rafmacalaba/datause-encoder-data huggingface dataset:
sector-occupation-formal status) changes in inequality remain a dominant factor, explaining 42 percent of the reduction in the variance between 2002 and 2015.

Using detailed administrative data for the formal sector in Brazil, Costa Rica, and Ecuador, our analysis also shows that firm effects accounted for a larger share of the change in wage inequality than worker effects. Interestingly, in Brazil and Ecuador, where wage inequality fell, differences across firms narrowed over time, whereas in Costa Rica, where wage inequality increased, these differences widened. Further, in all three countries, most of the observed changes in wage inequality were accounted by changes between rather than within firms. The reduction in inter-firm wage differentials in Brazil and Ecuador is in sharp contrast with their expansion in Germany, as documented by Card, Heining, and Kline (2013) and in the United States, as documented by Song et al. (2018). Understanding the large within-group shares in inequality decline and the potential role of between-firm differentials are promising avenues for further research.

# **References**

- Abowd, J., R. Creecy, and F. Kramarz. 2002. “Computing Person and Firm Effects Using Linked Longitudinal Employer-Employee Data.” Technical Paper TP-2002-06. Washington, DC, United States: U.S. Census Bureau, Center for Economic Studies.

- Abowd, J., F. Kramarz, and D. Margolis. 1999. “High Wage Workers and High Wage Firms.” _Econometrica_ 67(2): 251–333.

- Acemoglu, D. 1999. “Changes in Unemployment and Wage Inequality: An Alternative Theory and Some Evidence.” _American Economic Review_ 89(5): 1259–78.

- Acemoglu, D., and D. Autor. 2011. “Skills, Tasks and Technologies: Implications for Employment and Earnings.” _Handbook of Labor Economics_ . Volume 4B: 1043–1171. Amsterdam, The Netherlands: Elsevier.

- Ad ̃ao, R. 2015. “Worker Heterogeneity, Wage Inequality, and International Trade: Theory and Evidence from Brazil.” Cambridge, United States: Massachusetts Institute of Technology. Unpublished paper.

- Alvaredo, F., L. Chancel, T. Piketty, E. Saez, and G. Zucman. 2017. “Global Inequality Dynamics: New Findings from WID.world.” _American Economic Review_ 107(5): 404–09.

- Alvaredo, F., and L. Gasparini. 2015. _“Recent Trends in Inequality and Poverty in Developing Countries.”_ In: A. B. Atkinson and F. Bourguignon, editors. _Handbook of Income Distribution_ . Amsterdam, The Netherlands: Elsevier.

- Alvarez, J., F. Benguira, N. Engbom, and C. Moser. 2018. “Firms and the Decline in Earnings Inequality in Brazil.” _American Economic Journal: Macroeconomics_ 10(1): 149–89.

36
The 2006-2013 time period includes the global financial crisis of 2007. Turkey, similar to many countries, was impacted negatively from the crisis both in terms of growth and poverty. Therefore it is useful to look at Turkey’s poverty reduction performance more specifically in two sub-time periods; the crisis period<sup>12</sup> 2006-2009 and the post-crisis period 2009-2013.

Between 2006 and 2009, with the effect of the crisis, only 7 of 26 NUTS2 regions reduced poverty in Turkey (Figure 15). The Mediterranean region was clearly the best performer in this time period as TR61, TR62 and TR63 all achieved significant poverty reduction despite the ongoing crisis. On the other hand, TR10-Istanbul, and TR41 and TR42 (industrial provinces in Istanbul’s periphery) took the most substantial hit from the financial deterioration. Regional variance in poverty reduction between the more agricultural and services based South Coast versus the industrial Marmara region suggests that falling into poverty during the crisis was more likely in the industrial powerhouses of Turkey. However, NUTS2 regions in the more agricultural Black Sea Coast, and Eastern Turkey which has the highest share of workers in husbandry also experienced sizable increases in poverty rates.

In the high growth post-crisis period, Turkey was able to recover impressively as 24 of the 26 NUTS2 regions decreased their poverty rates (Figure 16). Interestingly, regions in the Mediterranean were relatively less successful in reducing poverty after 2009. The Black Sea coast, Central Anatolia (heartland of Anatolian tigers), as well as the industrialized west decreased poverty substantially. On the other hand, Eastern Turkey had subpar performance in poverty reduction. NUTS2 regions in East Turkey were the four least successful performers in the post-crisis period, two of which (TRA1 and TRB1) experienced an increase in their poverty levels.

