rafmacalaba/datause-encoder-data huggingface dataset:
- **Bisschop, Paul, Stephen Kastoryano, and Bas van der Klaauw** , “Street Prostitution Zones and Crime,” _American Economic Journal: Economic Policy_ , November 2017, _9_ (4), 28–63.

- **Borker, Girija** , “Safety First: Perceived Risk of Street Harassment and Educational Choices of Women,” _World Bank Working Paper 9731_ , 2021, (July).

- **Bursztyn, Leonardo, Alessandra L Gonz ́alez, and David Yanagizawa-Drott** , “Misperceived social norms: Women working outside the home in saudi arabia,” _American economic review_ , 2020, _110_ (10), 2997–3029.

- **Cohen, Jessica and Pascaline Dupas** , “Free Distribution or Cost-Sharing? Evidence from a Randomized Malaria Prevention Experiment,” _The Quarterly Journal of Economics_ , 02 2010, _125_ (1), 1–45.

- **Cohen, Mark, Roland Rust, Sara Steen, and Simon Tidd** , “Willingness-to-pay for crime control programs,” _Criminology_ , 2004, _42_ (1), 89–110.

- **Cullen, Julie Berry and Steven D Levitt** , “Crime, Urban Flight, and the Consequences for Cities,” _Review of Economics and Statistics_ , 1999, _81_ (May), 159–169.

- **Cunningham, Scott and Manisha Shah** , “Decriminalizing Indoor Prostitution: Implications for Sexual Violence and Public Health,” _The Review of Economic Studies_ , 12 2017, _85_ (3), 1683–1715.

- **Datafolha** , “Ass ́edio sexual (PO813823),” Technical Report, Datafolha Instituto de Pesquisas 2015.

- **Field, Erica and Kate Vyborny** , “Women’s mobility and labor supply Experimental evidence from Pakistan,” _ADB Working Paper_ , 2022, (1629317), 1–20.

- **Field, Erica M., Rohini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer Moore** , “On Her Own Account: How Strengthening Women’s Financial
There is little doubt that economic growth reduces poverty (e.g., Dollar and Kraay 2002). However, sectorial growth rates might have a different impact on the poor. Ravallion and Datt (1996) do not find any impact of Indian manufacturing growth on the poor, even in urban areas, whereas rural growth reduced poverty both in rural and urban areas. Gallup, Radelet, and Warner (1997) find that a one percent growth in agricultural GDP per capita leads to a 1.61 percent increase in per capita income of the bottom quintile, whereas an equivalent increase in industrial GDP increases the income of the bottom quintile by only 1.16 percent.<sup>14</sup> Unfortunately the data used by these authors only included 35 developing countries and the differences in the sector effects were not statistically significant. Timmer (2002) studies the contribution of agricultural and non-agricultural output per worker to the income per capita across quintiles of the income distribution. Although he finds a slightly greater impact of agricultural output, it is unlikely that the sector differences in his estimations were statistically significant.

The data on income shares across quintiles come from Dollar and Kraay (2002) and the per capita incomes from the World Bank’s World Development Indicators 2003.<sup>15</sup> The resulting data span from 1960 to 2000. The econometric estimations utilize a panel of five-year averages. To study the contribution of agricultural GDP to the income of different quintiles, we estimate our specifications using GMM in order to deal with the endogeneity of the sector GDPs. This is the main difference between our work and what has been done so far in the literature. More specifically, the GMM IV system estimations, which were explained in Section III.C., use appropriate lags of the sectorial GDPs as instrumental variables.

Table 4 reports the impact of output per worker in agriculture and non- agriculture on the income per capita of each quintile while also controlling for regional heterogeneity. The results

> 14 This paper was cited by Timmer (2002). We thank him for providing us a copy of it. 15 See Appendix C for data definitions and primary sources.

