rafmacalaba/datause-encoder-data huggingface dataset:
data (with data pooling), and may be considered in other countries with similar capacity, such as through centralized credit registries.

The BCBS has also called for the expansion of practical expedients already available in the IASB proposal of November 2009, especially taking into consideration the need for such practical expedients for small banks and smaller portfolios, where risk management capacity and resources are limited, or the costs of implementing sophisticated models outweigh the benefits. These are also realistic concerns in many developing countries. To this end, the BCBS proposed the use of a simplified average loss rate, which ―would represent expected credit losses by loan type derived from historical experience based on some measure of actual losses (eg direct writeoffs, specific provisions on non-performing loans) and adjusted for current conditions‖ (BCBS 2010c, p. 15). This is a considerably simpler approach than the IASB proposal, but still would require more refined calibration than the provisioning matrix based on classification currently in use in many developing countries. It is also similar to the dynamic provisioning schemes in some countries, as will be discussed in the next section (plus adjustments for current conditions).

# **_Dynamic Provisioning_**

Regarding dynamic provisioning, there are only a few case studies available: namely, those for Spain, Uruguay, Bolivia, Colombia, and Peru. These systems (which come with or are referred to by different names—dynamic, statistical, countercyclical provisioning, and even ―procyclical‖ provisioning) share some common features, but the specific design differs to various extent, as is discussed in Fernández de Lis and Garcia-Herrero (2010) and Wezel (2010).

All existing dynamic provisioning systems use estimation of expected losses based on historical experience and distinguish between different types of exposures (thus different loss experience and expectations). Most of them are rule-based, and use the concept of general provisioning. As mentioned, a rule-based system is more resistant to undue political and market pressures once the rule is successfully put into place, which is no small challenge in itself. General provisioning provides more flexibility than specific provisioning in terms of allocation to cover losses when they arise. There are also many differences in specific designs. For example, in some regimes, the rule dictates a continuous buildup and release of provisions, while in others it dictates the switching of states. While most rules focus on institution-specific measures of loan quality, the Peruvian regime is distinct in that it has an explicit systemic focus by setting the rules based on economic growth (see table 3 and a more detailed description in appendix A).

**Table 3. Select Features of Existing Dynamic Provisioning Systems**

|Feature|**Spain**|**Uruguay**|**Bolivia**|**Colombia**|**Peru**|
|---|---|---|---|---|---|
|Rule or discretion|Rule|Rule|Rule|Discretion<br>(maychange)|Rule|
|Continuous or switching of<br>states|Continuous|Continuous|Two states|Two states|Two states|

15
economy. Importers in these designated sectors are required to export an amount of local production equal to the value they import. The policy was first applied to car producers and was then extended to the pharmaceutical industry.

By the end of July 2011, media reported that similar restrictions were also applied to refrigerators, washing machines, cookers, and toys. _Source:_ Global Trade Alert 2011c.

- November “Not one nail imported, if possible,” said President Cristina Fernández de 29, 2011 Kirchner, addressing the Chamber of Construction. She stressed the need to strengthen import substitution and to lower input costs so as to improve the trade balance and create more employment in the country. Speaking to _Ultimas Noticias_ , Gabriel Murara, the vice president of the Confederation of Industry of Uruguay, which is a member country of MERCOSUR, admitted concerns about this statement, adding that he saw no quick exit from problems with Argentina. _Source:_ Delletore 2011.

- March 22, The Minister of  Industry, warned agricultural machinery manufacturers 2012 that to do business in the Argentine market that they must incorporate more parts produced in Argentina. “The policy is one of carrot and stick; companies that use domestic demand must generate Argentine employment.”

- The Minister set a target of 50 percent local value added for the industry, and wants to see the sector reverse the trade deficit of Arg$450 million recorded in 2010. Companies such as Fiat, Case New Holland, John Deere, Pauny-Stara, Agco, and Claas have signed agreements with the government to adapt to the new policy of import substitution and countertrade. _Sources:_ Infocampo 2012; Ministry of Industry 2012.

