estimates, and nowhere is there evidence of a decline over time in the distance coefficient” (Frankel 1997, p. 72). Disdier and Head (2003) perform a meta-analysis on estimates of the distance coefficient using data from 51 papers that employ gravity equations. Their results imply that there is an increasing impact of distance on trade on the order of about 20 percent since 1965. In related work, Brun et al. (2002) estimate a panel gravity model over the period 1962-1996 and find that the coefficient on distance has increased in absolute value by 11 percent over the last 35 years.

In contrast, Rauch (1999), also using a gravity equation on data disaggregated by market type, finds that the effect of distance on trade has declined since 1970. Similarly, Leamer and Levinsohn (1995) find that the share of trade between adjacent countries declined from 1970 to 1985, suggesting distance has become somewhat less important— though their results exclude income effects. An alternative way to measure integration is by examining relative price movements. Engels and Rogers (1998) examine correlations in relative prices across countries and also find evidence that markets have become more integrated over large distances.

Different explanations for the persistence of distance as a determinant of trade flows abound. Regionalism is the most obvious explanation, but even studies controlling for free trade agreements find an increased importance of distance. Leamer and Storper (2001) argue that personal contact is important, thus the effect of distance will change very little from important advances in communications such as the Internet.<sup>2</sup> They offer

> 2 Indeed, Freund and Weinhold 2003 do not find evidence that the Internet has altered the effect of distance on trade.

5
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MMA (McLennan Magasanik Associates Pty Ltd.) (1999). “Projections of Price of Renewable Energy Certificates to Meet the 2% Renewable Energy Target”. Report to the Australian Greenhouse Office. [In Skytte, K., Meibom P., Uyterlinde M.A, Lescot, D., Hoffmann, T., del Rio P., (2003). “Challenges for Investment in Renewable Electricity in the European Union”. Background report in the ADMIRE REBUS project supported by the European Commission. November 2003.] MRC (Marbek Resource Consultants) (2004). Administration and transaction cost estimates for greenhouse gas offset system. Report submitted to Strategic Policy Branch Agriculture and AgriFood Canada by Marbek Resource Consultants in association with PricewaterhouseCoopers and The International Institute for Sustainable Development. 8 January 2004.

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Ménard, C. (2004). “Transaction Costs and Property Rights” Edited by Claude Ménard. The International Library of the New Institutional Economics. Volume 2. Edward Elgar Publishing.

Mettepenningen, E., and van Huylenbroeck, G. (2009). “Factors influencing private transaction costs related to Agri-environmental Schemes in Europe”. In: Bruwer, F., van der Heide, M. (Eds.), Multifunctional Rural Land Management: Economics and Policies. [In Coggan et al., 2010] Metz, B. Davidson, O. Bosch, P. Dave, R. and Meyer, L. (Eds.) (2007). “Climate Change 2007: Mitigation. Contribution of Working III to the Fourth Assessment Report to the Intergovernmental Panel on