21
|**Turnout**<br>46.8%<br>45.6%<br>45.6%<br>45.3%<br>48.1%<br>53.8%<br>53.8%<br>54.5%<br>48.9%<br>48.9%<br>45.2%<br>51.4%<br>53.8%<br>52.9%<br>39.4%<br>56.6%<br>50.0%<br>63.7%<br>46.8%<br>46.8%<br>59.5%||
|---|---|
|**Approval**<br>46.3%<br>70.6%<br>70.8%<br>36.2%<br>49.9%<br>43.2%<br>48.4%<br>56.0%<br>67.8%<br>68.0%<br>36.2%<br>59.6%<br>57.2%<br>52.9%<br>78.4%<br>50.3%<br>25.9%<br>41.0%<br>78.4%<br>60.1%<br>38.3%||
|**Outcome**<br>Failed<br>Passed<br>Passed<br>Failed<br>Failed<br>Failed<br>Failed<br>Passed<br>Passed<br>Passed<br>Failed<br>Passed<br>Passed<br>Passed<br>Passed<br>Passed<br>Failed<br>Failed<br>Passed<br>Passed<br>Failed||
|**Orientation**<br>Restrictive<br>Restrictive<br>Restrictive<br>Restrictive<br>Restrictive<br>Expansive<br>Expansive<br>Expansive<br>Restrictive<br>Restrictive<br>Restrictive<br>Expansive<br>Restrictive<br>Restrictive<br>Restrictive<br>Restrictive<br>Restrictive<br>Restrictive<br>Expansive<br>Expansive<br>Restrictive||
|**Type**<br>PI<br>OR<br>OR<br>PI<br>PI<br>MR<br>MR<br>OR<br>OR<br>OR<br>PI<br>MR<br>PI<br>PI<br>OR<br>PI<br>PI<br>PI<br>OR<br>OR<br>PI|20.<br> (PI)|
|illegal immigration<br> law on asylum<br> law on emergency measures on asylum and foreigners law<br>lating immigration<br> abuses in asylum law<br> bill on ordinary and facilitated naturalization (2nd generation)<br> bill on ordinary and facilitated naturalization (3rd generation)<br>on of free mobility to new EU member states (EU-10)<br> law on foreigners<br> law on asylum<br>ocratic naturalization<br>l of the EU-Switzerland bilateral agreement on free mobility<br> the construction of minarets<br> deportation of foreign criminals<br>modifcation of asylum law<br> mass immigration<br>erpopulation (ECOPOP)<br> actual deportation of foreign criminals<br> law on asylum<br>facilitated naturalization of third generation foreign youth<br>erate immigration (Limitation Initiative)|on the issues of migration and asylum in Switzerland between 1996 and 20<br>d Stutzer (2021), adding the vote No. 631 to their list.<br>datory referendum (MR), optional referendum (OR) and popular initiative|
|**Label**<br>Against <br>Federal <br>Federal <br>For regu<br>Against <br>Federal <br>Federal <br>Extensi<br>Federal <br>Federal <br>For dem<br>Renewa<br>Against <br>For the <br>Urgent <br>Against <br>Stop ov<br>For the <br>Federal <br>On the <br>For mod|pular votes <br>ermann an<br>s are: man|
|**e**<br>2.1996<br>6.1999<br>6.1999<br>9.2000<br>1.2002<br>9.2004<br>9.2004<br>9.2005<br>9.2006<br>9.2006<br>6.2008<br>2.2009<br>1.2009<br>1.2010<br>6.2013<br>2.2014<br>1.2014<br>2.2016<br>6.2016<br>2.2017<br>9.2020|ts all po<br>ws Zimm<br>ular vote|
|**Dat**<br>01.1<br>13.0<br>13.0<br>24.0<br>24.1<br>26.0<br>26.0<br>25.0<br>24.0<br>24.0<br>01.0<br>08.0<br>29.1<br>28.1<br>09.0<br>09.0<br>30.1<br>28.0<br>05.0<br>12.0<br>27.0|table lis<br>ist follo<br> of pop|
|**o.**<br>2<br>4<br>5<br>7<br>1<br>0<br>1<br>9<br>4<br>5<br>2<br>0<br>7<br>2<br>1<br>0<br>8<br>7<br>4<br>9<br>1|his <br>he l<br>ypes|
|**N**<br>43<br>45<br>45<br>46<br>49<br>51<br>51<br>51<br>52<br>52<br>53<br>54<br>54<br>55<br>57<br>58<br>58<br>59<br>60<br>60<br>63|T<br>T<br>T|

42
The difference between the minimum and maximum values for the levelized costs of solar energy technologies (and also other energy technologies) are wide due mainly to large variations in overnight construction costs and to different capacity factors. For example, the overnight construction costs of grid connected solar PV system vary from US$2,878/kW to US$7,381/kW (NEA/IEA, 2010). Similarly, the overnight construction costs of CSP vary from US$4,347/kW (NEA/IEA, 2010) to US$5,800/kW (Lazard, 2009). The capacity utilization factor of simple cycle gas turbine varies from 10% (Lazard, 2009) to 85% (NEA/IEA, 2010). Furthermore, very different economic lives are assumed for hydro, coal and nuclear plants.

