rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Energy bundle<br>(XNRG)<br>Electric bundle Non-electric<br>(XELY) bundle (XNELY)<br>Oil and biofuels<br>Coal (XCOA) Gas (XGAS)<br>bundle (XOBF)<br>Other oils bundle Ethanol/gasoline Biodiesel/diesel<br>(XOIL) bundle (XETG) bundle (XBDD)<br>Ethanol (XETH) Gasoline (XGSO) Biodiesel (XBDL) Diesel (XDSL)<br><!-- End of picture text -->

**(c) Energy demand structure**

All other demand variables presented in the subsequent tiers of the nested structures in Figure 1 are derived in a similar manner to Equations (2) to (4). Note that PX is the producers‟ price prior to the application of a production tax or subsidy and excise tax. The market price for an output (PP) is calculated as follows: where <sup>P</sup> is a tax (or subsidy) rate applied to production and <sup>x</sup> is an excise tax ($/liter) applied to the sales volume.

Land use changes are incorporated into the model via a CET representation of land supply for each country/region (see Figure 1b). Total land areas are first divided into 18 agro-ecological zones (AEZ) in every country/region. In line with Birur et al. (2008), on the top level of the CET structure, total available land area, under each AEZ, is allocated to forest land, pasture and plantation of crops. On the second level, crops are further divided into four different categories: rice, sugar-crops, grains and oilseeds, and fruits and vegetables. Finally, the grains and oilseeds category is partitioned into wheat, corn, other coarse grains, and oilseeds. This nested structure 6
37 that simple lin3ar growth regressions can not isolate the effects of monetary policy on growth.

Outside of money, the fin..ncial system may provide a varietv of important services. As discussed in the World Development Report 1989, financial systems can help pool, allocate, and manage risk,<sup>mobilize savings</sup> **for projects that could not be** independently financed, monitor the<sup>activities</sup> of mangers, provide information on the economy, and evaluate enterprises for investors. Although these products of the financial system are well<sup>known, it</sup> is only recently that economists have begun to study the emergence<sup>and</sup> implications of financial assets and institutions within the context tormal theoretical frameworks. McKinnon (1973) and Shaw (1973) provide insightful descriptions of th.e role of finance in growth. Bernanke and Gertler (1989, 1990) formally model the role of financial markets in depressions. Jovanovic and Greenwood (1990), Greenwald and Stiglitz (1989), and Levine<sup>(1990, 1991)</sup> formally model potential linkages between economic growth and financial markets that emerge to mitigate specific problems that exist in the model economy, e.g., allocation of risk under asymmetric information, economies of scale in information gathering, etc.

Econometrically, it is difficult to construct measures of specific financial policies or of the services provided by financial markets that<sup>are</sup> meaningful and available for a broad cross-section of countries. Gelb (1989), The World Development Report (1989), Goldsmith (1969), and McKinnon<sup>(1973)</sup> have linked measures of domestic financial activity to grcwth. For example, Goldsmith (1969) finds that as real income riser, the ratio of financial institutions' assets to GNP grows, while The World Development Report 1989 demonstrates that the distribution of financial assets among financial
# The Downside of the Kuznets Curve and 20th Century Ineaualitv Histories

Europe and America were not always so egalitarian, it seems. So, when did the egalitarian leveling take place? We have far better evidence to answer that question today than Kuznets had in 1955.

What Arthur Burns saw in America as a "revolutionary leveling" after 1929 apparently was shared by most of Europe, whose experience is documented in Figure 3. What is so striking about Figure 3 is the extraordinary similarity in each national downswing of the Kuznets Curve across the 20th century. the one possible exception is Germany in the interwar decades, but still the conformity is striking. Nor is the historical pattern in Figure 3 unique. When these 20th century time series are pooled with the contemporary cross-section in Figure 1, neither the levels nor the trends show sigificantly different patterns than those already summarized in Figure 1. Thus, 20th century history agrees with the international cross-sections and with Kuznets' original conjecture: there is a clear trend towards equality in the later stages of development.

# A Word About Market Forces Versus the Fisc

The inequality we have been talking about thus far is pre-fisc, that is, income before the effects of taxes, transfers, or government purchases of goods and services. There are three reasons for the choice. First, and certainly most pragmatic, comparative assessments are far easier to make since the literature is dominated by pre-fisc estimates. Second, while well-being at any point in time is certainly better gauged by post-fisc income, it is not at all clear that inequality trends can be explained by changes in the "fisc".