16
Table B4: Difference-in-Difference- Share of Informal Workers

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Outcome:|Informal|Informal|Inf.-non salary|Inf.-non salary|Informal|Informal|Inf.-non salary|Inf.-non salary|
|||_Pa_|_nel A: Continuous _|_Treatment Measu_|_re_||||
|-lndistance_i_ x 1999|0.000|0.002|0.001|0.002|-0.001|0.001|-0.001|-0.000|
||(0.004)|(0.005)|(0.004)|(0.004)|(0.006)|(0.006)|(0.005)|(0.006)|
|-lndistance_i_ x 1999|-0.008|-0.007|-0.012**|-0.011**|-0.015**|-0.016**|-0.018***|-0.019***|
||(0.006)|(0.006)|(0.005)|(0.005)|(0.007)|(0.007)|(0.006)|(0.007)|
|-lndistance_i_ x 2004|-0.016***|-0.015**|-0.016***|-0.014**|-0.019**|-0.020**|-0.017**|-0.017**|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.008)|(0.008)|(0.007)|(0.008)|
|Observations|11,504|11,504|11,504|11,504|11,504|11,504|11,504|11,504|
|R-squared|0.866|0.866|0.844|0.843|0.869|0.869|0.847|0.847|
|||_Panel B: _|_Treatment Measur_|_e using the dumm_|_y variable_||||
|_Ti_ x 1999|-0.006|-0.010|-0.001|-0.006|-0.005|-0.010|-0.006|-0.013|
||(0.010)|(0.010)|(0.009)|(0.009)|(0.011)|(0.011)|(0.010)|(0.010)|
|_Ti_ x 2004|-0.023*|-0.026**|-0.023**|-0.029**|-0.033**|-0.036***|-0.028**|-0.035***|
||(0.013)|(0.013)|(0.011)|(0.011)|(0.013)|(0.013)|(0.012)|(0.012)|
|_Ti_ x 2009|-0.036***|-0.035**|-0.034***|-0.036***|-0.037**|-0.036**|-0.026*|-0.029**|
||(0.013)|(0.014)|(0.012)|(0.013)|(0.014)|(0.014)|(0.014)|(0.014)|
|Observations|11,504|11,504|11,504|11,504|11,504|11,504|11,504|11,504|
|R-squared|0.866|0.866|0.843|0.843|0.869|0.869|0.847|0.847|
|Mean outcome before the sho|ck<br>0.582|0.582|0.415|0.415|0.582|0.582|0.415|0.415|
|Distance Measure|Meters|Minutes|Meters|Minutes|Meters|Minutes|Meters|Minutes|
|Distance Controls|X|X|X|X|X|X|X|X|
|State-Time FE|X|X|X|X|||||
|Municipality-Time FE|||||X|X|X|X|

_Notes:_ This table reports the results of a regression relating changes in the share of informal workers in each location with the line B of the subway. Panel A reports the results for the continuous treatment measures, and panel B for the dummy variables. In the first four columns, I include state-time fixed effects, and in the fifth column to the eighth column municipality-time fixed effects. Standard errors are clustered at the census tract level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.
China will also have to continue to invest in its human capital, as the quality of its labor force catches up to the OECD countries (Figure 25). Over 30 years, China has made primary education universal and expanded access to education at all levels. To obtain greater benefits from technology diffusion from abroad and to nurture frontier discovery, China now has to focus on investing in the skills of its labor force (Eaton and Kortum, 1996; Seck, 2012).

**Figure 24 China’s TFP relative to the Figure 25 Human capital index, 2017 global technology frontier, 2017**

(x-axis in log scale) (both axes in log scale) <!-- Start of picture text --> CHN<br>CHN<br>PPP GDP per capita in 2017 (thousand USD)20 40 60 80 100 PPP GDP per capita in 2017 (thousand USD)20 40 60 80 100<br>1.5 4<br>1<br>3<br>.5<br>2<br>Human capital index<br>TFP relative to technology frontier (US)<br>1<br><!-- End of picture text --> Source: PWT 9.1; authors’ calculations. Note: OECD countries in orange.

Source: PWT 9.1; authors’ calculations. Note: OECD countries in orange.

Over the past four decades, China has made such great strides in physical capital deepening that its government capital stock per worker has already reached OECD levels (Figure 26).<sup>20</sup> This suggests that there may be limited economic gains from future high growth in government investment. But there is scope for rapid growth of private capital. Starting from a very low base in the 1970s, private sector capital increased at a remarkable pace but remains considerably below advanced country levels (Figure 27).