As Figure 4 shows, there are some classifiable differences in the poverty reduction performance across regions. First, the majority of NUTS2 regions are located in the lower left quadrant, where initial poverty in 2006 is lower and there is a high level of poverty reduction. Istanbul (TR10) stands out as the only region with low initial poverty but experienced a significant increase in poverty from 2006 to 2013. In the data, a natural break in poverty rates exists for Eastern regions that have much higher rates of poverty. However, TRC regions in the East performed better, experiencing high levels of poverty reduction. The remaining regions experienced either very low levels of poverty reduction or increasing poverty.

> 12 This could be further divided in to pre-crisis, crisis, and post-crisis period, however, data availability only allows for this division at the NUTS2 level.

9
**Figure 3.1: Central American comparisons for equity of coverage**

<!-- Start of picture text --> (a) Electricity<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>90<br>80<br>70<br>Guatemala<br>60<br>Nicaragua<br>50<br>Panama<br>40<br>El Salvador<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Consumption quintile<br>(b) Water<br>100<br>90<br>80<br>70<br>Guatemala<br>60<br>El Salvador<br>50<br>Nicaragua<br>40<br>Panama<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Consumption quintile<br>(c) Sanitation<br>100<br>90<br>80<br>70<br>Guatemala<br>60<br>El Salvador<br>50<br>Nicaragua<br>40<br>Panama<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Consumption quintile<br>(d) Telephone<br>100<br>90<br>80<br>70<br>Guatemala<br>60<br>El Salvador<br>50<br>40 Nicaragua<br>Panama<br>30<br>20<br>10<br>0<br>1 2 3 4 5<br>Consumption quintile<br>Percentage of households<br>Percentage of households<br>Percentage of households<br>Percentage of households<br><!-- End of picture text --> _Sources_ : El Salvador (Encuesta de Hogares de Propósitos Múltiples 1997); Guatemala (ENCOVI 2000, Instituto Nacional de Estadística - Guatemala); Honduras (Encuesta Nacional de Ingresos y Gastos de los Hogares, 1999); Nicaragua (LSMS 1998-99); Panama (LSMS 1997).

17
_Table 2: Inequality decompositions for 1993 and 1998, and change 1993-98_

||Coef|ficients|Me|ans|Elast|icities|Concentrat|ion indices|Con|tributions t|o C|
|---|---|---|---|---|---|---|---|---|---|---|---|
||1993|1998|1993|1998|1993|1998|1993|1998|1993|1998|Change|
|Child's age (in months)|0.038|0.039|60.982|66.962|1.137|1.630|0.020|0.018|0.023|0.030|0.007|
|Child's age squared|0.000|0.000|4883.834|5616.139|-0.634|-0.880|0.030|0.028|-0.019|-0.025|-0.006|
|Child = male|0.086|0.143|0.514|0.506|0.022|0.045|0.003|0.014|0.000|0.001|0.001|
|Household|-0.261|-0.272|7.300|7.611|-0.936|-1.288|0.038|0.040|-0.035|-0.052|-0.016|
|Safedrinkingwater|-0.023|-0.081|0.221|0.331|-0.003|-0.017|0.312|0.256|-0.001|-0.004|-0.003|
|Satisfactory sanitation|-0.128|-0.050|0.146|0.202|-0.009|-0.006|0.468|0.508|-0.004|-0.003|0.001|
|Years schooling<br>household head|_-0.005_|-0.003|6.812|7.108|-0.017|-0.015|0.065|0.094|-0.001|-0.001|0.000|
|Years schooling mother|-0.012|-0.001|6.321|6.722|-0.037|-0.003|0.075|0.108|-0.003|0.000|0.003|
|Fixed effects|||3.008|2.468|1.477|1.534|-0.024|-0.031|-0.035|-0.047|-0.012|
|Total|||||||||-0.075|-0.102|-0.027|