-14-
the total electricity generation capacity by 2050. The share of natural gas in the total electricity generation capacity in 2050 would drop to 14% in the green scenario from 19% in the baseline scenario. It would drop further to 9% under the super-green scenario. The share of nuclear power in 2050 would increase to 13% and 15% under the green and super green scenario, respectively from the 6% in 2015 under the baseline scenario.

**Table 3: Total Installed Capacity (% and GW)**

|||**Normal d**|**emand**|||**Low**|**demand**||
|---|---|---|---|---|---|---|---|---|
||**2020**|**2030**|**2040**|**2050**|2020|2030|2040|2050|
||||Base<br>|line Scenari<br>|o<br>||||
|Biomass|0%|0%|0%|0%|0%|0%|0%|0%|
|Coal|30%|16%|6%|4%|30%|22%|6%|5%|
|NaturalGas|14%|17%|25%|19%|14%|9%|20%|17%|
|Hydro|28%|30%|28%|31%|28%|31%|31%|31%|
|Nuclear|6%|7%|6%|5%|6%|7%|7%|6%|
|Oil|7%|1%|1%|1%|7%|1%|1%|1%|
|PV|3%|2%|9%|7%|3%|2%|7%|6%|
|Wind|13%|28%|25%|32%|13%|28%|28%|34%|
|Total(GW)|23|21|23|30|23|20|21|25|
||||Gre|enScenario|||||
|Biomass|0%|0%|0%|2%|0%|0%|0%|3%|
|Coal|30%|9%|5%|2%|30%|9%|5%|3%|
|Natural Gas|14%|21%|18%|14%|14%|17%|14%|11%|
|Hydro|28%|31%|36%|28%|28%|35%|39%|34%|
|Nuclear|6%|7%|5%|13%|6%|8%|6%|7%|
|Oil|7%|1%|1%|1%|7%|1%|1%|1%|
|PV|3%|2%|13%|10%|3%|3%|12%|10%|
|Wind|13%|28%|22%|30%|13%|27%|23%|30%|
|Total(GW)|23|20|26|33|23|18|22|27|
||||Super-<br>|greenscena<br>|rio<br>||||
|Biomass|0%|5%|4%|4%|0%|4%|3%|5%|
|Coal|30%|0%|0%|0%|30%|0%|0%|0%|
|Natural Gas|14%|8%|5%|9%|14%|8%|1%|1%|
|Hydro|28%|44%|38%|32%|28%|49%|41%|36%|
|Nuclear|6%|13%|18%|15%|6%|9%|20%|17%|
|Oil|7%|0%|0%|0%|7%|0%|0%|0%|
|PV|3%|14%|19%|8%|3%|8%|16%|10%|
|Wind|13%|16%|16%|32%|13%|22%|19%|30%|
|Total(GW)|23|21|25|29|23|19|23|26|

Please note that at present Romania has much higher installed capacity compared to its peak load (20 GW installed capacity vs. 10 GW peak load). Adding more renewable sources for energy generation, particularly the intermittent sources (wind and solar) would further increase the 12
|Other|6.1|4.5|5.8|
|---|---|---|---|
|**Employment categories**||||
|Private sector employee|34.7|12.8|13.7|
|Self-employed, agriculture|30.2|8.7|10.3|
|Self-employed, non-agriculture|9.6|56.5|50.8|
|Employer, agriculture|18.5|0.7|12.9|
|Employer, non-agriculture|4.1|3.4|2.5|
|Public sector employee|2.0|2.9|4.7|
|Unpaid worker|0.7|12.3|1.3|
|Domestic worker|0.3|2.7|3.7|
|**Main reason why not looking for a job (last month)**||||
|Very advanced age|39.8|1.3|12.7|
|Household tasks/chores|23.0|**97.2**|**81.6**|
|Retired|11.2|0.1|1.2|
|Disability|10.1|0.2|2.3|
|Temporary illness|7.3|0.6|1.6|
|Other reason|8.6|0.6|0.6|

_Source_ : Authors’ tabulations using the Encuesta de Hogares de Propósitos Múltiples 2015 (EHPM).