> 20 Government investment in the IMF data set accounts for capital spending by central and subnational governments, so that comparable data are available for a large number of countries. The total infrastructure stock for China is possibly underestimated, as infrastructure investment by state-owned enterprises (SOEs) is included in the private capital stock. For details refer to https://www.imf.org/external/np/fad/publicinvestment/data/info.pdf.

19
> - _74_ -

**Tab 18: Aural Conrimptbon Parameters** Acroms States **Wtth Dltterhg** AgrWultural **hbcoms, hdba** 1983

||Total Rural|Budget Share Sp|ent on Nonfoods|
|---|---|---|---|
|State|Expenditure|Average|MarginalV|
||(rupees/capita<br>In 30 days)|(perc|ent)|
|High Agricultural Income<br>||||
|Punjab|170.5|41.3|56.3|
|Haryana|151.8|36.4|46.3|
|Moderate Agricultural Income||||
|Karnataka|116.8|36.5|36.6|
|Gujarat|122.7|33.8|49.0|
|Low Agricultural Incomo||||
|Madhya Pradesh|100.5|33.5|45.5|
|Bihar|93.8|26.3|32.0|
|All India Rural Average|112.5|34.2|41.9|

a/ Calcuated as bl + b2(1 + ln(E)) where E = average total expenditure. bl and b2 are obtan from Engel curves estimated in share form as follows: Snf * bo 1/(E) + bi + b2 liln(E) where S'f - Enf/E= share of expenditure on nonfoods. **See** Hazil and Roell (1983) for details on estimating procedure. Because household data were _ru_ available to us, we have used grouped data induding 13 observations, one for each _expenditure_ cass reported by National Sample Survey.

Sour: National Sample Survey, 38th Round as reported in S Vol.9, No.3 (April 1986).
<!-- Start of picture text --> Type of cultural properties Number<br>Designated important cultural properties 2000 eeeneeeeeeeeeees 12,874 a) 1,085<br>Fine and applied arts 222.2020 1OATE a) 868<br>Burl dings ooo. ooo eee ceecce eee eee eee eee eee eeeeeteeeeeeeeeeeeneees 2,398 a) 217<br>Historic sites, places of scenic beauty and natural monuments ............... 3.088 b) 172<br>Historic Sites ooo cece cece ecec ee ceeneseeeneneeneneeneneeneneeneneseeseneeeeneneneeneneeee 1,709 b) 61<br>Places of scenic beauty 22. eee eee cee cece ee ceeeeeeeneeeeeeeeceneeeeeeeenee 374 b) 36<br>Important tangible folk cultural properties 2202002000000...cece cece 213<br>Important intangible folk cultural properties —..0. 2000. ...oeece ce ceeeee eee eens 281<br>Important intangible cultural properties<br>Recognized indivicduals 02.2.2... eeeeeeeeeeeceeeceeeececeeceeeeeeneeeeeeeeeeeees 79<br>Craft techmiques 222.02... cece cecceeceesecceeneeceseeceeseeceeseeceecesseeseteeeseceeeees 41<br>Recognized holding groups 22.22. ..eee ee eeeeeeeeeeeeeeeeneeeeeeeeeeeeeeeeeeeeeeeee 26<br>Performing arts 222.22... eeeceecceeceeeceeeeeeceeececeeneeeceeeeeceeeceeeeeeeeeeeeee: 12<br>Craft techniques 22........2.e cece eeeeeeceeseeeeeeeeeeeeeseeeeeeeeeeeeeeceeeeeeeeneneese 14<br>Traditional building preservation areas 2.00.00...0c ce cece cece eects eee 102<br>a) National treasures only. b) Specially designated places only.<br>Source: Ministry of Education, Culture, Sports, Science and Technology.<br><!-- End of picture text -->
# **<u>Table 2: Definitive Measures of the National Commission for Price Distortions and Sectors Affected</u>**