_Table 3: Oaxaca-type decomposition for change in inequality, 1993-98_

||Eqn<br>_AC*q_|(6)<br>_A77_<br>**_MC_**|Eqn<br>AC.r|(7)<br>**_A-C_**|To<br>Total|tal<br>%|
|---|---|---|---|---|---|---|
|Child's age (in months)|-0.003|0.010|-0.002|0.009|0.007|-30%|
|Child's age squared|0.001|-0.007|0.001|-0.007|-0.006|26%|
|Child = male|0.000|0.000|0.000|0.000|0.001|-3%|
|Household consumption|-0.003|-0.013|-0.002|-0.014|-0.016|74%|
|Safe drinking water|0.001|-0.004|0.000|-0.004|-0.003|16%|
|Satisfactory sanitation|0.000|0.001|0.000|0.002|0.001|_-5%_|
|Years schooling household<br>head|0.000|0.000|-0.001|0.000|0.000|1%|
|Years schooling mother|0.000|0.003|-0.001|0.004|0.003|-11%|
|Fixed effects|-0.011|-0.001|-0.010|-0.002|-0.012|55%|
|"Residual"|||||0.005|-24%|
|Total|-0.015|-0.012|-0.016|-0.012|-0.022||

_16_
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39
high impact intensity and others have none. The first and second moments of the spatial impact distribution also change over time: The median landfall location shifts eastward from northern Odisha to the Bangladesh/West Bengal border area, while locational variance increases markedly after 1960.

We also consider the overall distribution of impacts during the 140-year period covered by our database. Despite pronounced period-to-period shifts in impact locations, the overall distribution is far from uniform. We find the highest impact zones in northern Odisha and the Sundarbans region of West Bengal, with somewhat lower impacts in the zone between those two areas and a pattern of decline from the high-impact zones to the southern coastline boundaries of Odisha and Bangladesh. We should note that these boundary results are not artifacts of our methodology: We have also incorporated cyclone strikes in proximate areas south of the end points. Finally, despite the pronounced bimodality of the spatial distribution, we also find significant variation within zones. For example (Figure 8c), western coastal Bangladesh, generally a mid-impact zone, also has significant stretches of coast that have very low historical impacts.

To summarize, our focal coastline area has witnessed highly-varied long- and short-term distributions of cyclonic storm impacts during the past 140 years. Our database therefore incorporates three major elements that are important for research on expectations formation and adaptation: pronounced long-term impact clustering; highly-varied clustering over 20-year periods; and a pronounced west/east trend. As previously noted, such variation is necessary for using cyclone impacts as effective statistical controls in comparative adaptation studies. We conclude that our panel database can make a significant contribution to household- and community-level studies that also incorporate social, economic, demographic and political variables from the relevant regions of India and Bangladesh.

21
Local campaigns around the country to raise the minimum wage, or the election of Bill de Blasio as Mayor of New York on a platform committed to reversing the city’s growing economic divide, are indications of the immediacy and appeal of these issues. The combination of political advocacy with sensible programs and policies could make America’s cities and metro regions ever more lively testing grounds for ways to make economic change deeply and broadly inclusive.

# **Works cited**

- Alexander, Michelle. 2012. _The new Jim Crow: Mass incarceration in the age of colorblindness._ The New Press.

- Bates, Timothy. 2009. “Utilizing Affirmative Action in Public Sector Procurement as a Local Economic Development Strategy.” _Economic Development Quarterly_ 23.30: 180‐192.

- Benner, Chris, and Manuel Pastor. 2012. _Just Growth: Inclusion and Prosperity in America’s Metropolitan Regions_ . New York: Taylor & Francis.

- Blackwell, Angela Glover, and Judith Bell. 2005. “Equitable Development for a Stronger Nation: Lessons from the Field.” In _The Geography of Opportunity: Race and Housing Choice in Metropolitan America_ , edited by Xavier de Souza Briggs, 289–309. Washington, DC: Brookings Institution Press.

- Bluestone, Barry, and Bennett Harrison. 1982. _The Deindustrialization of America: Plant Closings, Community Abandonment, and the Dismantling of Basic Industry_ . New York: Basic Books.

- Briggs, Xavier de Souza. 1998. “Brown kids in white suburbs: Housing mobility and the many faces of social capital.” _Housing policy debate_ 9.1: 177‐221.

- Briggs, Xavier de Souza. 2005. “Politics and Policy: Changing the Geography of Opportunity.” In _The Geography of Opportunity: Race and Housing Choice in Metropolitan America_ , edited by Xavier de Souza Briggs, 310–41. Washington, DC: Brookings Institution Press.