Furthermore, inactivity rates for women are higher if their spouse reports being either self-employed or an employer in agriculture – i.e. families with heavy reliance on their own agricultural production (Table 2). This is exactly the type of household one might expect women to have higher rates of employment if they are involved in supporting agricultural production and these tasks are being accurately counted. While we might have expected that women in these households are more likely to be unpaid workers (family members who contribute to the family enterprise without receiving pay), this is actually a less likely outcome than in households where the spouse is running a non-agriculture enterprise (either as self-employed or an employer). One potential explanation is a very strict division of labor where women rarely participate in agricultural activities. This arrangement would differ substantially from the typical agricultural production function of households in which most members contribute, at least in specific tasks or specific crops. This explanation is also not supported by earlier research on Honduran agricultural production as reported above. Instead, the more likely explanation is that there is an undercount of women’s participation in agricultural activities specifically.

Table 2. Employment outcome by outcome of spouse, rural households with male head and female spouse

||||**Occupat**|**ion of the hus**<br>|**band**||
|---|---|---|---|---|---|---|
|||Self-||Self-||Wage,|
|||employed,<br>agriculture|Employer,<br>agriculture|employed,<br>other|Employer,<br>other|<br>private<br>sector|
||Self-employed, agriculture|5.0|5.0|0.8||1.1|
||Employer, agriculture|0.1|0.8|||0.2|
|**Occupation**|Self-employed, other|18.8|18.6|20.6|20.2|20.0|
|**of the wife**|Employer, other|0.5|0.2|2.2|7.9|0.7|
||Unpaid worker|3.0|4.5|14.6|6.1|2.8|
||Inactive|69.7|68.6|55.4|57.7|65.3|
||Other|2.9|2.2|6.4|8.2|10.0|
||_Total_|_100_|_100_|_100_|_100_|_100_|
||_Number of observations_|_2,876_|_1,562_|_806_|_352_|_3,083_|

8
Figure 2 and Table 1 provide a perspective on the unconditional changes in inspections and time to clear customs in Albania from 2007 to 2012. But modern risk management systems condition the probability of inspection on information from the import declaration, including the identity of the importing firm, the type of product imported and the origin country. It is therefore important to condition on such criteria when describing changes in the probability of inspections over the sample period. Using the data at the importing firm-HS 6-digit product-origin country-year level, we estimate OLS regressions of the median allocation to the red channel on year fixed effects while controlling for importing firm-HS 6-digit product-origin country fixed effects.<sup>32</sup> The results in Table 2 show that the conditional probability of a red channel inspection declines steadily and significantly over the period. By 2012 our measure of the conditional inspection rate is 21 percentage points lower than in 2007. This magnitude is very large, even comparing to the decline in the unconditional inspection rate in Figure 2. This dramatic variation in conditional inspection probabilities underpins our subsequent analysis.

**Table 2. Conditional change in inspections over the sample period**

||Dependent variable is median<br>allocation to red channel at firm‐<br>HS6 product‐origin country‐year<br>level<br>OLS estimation<br>(1)|
|---|---|
|Year 2008|‐0.039***<br>(0.003)|
|Year 2009|‐0.117***<br>(0.003)|
|Year 2010|‐0.161***<br>(0.003)|
|Year 2011|‐0.199***<br>(0.003)|
|Year 2012|‐0.210***<br>(0.003)|
|Year 2007 (constant)|0.463***<br>(0.002)|
|Firm*HS6*country fixed effects|Yes|
|Observations|784,340|
|R‐squared|0.805|

Note: Standard errors in parentheses. *** indicates significance at the 1% confidence level.

Improved risk management capabilities are not always associated with substantial reductions in the inspection burden. In environments where traders are frequently non-compliant, these mechanisms may aid detection but not facilitate sharp reductions in the share of consignments

> 32 Median allocation to the red channel means that an observation at the importing firm-HS 6-digit product-origin country-year level takes the value of one if 50 percent or more of the shipments in that cell are assigned to the red channel and zero if less than 50 percent of shipments are assigned to the red channel.  We discuss our reasons for defining inspection activities like this in Section 5.