- 36 -
|**Country**|_it_<br>_Y_ (Tax/GDP)|_it_<br>_Y_ (Rev/GDP)|_it_<br>_GDP_|_it_<br>_POP_|_it_<br>_TRADE_|_it_<br>_AGR_|_it_<br>_CORR_|_it_<br>_BUREAU_|
|---|---|---|---|---|---|---|---|---|
|Portugal|33.5|37.4|9,588.3|0.4|67.5|4.6|-7.5|-7.5|
|Romania|22.5|25.8|1,745.6|-0.5|66.0|17.3|-5.1|-2.5|
|Russian Federation|22.7|28.9|1,818.1|-0.3|62.4|6.6|-3.4|-3.5|
|Rwanda|9.2|10.6|234.8|2.0|34.4|42.0|...|...|
|Senegal|16.4|17.3|412.0|2.5|66.9|18.9|-4.8|-3.5|
|Seychelles|35.4|44.3|6,792.4|1.3|145.3|3.3|...|...|
|Sierra Leone|9.3|9.7|173.0|2.0|48.7|50.2|-3.5|-0.2|
|Slovak Republic|30.7|35.2|3,575.9|0.1|134.8|4.8|-5.9|-7.7|
|Slovenia|36.7|39.0|8,823.0|0.0|112.2|4.0|-5.8|-7.5|
|South Africa|25.3|26.8|3,030.0|1.8|49.1|3.8|-6.1|-6.7|
|Spain|25.9|28.6|13,444.8|0.7|50.1|4.9|-7.0|-8.3|
|Sri Lanka|16.2|18.3|783.8|0.9|79.7|21.6|-5.6|-5.0|
|St. Kitts And Nevis|24.6|31.5|6,915.5|0.9|120.9|4.6|...|...|
|St. Vincent and the Grenadines|23.8|28.6|2,781.4|0.6|117.6|11.7|...|...|
|Sudan|6.2|7.5|349.6|2.2|29.7|41.0|-2.2|-2.5|
|Swaziland|26.0|27.4|1,324.1|2.6|173.5|14.9|...|...|
|Sweden|35.2|39.6|25,078.9|0.3|74.8|2.3|-9.6|-10.0|
|Switzerland|19.5|22.2|32,926.5|0.6|74.4|1.9|-8.6|-10.0|
|Syria|17.3|22.9|1,100.2|2.6|67.5|26.5|-5.2|-3.5|
|Tajikistan|10.3|11.3|188.2|1.3|120.1|28.6|...|...|
|Thailand|16.5|19.5|2,012.2|1.1|103.8|9.7|-3.8|-6.4|
|Trinidad And Tobago|23.1|27.1|5,935.3|0.5|94.5|1.8|-4.6|-6.5|
|Tunisia|25.5|29.7|1,906.0|1.4|90.9|12.8|-4.5|-5.0|
|Turkey|15.7|19.3|2,822.5|1.7|50.7|15.3|-4.6|-6.0|
|Uganda|11.1|11.4|227.4|3.2|34.4|41.4|-3.9|-4.0|
|Ukraine|23.1|27.8|751.4|-0.7|92.2|15.6|-3.1|-2.5|
|United Kingdom|35.5|37.0|22,876.3|0.3|55.0|1.4|-8.0|-10.0|
|<br>United States|17.7|18.3|32,420.3|1.1|23.4|1.5|-7.5|-10.0|
|Uruguay|25.6|28.1|5,890.9|0.7|41.7|7.9|-5.0|-4.3|
|Vanuatu|19.4|22.3|1,255.2|2.3|101.3|15.9|...|...|