- Carr, L. and Michele Mulcahy. 2010. “Rebuilding Communities in Economic Distress: Local Strategies to Sustain Homeownership, Reclaim Vacant Properties, and Promote Community‐Based Employment.” National Community Reinvestment Coalition.

- Cleveland Foundation. 2013. “Cleveland’s Greater University Circle Initiative: Building a 21<sup>st</sup> Century City through the Power of Anchor Institution Collaboratives.” Cleveland Foundation.

- Clotfelter, Charles T., Helen F. Ladd, and Jacob L. Vigdor. 2007. “Teacher Credentials and Student Achievement: Longitudinal Analysis with Student Fixed Effects.” _Economics of Education Review_ 26.6: 673–82.

- Cornia, Giovanni Andrea. 2003. “The impact of liberalisation and globalisation on within‐ country income inequality.” _CESifo Economic Studies_ 49.4: 581‐616.

- Fagan, Jeffrey, Valerie West, and Jan Hollan. 2002. “Reciprocal effects of crime and incarceration in New York City neighborhoods.” Fordham Urban Law Journal 30: 1551.

39
**Figure  2.1 Countries with largest areas irrigated by untreated and treated wastewater.**

<!-- Start of picture text --> China*<br>Mexico Treated wastewater<br>India(1)*<br>Chile(1)* China 4 million ha; out of<br>Pakistan Syria* proportion<br>Colombia Untreated wastewater<br>Argentina<br>SA*<br>Ghana*<br>Vietnam<br>Peru<br>Turkey<br>Morocco<br>Egypt(1)<br>Kuwait#<br>Sudan<br>Tunisia<br>Nepal(1)<br>Bolivia#<br>Chile<br>Mexico<br>Israel(1)*<br>Egypt*<br>Cyprus Treated wastewater<br>Italy*<br>Argentina<br>Australia<br>UAE(1)<br>USA<br>Jordan<br>Turkey*<br>Syria#<br>Tunisia<br>Kuwait(1)<br>Oman(1)<br>France<br>Libya(1)<br>S. Arabia<br>Germany<br>0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200<br>Area ( '000 ha)<br><!-- End of picture text --> _Sources:_ Scott _et al_ ., 2010; Jiménez and Asano, 2008; Xianjun _et al_ ., 2003; Xie _et al_ ., 2009. *Data uncertain; (1) Area probably underestimated; + Practice reported (including forestry), but data missing Jiménez and Asano (2008) report that globally about 20 million hectares of agricultural land is irrigated with polluted water (i.e., direct use of raw or untreated wastewater, direct use of treated wastewater, as well as indirect use of untreated wastewater). According to Pearce (2004), “a tenth of the world’s irrigated crops—everything from lettuce and tomatoes to mangoes and coconuts—are watered by sewage, and much of that sewage is raw and untreated, gushing direct from sewer pipes into fields at the fringes of the developing world's great megacities”.<sup>5</sup> The estimates suggest that the extent of unplanned wastewater irrigation (with direct and indirect use of untreated wastewater) is an order of magnitude greater than planned wastewater use (with direct use of treated water and some indirect use of untreated water) (Scott _et al_ ., 2010). In lower income countries, wastewater irrigation is predominantly unplanned and either involves the direct use of untreated wastewater or the indirect use of highly polluted waters from rivers and streams. Farmers seldom have other water sources, and unplanned wastewater use is their only means to irrigate when investments in wastewater treatment