16
Policy Research Working Paper 7264

# **Abstract**

This paper investigates how internal migration is affected by Brazil’s increased integration into the world economy. It analyzes the impact of regional differences in access to foreign demand on sector-specific bilateral migration rates between the Brazilian states for the years 1995 to 2003. Using international trade data, a foreign market access measure is computed at the sectoral level, which is exogenous to domestic migration. A higher foreign market access is associated with a higher local labor demand and attracts workers via two potential channels: higher wages and new job opportunities. Results show that both channels play a significant role in internal migration. Further, we find a heterogeneous impact across industries, according to their comparative advantage on the world market. However, the observed impact is driven by the strong reaction of low-educated workers to changes in market access. This finding is consistent with the fact that Brazil is exporting mainly goods that are intensive in unskilled labor.

This paper is a product of the Development Policy Department, Development Economics Vice Presidency. 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 laura.hering@gmail.com and paillacar@gmail.com.

_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
Table 6: BAU-2030 and base year data aggregated by 5% percentiles of income. GDP per capita as population weighted averages, population in millions, number of poor people in millions, annual tree loss in square kilometers, PM2 _._ 5 in population weighted average concentrations, carbon emissions in million tons. World totals are scaled to world totals using multipliers (1.16 for population and carbon based on the share of population in our data, and tree loss 1.42 based on the share of tree cover in the data).

|Income|GDP p.c.|GDP p.c.|Pop.|Pop.|No. Poor|No. Poor|Treeloss|Treeloss|PM25|PM25|CO2|CO2|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Group|2014|2030|2014|2030|2014|2030|2014|2030|2014|2030|2014|2030|
|1|1_,_094|1_,_548|68|101|41|55|3_,_382|3_,_640|15|20|10|28|
|2|1_,_495|2_,_611|171|262|74|70|3_,_750|3_,_977|17|21|20|109|
|3|1_,_899|2_,_388|46|67|19|24|1_,_415|1_,_684|23|25|22|39|
|4|2_,_305|3_,_308|96|139|36|43|1_,_821|1_,_931|29|31|27|71|
|5|2_,_917|4_,_720|237|305|57|45|1_,_295|1_,_331|47|52|96|285|
|6|4_,_249|5_,_381|265|366|43|48|5_,_782|5_,_713|46|48|190|282|
|7|5_,_265|9_,_887|111|133|6|2|4_,_716|4_,_031|23|20|170|385|
|8|5_,_426|10_,_301|1_,_519|1_,_936|341|288|4_,_440|3_,_769|54|51|2_,_286|5_,_634|
|9|6_,_875|9_,_344|176|231|21|22|3_,_297|3_,_043|12|13|212|399|
|10|8_,_219|10_,_627|15|17|1|1|4_,_002|3_,_395|9|9|14|22|
|11|10_,_078|16_,_232|292|352|22|11|17_,_799|17_,_676|15|15|540|1_,_338|
|12|11_,_993|16_,_578|114|141|11|11|3_,_784|4_,_037|15|17|646|1_,_150|
|13|13_,_135|24_,_563|1_,_691|1_,_825|49|5|33_,_540|39_,_407|44|44|11_,_212|25_,_839|
|14|16_,_347|20_,_955|206|244|4|3|2_,_032|2_,_132|22|24|1_,_128|1_,_864|
|15|18_,_386|28_,_014|162|193|3|2|5_,_160|5_,_859|17|17|750|1_,_402|
|16|22_,_607|37_,_980|53|60|0|0|1_,_822|2_,_194|20|24|438|1_,_112|
|17|33_,_000|39_,_059|204|219|0|0|6_,_647|8_,_832|21|22|1_,_160|1_,_770|
|18|38_,_515|47_,_345|244|253|0|0|26_,_962|32_,_972|12|13|2_,_324|3_,_324|
|19|43_,_584|53_,_144|128|132|0|0|4_,_694|5_,_794|15|16|1_,_276|1_,_788|
|20|51_,_694|64_,_462|354|384|0|0|17_,_649|19_,_466|10|11|5_,_534|7_,_414|
|world|14_,_088|19_,_899|7_,_137|8_,_537|730|630|218_,_664|242_,_656|36|36|32_,_544|62_,_934|

xii
Porto, Guido. 2005. “Estimating household responses to trade reforms: Net consumers and net producers in Rural Mexico.” World Bank Policy Research Working Paper No. 3695, Washington, DC.