- 25 -
**<u>Table A.15. The placebo test of the program effect on individuals’ outcomes using the threshold of the poverty rate at 30%</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|Log of<br>monthly<br>wage (wage<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Log of<br>education<br>subsidy for<br>students<br>(children<br>aged 6-17)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Districts with the 2006 poverty above|-0.0083|-0.0027|-0.0028|-0.1095|0.2970|0.3128|0.0201|0.0088|
|30% * Year 2008|(0.0370)|(0.0178)|(0.0273)|(0.2217)|(0.2287)|(0.2325)|(0.0170)|(0.1262)|
|(Poverty rate – 30) * Year 2008|0.0045|-0.0003|-0.0033|-0.0292|-0.0084|-0.0075|-0.0011|0.0239|
||(0.0046)|(0.0023)|(0.0026)|(0.0192)|(0.0196)|(0.0187)|(0.0021)|(0.0168)|
|Districts with the 2006 poverty above|-0.0151**|0.0011|0.0136***|0.0466|-0.0420|-0.0443|-0.0007|0.0193|
|40% * (Poverty rate – 30) * Year 2008<br>Age|(0.0069)<br>0.0333***|(0.0036)<br>0.0091***|(0.0050)<br>0.0091***|(0.0413)<br>0.1083***|(0.0363)<br>-0.0091***|(0.0363)<br>-0.0084***|(0.0033)<br>0.1542***|(0.0330)<br>0.1161***|
||(0.0009)|(0.0005)|(0.0005)|(0.0044)|(0.0026)|(0.0024)|(0.0063)|(0.0194)|
|Age squared|-0.0003***|-0.0001***|-0.0001***|-0.0015***|0.0004***|0.0003***|-0.0079***|-0.0058***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0000)|(0.0003)|(0.0009)|
|Male|-0.0779***|-0.0233***|0.1222***|1.3178***|-0.1633***|-0.1571***|0.0129***|-0.0144|
||(0.0103)|(0.0047)|(0.0054)|(0.0473)|(0.0187)|(0.0169)|(0.0048)|(0.0138)|
|Ethnic minorities (yes=1, Kinh=0)|0.0921***|-0.0585***|0.0180*|0.2516***|-0.0552|-0.0600|-0.0869***|0.7475***|
||(0.0122)|(0.0056)|(0.0095)|(0.0865)|(0.0565)|(0.0537)|(0.0097)|(0.1506)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.0476**|-0.0664***|-0.0327***|-0.3447***|0.6606***|0.5970***|0.2556***|-0.3239***|
||(0.0197)|(0.0082)|(0.0105)|(0.0982)|(0.0321)|(0.0302)|(0.0333)|(0.1140)|
|Observations|251,539|251,539|251,539|251,539|167,838|167,838|80,003|80,003|
|R-squared|0.171|0.057|0.134|0.132|0.118|0.117|0.196|0.305|

Note: This table reports district fixed-effect RDD-DD regressions of employment of individuals using individual-level observations. The placebo treatment includes districts with the 2006 MOLISA poverty above 30%. The sample consists of individuals living in districts with the 2006 MOLISA poverty rate between 20% and 40%. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

65
Figure 16: Impacts on business practices by time spent on Chaldal <!-- Start of picture text --> Estimated coefficient on time spent by firm on Chaldal<br>with 95% CI, for various outcomes<br>Marketing Accounting Packaging<br>Digital technology Logistics Facilities<br>Index of business practices<br>.4<br>.3<br>.2<br>.1<br>0<br><!-- End of picture text --> A similar association with tenure is observed in the case of Daraz. Given size and age, sellers that have spent more years on Daraz are more likely to report an increase in sales (figure 17). They are also more likely to report using Daraz features such as order management, product reviews, customer messenger, financial statements and promotion tools (figure 18).<sup>19</sup>

> 19The plotted estimates of the coefficient on time spent on Daraz are from the regressions shown in tables A.9 and A.11.

23
Table 2: Characteristics of sellers on Daraz

||Mean|Standard Deviation|
|---|---|---|
|How many workers work at this business?|3.5|14.13|
|Firm age (years)|3.4|5.17|
|Time on Daraz (Years)|1.6|1.31|
|Owner’s age|29.9|8.25|
|Owns motorcycle|0.6|0.49|
|Owns car|0.3|0.44|
|Owns refrigerator|0.8|0.37|
|Owns smart phone|1.0|0.13|
|Owns land|0.6|0.50|
|Registered|0.7|0.44|
|VAT reg.|0.3|0.44|
|Observations|1842||

Figure 3: Daraz vendor size (Number of employees) <!-- Start of picture text --> 86.9<br>11.0<br>1.7 0.4<br>1-5 6-19 20-99 >=100<br>Number of workers (including owner)<br>100<br>80<br>60<br>40<br>Share of total firms (%)<br>20<br>0<br><!-- End of picture text --> In contrast to the Chaldal vendor sample, many firms in the Daraz sample show signs of low levels of formality. Only 25.9 percent have VAT registration. 9 Among the firms, 72.7 percent operate either out of the owner’s home or in a separate building on the same lot, and 43.2 percent have family involved in the business, including 15.1 percent which use other family members as unpaid workers. For many business owners the business is part time: 53.3 percent have another job

> 9Moreover, while 56 of the Daraz sample firms are registered as sole proprietorship companies, 25.4 percent do not have a formal legal structure.