> 5 http://www.newscientist.com/article/dn6297

6
**Table 1.A. Literature review of forecasting methods for crude oil prices**

|**Authors and**<br>**year**|**Crude oil**<br>**prices**|**Methods**|**Main findings**|**Real or**<br>**nominal**<br>**prices**|**In-sample or**<br>**out-of-sample**<br>**forecasts**|
|---|---|---|---|---|---|
|Abosedra<br>(2006)|WTI|Futures,<br>univariate<br>models|Futures and univariate models<br>are unbiased and weakly<br>inefficient.|Nominal<br>prices|In-sample<br>forecasts|
|Abosedra||||||
|and<br>Baghestani<br>(2004)|WTI|Futures, no-<br>change|Futures prices underperform<br>no-change forecasts.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Alquist and<br>Kilian (2010)|<sup>WTI</sup>|Futures, no-<br>change|Futures are not the most<br>accurate predictor of the spot<br>oil price.|Nominal<br>prices|Out-of-sample<br>forecasts|
|Alquist,<br>Kilian and<br>Vigfusson<br>(2013)|WTI|Futures, no-<br>change, VAR|VAR models have lower out-<br>of-sample forecasting errors<br>than no-change. Long-term<br>futures prices are less accurate<br>than the no-change.|Both prices|Out-of-sample<br>forecasts|
|Baumeister<br>and Kilian<br>(2014)|WTI|Futures,<br>SVAR, no-<br>change|VAR models generate more<br>accurate forecasts than futures<br>prices and no-change<br>forecasts.|Real prices|Out-of-sample<br>forecasts|
|||VAR model,||||
|Baumeister<br>and Kilian<br>(2015)|WTI|futures prices,<br>no-change,<br>forecast<br>combination<br>models|Forecast combinations<br>generate more accurate out-of-<br>sample forecasts than no-<br>change forecasts.|Real prices|Out-of-sample<br>forecasts|
|Baumeister<br>and Kilian<br>(2012)|WTI|AR, ARMA,<br>BVAR, VAR,<br>futures prices,<br>no-change|Recursive VAR forecasts have<br>lower forecast errors at short<br>horizons than other models.|Real prices|Out-of-sample<br>forecasts|
|Chen (2014)|Brent;<br>WTI|Predictive<br>regression<br>models, no-<br>change|Oil-sensitive stock prices<br>contain substantial<br>information for predicting<br>nominal and real crude oil<br>prices at short horizons.|Both prices|Both types of<br>forecasts|

22
||**Contents**|
|---|---|
|Figure 6. Factoring Volume in Selected EU 15 Countries as Percent of GDP . . . . . . . .|. . . . . . . . . . . . .<br>25|
|Figure 7. Rule of Law and Progress in Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . .<br>28|
|**Boxes**||
|Box 1. The History of Factoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . .<br>5|
|Box 2. The Role of Reverse Factoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . .<br>15|
|Box 3. Legal Frameworks for Factoring in the EU 8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . .<br>30|
|Box 4. The Factoring Industry in Poland . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . . . . . .<br>33|

**iii**

**THE WORLD BANK**
14 revenues from the ministry of health, or they may collect unofficial payments. Both constitute payments for resources used in delivering health care, and in that sense ought to be included.<sup>2</sup> In this approach, aggregate fees would be obtained from the survey, and aggregate costs for subsector _k_ would be obtained by adding government spending on the subsector (i.e. subsidies to the subsector) to the grossed‐up user fees from the survey data.

If the proportionality assumption is made, it makes no difference whether one uses the grossed‐up out‐of‐pocket payment data from the survey or the NHA figure, since everyone’s payments are scaled up or down by the same fraction and ���� is unaffected, cf. eqn (3). By contrast, if the conventional constant‐cost assumption is made, whether one uses the grossed‐ up household survey out‐of‐pocket spending total or the NHA figure _does_ make a difference. Scaling up or down the reported fees leaves ���� , ���� and _Sk_ unaffected in eqn (2), but _Ck_ and _Fk_ are both affected, _Fk_ proportionately more than _Ck_ . In the present case, where the survey leads to a larger aggregate out‐of‐pocket spending figure than the NHA, the effect of scaling down the survey data to gross up to the NHA figure is to put more weight on ���� in eqn (2). Finally, if the more general assumption of a linear relationship between costs and fees is made, it makes no difference whether the survey fee payments are scaled up or down to the NHA figure, at least in the case where one holds constant _ak_ . In this case,  _k_ adjusts to hold ( _k_ ‐1) _Fk_ / _Sk_ in eqn (5) constant, since otherwise the cost constraint, eqn (6), is no longer satisfied.

# **_Results_**

Table 3 shows the inequality in the incidence of health sector subsidies in Vietnam, according to the various methods outlined above. The figures for total subsidies in each case are

> 2 One problem for the second approach above is that informal payments may not be levied according to the fee schedule, so assuming subsidies are proportional to fees recorded in the survey may be problematic. This is not a reason to scale down user fees in the survey, though, or to favor the traditional approach to BIA. Rather it would be a reason to try to capture explicitly informal payments and to treat them separately—perhaps assuming they are proportional to income rather than linked to the fee schedule.