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16
32

# Figure 1

## (a) Actual and fitted values from equation (15)

<!-- Start of picture text --> 8.0<br>7.5<br>7.0<br>6.5 ,.....<br>i6.5 965 1970 1975 1980 1985 1990<br>I| _ Actual - Fitted<br><!-- End of picture text -->

## (b) Residuals from equation _(15)_

<!-- Start of picture text --> 0 .0 1 0 -- - --- --- -- --- --- --- -- --- --- --- -- --- --- -- - -- --- -- -_ -- --<br>0.005<br>0 000 A<br>-<br>1 6 . 75....0..0...... ... ..... ...<br><!-- End of picture text -->
# **Annex I – Pakistan Transactions Profile**

Transactions Profile for Pakistan at the Federal Government (FY 2016/17‐ Amount Ranges in Pakistan Rupees).

<!-- Start of picture text --> PAKISTAN ‐ FEDERAL GOVERNMENT ‐ 2016‐2017<br>NON HR RELATED EXPENDITURE<br>Cumulative % by number Cumulative percenage by amount 2 per. Mov. Avg. (Cumulative % by number) 2 per. Mov. Avg. (Cumulative percenage by amount)<br>PAKISTAN RUPEES<br>96.352 97.962 99.223 99.758 99.947 99.977 99.999 100.000 100.000<br>86.795<br>71.631 73.646<br>53.378<br>49.084<br>42.936<br>38.137<br>PERCENTAGE 29.534<br>21.873<br>12.686 16.206<br>0.493 1.161 2.940 6.498<br><!-- End of picture text --> Transactions Profile for the Pakistan Province of Punjab (FY 2016/17‐ Amount Ranges in Pakistan Rupees).

<!-- Start of picture text --> PUNJAB<br>PKR<br>96.145 98.012 99.189 99.815 99.975 99.990 100.000 100.000<br>87.214 86.965<br>80.434<br>74.393<br>62.208<br>52.790<br>44.945<br>PRCENTAGE<br>36.419 34.179<br>26.184<br>12.887<br>6.897<br>1.008 2.421<br><!-- End of picture text --> 26
'I ~~~~~~~~~~~~~~~~~~~~~~~~~~0In 0 ft _40_ ~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ - I ata **LI,** .... ~~~~~~~~~~~~~~~~~~~~~~...... _m_ o -4- I I--- _ _ _ _ _ --_ _ _ _ _ _.......0 CD, _ _ _ _ _ __ _ _ _ _ _ _ to rn **~** ~ ~ **~** ~ ~ ~ ~ ~~~~~~~~~~I. m jo I~~~~~~~~~~~~~~~~~~mU 304~~~~~~~~~~~~~~~~~ CD m~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Another strand of the literature examines the idea that disparities are due to social norms and institutions. Although developed countries do not have social customs that limit freedom or inferior property rights such as those described in figures 3 and 4, norms regarding the division of labor in the household, especially those regarding child rearing, may be a limiting factor. In her analysis of the World Value Surveys, Fortin (2005) finds a strong correlation between “traditional family norms” and both female labor-force participation and a wage gap. Ganguli et al. (2010) find that marriage and motherhood are important factors in explaining the correlation between reduced education inequality and female labor-force participation. Using historical microlevel census data, they show that the reduction in education inequality is not matched by a reduction in marriage and the motherhood gap (that is, the difference in labor force participation between married and single women and between women with and without children). Evidence on historical patterns of female labor force participation in the United States is consistent with the idea that gender differences in the production function for children play an important role in explaining gender disparities in economic outcomes. For instance, Albanesi and Olivetti (2009) show that the introduction of formula milk, which reduces gender differences in child-feeding skills, has played a crucial role in explaining the increase in labor-force participation by married women in the United States over the last century.

There is no doubt that technological progress (e.g., formula milk, the contraceptive pill) has reduced women’s comparative advantage in child rearing, but important differences remain in the division of housework and child care. The recent evidence presented in the 2012 World Development Report indicates that, compared with men, women devote much more time to housework and child care at the expense of “market time” in both developed and developing countries.

Parental-leave policies are likely to be a factor in these stark differences in time use. For instance, parental-leave policies in all OECD countries clearly place the mother in charge of caring for newborns. In 2007, the average duration of maternal leave at full pay in OECD countries was just under 20 weeks, and all countries except the United States and Australia had a minimum leave of five weeks. In contrast, full-pay paternal leave for at least five weeks is only allowed in three of the 30 OECD countries.<sup>6</sup> Progress toward gender parity in child care may not have an immediate effect on cultural norms, but there is evidence that 17