9
**Table 6.** Change in Real GDP (Scenario vs. BAU; DR = 70%)

|Year / TR|Lump-sum transfer|cutlb|subew|
|---|---|---|---|
|2035|-0.02%|0.03%|-0.02%|
|2040|-0.10%|0.03%|-0.10%|

TR: Tax revenue recycling scenario (cutlb = cutting labor tax; subew = subsidizing wind power generation) Assuming PNMC is fully effective (DR = 70%). The subsidy rate cannot exceed 50%. The remaining carbon tax revenue is given back to the household.

**Table 7.** Change in Real GDP (Cutting labor tax vs. Lump-sum transfer)

|Year / DR|70%|52.5%|35%|
|---|---|---|---|
|2007|0.00%|0.00%|0.00%|
|2010|0.00%|0.00%|0.00%|
|2015|0.00%|0.00%|0.02%|
|2020|0.00%|0.09%|0.24%|
|2025|0.00%|0.19%|0.26%|
|2030|0.00%|0.26%|0.28%|
|2035|0.05%|0.28%|0.32%|
|2040|0.14%|0.34%|0.35%|

**Table 8.** Change in Sectoral Output (Scenario vs. BAU; DR = 70%)

|TR|cutl|b|sub|ew|
|---|---|---|---|---|
|Year|2035|2040|2035|2040|
|Other Agriculture|0.05%|0.00%|0.06%|0.03%|
|Sugarcane industry|-0.07%|0.10%|-0.10%|0.07%|
|Soy beans industry|0.14%|0.23%|0.08%|0.09%|
|Forest sector|-0.22%|-0.24%|-0.22%|-0.26%|
|Other industry|-4.44%|-0.78%|-4.38%|-0.99%|
|Electricity: Hydro|-0.60%|-1.30%|-0.64%|-1.36%|
|Electricity: Nuclear|-0.60%|-1.29%|-0.64%|-1.36%|
|Electricity: Coal|-3.28%|-7.84%|-3.22%|-7.68%|
|Electricity: Gas|-1.51%|-3.51%|-1.53%|-3.49%|
|Electricity: Oil|-2.11%|-4.95%|-2.10%|-4.89%|
|Electricity: Wind|-0.64%|-1.39%|10.19%|9.42%|
|Electricity: Biomass|-0.12%|-0.04%|-0.14%|-0.07%|
|Service sector|0.98%|2.43%|1.02%|2.61%|

TR: Tax revenue recycling scenario (cutlb = cutting labor tax; subew = subsidizing wind power generation) Assuming PNMC is fully effective (DR = 70%). The subsidy rate cannot exceed 50%. The remaining carbon tax revenue is given back to the household.

23
Policy Research Working Paper 6734

# **Abstract**

This paper studies the impact of gender and ethnicity on educational outcomes using cross-country evidence from Bolivia, Mexico, Peru, Senegal, and Sierra Leone. It uses the Minnesota Population Center’s Integrated Public Use Microdata Series-International database, which includes individual-level data from large, harmonized, and representative samples of country censuses. Using an estimation method analogous to difference-indifferences, the paper finds that gender-based differences in literacy, primary school completion, and secondary school completion are larger for minority ethnic groups compared with others or, alternatively, ethnicity-based differences are larger for women compared with men. The findings suggest that the intersection of gender and ethnicity confers cumulative disadvantage for minority groups, especially in Latin America. The paper discusses the implications of these findings on the design of, targeting in, and resource allocation for development programs.

This paper is a product of the Social Development Unit, Sustainable Development Network. 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://econ.worldbank.org. The authors may be contacted at etas@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 its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team