|**Sector\Measure**||**Tariff Surc**|**harges**|**Minimum**|**Customs**|**Values**|**D**|**Counterv**<br>**uties**|**ailing**|**Anti-dumping**<br>**Duties**|
|---|---|---|---|---|---|---|---|---|---|---|
||1981-1987|1986-1989|1990-2002(a)|1981-1987|1986-1989|1990-2002 (b)|1981-1987|1986-1989|1990-2002|1990-2002 (c))|
|**Agriculture**|0|0|5|1|0|1|0|0|1|1|
|**Food products**|3|0|1|3|0|0|0|0|0|0|
|**Electrical, mechanical**|||||||||||
|**or fuel products**|5|0|0|0|1|1|0|0|1|0|
|**Capital goods**|0|0|0|0|0|0|0|0|0|0|
|**Leather and shoes**|1|0|0|0|0|0|0|0|0|1|
|**Forestry, paper and wood**|0|0|0|0|0|0|0|0|0|0|
|**Dairy products**|3|3|2|1|2|4|0|0|2|0|
|**Metals and metal mechanics**|4|0|~~1~~|0|3|1|0|0|0|2|
|**Mining**|2|0|0|0|0|0|0|0|0|0|
|**Tires, rubber and plastics**|1|4|1|1|3|1|0|0|0|0|
|**Fishing**|1|0|0|0|0|0|0|0|0|0|
|**Chemicals**|2|1|0|5|0|0|0|0|0|2|
|**Textiles**|22|6|4|0|14|8|0|0|2|0|
|**Glass and ceramics**|3|0|0|1|0|0|1|0|0|0|
|<br>**Total**<br>**Source: Central Bank of Chile (2**|**47**<br>**003), www.cn**|**14**<br>**dp.cl**|**14**|**12**|**23**|**16**|**1**|**0**|**6**|**6**|

## Note::

In 2003, there were no investigations and no measures of any kind were adopted.

a) As from 1999, surcharges correspond to safeguard measures. The former system of surcharges up to a 35% level was eliminated; b) As from 1999, MCV were removed from Chilean legislation; c) Before 1992, anti-dumping duties were not applied.

13
similar education levels. This may provide disincentives for women to enter the labor force. Another possible explanation for higher employment rates of better educated women could be that employers offering high skilled jobs have a much smaller gender bias.

Regionally, employment rates are highest in the capital city of Belgrade and lowest in Central Serbia. Rural population is more likely to be employed than urban, which is not uncommon in post-conflict countries due to a larger supply of low value-added jobs. Agriculture is a key sector for the rural economy, employing 47.2% of rural citizens (45.3% of men and 50.7% of women) while the job market in urban locations is dominated by services.

Gender disparities in access to job opportunities are much more pronounced in rural areas, where 33% more men than women are employed (Figure 3). Lower employment rates among rural women can potentially be explained by a greater prevalence of traditional lifestyles as well as by lack of child care institutions in rural locations. Another possible reason is that there are simply not enough jobs in rural Serbia offering attractive remuneration rates for women to make it worth for them to join the labor force. Studies from other countries, e.g., the export-oriented flower industry in Ecuador, show

**Figure 3. Employment rate of working age** **<u>population in rural and urban areas, %</u>**

<!-- Start of picture text --> 80 63.8<br>52.9<br>60 43.1 42.5<br>40 Men<br>20<br>Women<br>0<br>rural urban<br><!-- End of picture text --> <!-- Start of picture text --> Source: Serbia LFS, 2009<br><!-- End of picture text --> that when opportunities for women’s gainful employment arise, households reallocate family responsibilities with men spending much more time doing housework allowing women to take advantage of paid employment.<sup>3</sup>

# **1.2. Employment Patterns of Men and Women**

_<u>Differences in Employment by Type of Institutional Ownership and Sector of Economic Activity</u>_ Market-oriented reforms and privatization have resulted in the shift of a large share of the labor force into the private sector, which now accounts for almost 70% of total employment (Table 2). As in many other countries in the region, women are somewhat more likely to work in the public sector. Jobs in stateowned institutions tend to be relatively secure and rarely require overtime work, allowing women to combine employment with family responsibilities.

**Table 2.Structure of Employment by Type of Ownership, %** **<u>(population age 15 and above)</u>**

||Male|Female|Total|
|---|---|---|---|
|Private - registered ownership|58.0|52.2|55.5|
|Private – with unregistered ownership|14.1|12.9|13.6|
|State ownership|24.1|31.9|27.4|
|Social ownership|2.3|1.5|2.0|
|Other types of ownership|1.6|1.4|1.5|
|Total|100|100|100|

Source: Serbia LFS 2009

> 3 Newman, Constance. 2001. _Gender, Time Use, and Change: Impacts of Agricultural Export Employment in Ecuador_ . Policy Research Report on Gender and Development. Working Paper Series No18, The World Bank

4
# **REFERENCES:**

- Appadurai, Arjun. 2004. “The Capacity to Aspire: Culture and the Terms of Recognition,” in V. Rao and M. Walton (editors) _Culture and Public Action_ . Palo Alto: Stanford University Press.

- Arizpe, Lourdes. 2004. “The Intellectual History of Culture and Development Institutions,” in V. Rao and M. Walton (editors) _Culture and Public Action_ . Palo Alto: Stanford University Press.

- Baiocchi, Gianpaolo. 2005. _Militants and Citizens: The Politics of Participatory Democracy in Porto Alegre._ Stanford, California: Stanford University Press.

- Ban, Radu and Vijayendra Rao. Forthcoming. “Is Deliberation Equitable? Evidence from Transcripts of Village Meetings in South India.”

- Bardhan, Pranab. 1988. “Dominant Proprietary Classes and India’s Democracy,” in Atul Kohli (edited) _India’s Democracy: An Analysis of Changing State-Society Relations,_ pp.214-224, Princeton, New Jersey: Princeton University Press.

Bardhan, Pranab and Dilip Mookherjee. 2007. “Land Reforms, Decentralized Governance and Rural Development in West Bengal,” mimeo, January.

- Barnett, Clive. 2003. _Culture and Democracy: Media, Space, and Representation._ Tuscaloosa: The University of Alabama Press.

- Besley, Tim, Rohini Pande and Vijayendra Rao. 2005. “Participatory Democracy in Action,” _Journal of the European Economic Association_ 3(2-3): 648-657.

- Bohman, James. 1996. _Public Deliberation: Pluralism, Complexity, and Democracy._ Cambridge, M.A.; London, England: The MIT Press.

- Chambers, Simone. 1995. “Discourse and Democratic Parctice,” in Stephen K. White (edited) _The Cambridge Companion to Habermas._ Cambridge University Press.

Delli Carpini, Michael X., Fay Lomax Cook, and Lawrence R. Jacobs. 2004. “Public Deliberation, Discursive Participation, and Citizen Engagement: A Review of the Empirical Literature,” _Annual Review of Political Science_ 7:315-44.

Moon, Donald J. 1995. "Practical Discourse and Communicative Ethics," in Stephen White, (editor), _A Companion to Habermas,_ Cambridge University Press.

- Elster, Jon (ed). 1998. _Deliberative Democracy._ Cambridge, U.K.; New York: Cambridge University Press _._

- Fung, Archon. 2004. _Empowered Participation: Reinventing Urban Democracy._ Princeton, Oxford: Princeton University Press.

40
# **1.  Introduction**

Concerns about inequality are at the forefront of many policy debates today. From the “we are the 99 percent” slogans of the Occupy Wall Street movement, to major policy speeches by US President Barack Obama, it is hard to escape the view that rising inequality poses major challenges in advanced economies. In the developing world too, much has been written about the adverse effects of high and rising inequality on the pace of absolute poverty reduction. Concerns about inequality appear to be pervasive beyond policy elites as well. Public opinion surveys suggest that strong majorities of respondents in advanced economies feel that the gap between rich and poor has been rising in recent years. According to a recent Pew survey, over 80 percent of respondents in advanced economies are of the view that the gap between rich and poor has been increasing.<sup>1</sup> These views no doubt in part reflect the fact that inequality has in fact been increasing in many countries. In the US over the past four decades, the Gini coefficient has risen from around 30 to around 40. Roughly the same has happened in China, only more rapidly: between 1990 and 2009 the Gini coefficient has increased from 32 to 42. Much of this increase has happened at the upper end of the income distribution. According to Atkinson, Piketty and Saez (2011), the income share of the top 10 percent in the US has increased from around 33 percent in 1970 to nearly 50 percent in 2007, while in China it increased from 17 to 28 percent between 1986 and 2003.

However, it is also important not to lose sight of the fact that inequality has not increased in other countries, and has declined appreciably in still others. In the Atkinson, Piketty and Saez (2011) data, income shares of the top decile have been stable or even declining slightly since the mid-20<sup>th</sup> century in countries such as Germany, France, Switzerland, and the Netherlands. In Brazil, the Gini coefficient has declined from around 60 in the late 1990s to around 55 in the late 2000s. More systematically, in a large dataset of changes in inequality over periods at least five years long that we describe in more detail below, in roughly half of episodes the Gini coefficient of inequality increases, while in the other half of episodes it decreases.

In this paper, we aim to shed light on a very simple question: how much do these changes in inequality, in either direction, matter? To answer this question we first need to be precise about what we mean by “matter”. Our approach here is unabashedly modest: we use standard tools of social welfare analysis to calculate how much more or less growth in average incomes a country would need

> 1 See Pew Research Center (2013)

2
**TABLE 4. Impact of Privatization & Nationalization on Network Evacuation Rate in Maldonado**

|Variables||**A**<sup>**a**</sup><br>|**B**<br>|<sup>**a**</sup><br>||**C**<sup>**b**</sup><br>|**D**<br>|<sup>**c**</sup><br>|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(1)|(2)|(1)|(2)|(1)|(2)|
|Private provision|0.037|0.055|0.037|0.053|−0.003|−0.035|<br>−0.086***|<br>−0.081|
||(0.042)|(0.066)|(0.042)|(0.067)|(0.036)|(0.054)|(0.031)|(0.049)|
|Renationalized provision|0.163***|0.143**|0.163***|0.140**|0.244***|0.173***|0.154***|0.139**|
||(0.053)|(0.058)|(0.053)|(0.061)|(0.036)|(0.056)|(0.037)|(0.055)|
|Education||0.041|<br>|0.049|<br>|0.045||0.025**|
|||(0.028)||(0.031)|<br>|(0.029)||(0.012)|
|(log) Household per capita<br>i||0.210***||0.221***||0.191***||0.169***|
|ncome||(0.063)||(0.085)|<br>|(0.062)||(0.060)|
|(log) Accumulated<br>||0.075**||0.072*||0.072**||0.063***|
|precipitation||(0.030)||(0.043)||(0.030)||(0.022)|
|Observations|735|735|580|580|702|702|432|432|
|Log likelihood|−355.21|−331.55|−282.16|−263.89|−340.11|−316.54|−198.54|−195.19|
|Sampleperiod|1986|–2009|1993|–2009|1986|–2009|1986|–2009|

_Source:_ Authors’ estimation using ECH and National Meteorology Office data. _Note:_ Estimates are obtained using a Papke & Wooldridge Fractional Logit Model. Marginal effects are reported, and robust standard errors clustered at the city level are reported in parentheses. All models include city fixed effects. *, **, and *** indicate statistical significance at the 10 percent level, 5 percent level, and 1 percent level, respectively.<sup>a</sup> Treatment Units (three cities): Maldonado, San Carlos, and Pan de Azúcar; Control Units (32 cities): Artigas, Bella Unión, Canelones, Carmelo, Colonia, Dolores, Durazno, Florida, Fray Bentos, Lascano, Libertad, Melo, Mercedes, Minas, Montevideo, Paso de los Toros, Paysandú, Periferia Canelones, Rivera, Rocha, Rosario, Río Branco, Salto, San José de Mayo, San Ramón, Santa Lucía, Sarandí del Yí, Sarandí Grande, Tacuarembó, Tranqueras, Treinta y Tres, Trinidad, and Young.<sup>b</sup> Treatment Unit (one city): Maldonado; same Control Units as in A.

> <sup>c</sup> Treatment Unit (one city): Maldonado; Control Units (17 cities): Artigas, Canelones, Colonia, Durazno, Florida, Fray Bentos, Melo, Mercedes, Minas, Paysandú, Rivera, Rocha, Salto, San José de Mayo, Tacuarembó, Treinta y Tres, and Trinidad.

29