**Figure 4: Levelized Cost of Electricity Generation by Technology (2008US$/MWh)**

<!-- Start of picture text --> Capital Cost O&M Cost Fuel Cost<br>$719<br>7 %<br>$362<br>$336<br>98 %<br>11 % 22 %<br>$241<br>$192 $194 16 % 20 %<br>10 % 17 % $138 $148 $146<br>89 % $101 31 % $88 10 %4 %<br>90 % 83 % 25 %75 %$76 84 % 82 % 12 % $62 6 % 67 %27 6 %% 85 % 11 % $65 4 % 58 % 12 %88 %$29 92 %8 % 17 %19 %64 %$77 11 %58 % 23 %14 %63 %$43 47 %13 %40 % 16 %19 %65 %$76 85 %<br>Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max Min Max<br>Solar PV CSP Wind Gas CC Gas CT Hydro IGCC* Coal^ Nuclear<br><!-- End of picture text --> Note: * IGCC with carbon capture and storage. ^Supercritical coal.

It is also interesting to observe the contributions of various cost components (e.g., capital, O&M and fuel costs) to levelized cost. While capital cost accounts for more than 80% of the levelized cost for renewable energy technologies, it accounts for less than 60% in conventional fossil fuel technologies (e.g., coal, gas combined cycle). Fuel costs are the major components in most fossil fuel technologies.

13
The answer to this question in the case of Russia depends a great deal on the extent to which China and India’s patterns of exports overlap with each other and with Russia’s. As shown in Dimaranan, Ianchovichina and Martin (2007), the export patterns of China and India have been quite different, with India relying much more heavily than China on exports of services. Even within merchandise trade, their export patterns have been radically different at the six-digit level of the Harmonized System, with only one product—refined petroleum—appearing on the two countries’ top-25 list of products, which accounted for 58.4 percent of India’s merchandise exports and 38.4 percent of China’s in 2004.<sup>1</sup> Neither has an export composition anything like that of Russia. This difference in export patterns reduces the risk of a collision in which the prices of exports by China, India, Russia and other developing countries are all simultaneously depressed.

Complicating factor in the analysis is the fact that, while both China and India are more labor-abundant than developed economies, relative factor endowments and income levels vary substantially across regions within China and India. Many of China’s coastal areas are in a different income-level category than the much more labor-abundant inland provinces. This heterogeneity can influence the range of goods produced and exported by China, and therefore it helps explain the disproportionate similarity of China’s export bundle with those of the developed countries (Schott, 2007). India’s large number of skilled workers also implies that there may be a lot more competition between India and developed economies than suggested by its relative endowment shares.

Although China and India’s merchandise exports are dominated by manufactures (World Bank 2003), the composition of these manufactures and the approach to their production differ considerably. While their shares of manufactured intermediate inputs in non-fuel imports in 2004 were very similar, their shares of imports of parts and components differed sharply as might be expected given China’s much greater role in global production sharing. In 2004, imports of parts

> 1 A notable feature of China’s list is the prominence of computer and electronic equipment products under Chapters 84 and 85. These two chapters, which also include non-electronic equipment, alone accounted for almost 42 percent of China’s exports in 2004, up from 16 percent in 1994. In India, three HS products under Chapter 71 (diamonds and jewelry) and refined petroleum under Chapter 27 likewise accounted for 28 percent of total exports.

7
Page 83 what percent of the poor are women; which groups of women; urban/rural, marital status, age education status, nutritional status; what percent of poor households are headed by women; which women live in poverty even in households with income above the poverty line;

- number of income earners per child (specifying female-headed households).

# Data Considerations

7. Obtaining the data and information profile outlined in the preceding sections will be dependent on data availability. Data sources should be evaluated, as always, for coverage, completeness and quality. Are women's activities accurately reflected and quantified or, perhaps due to the sampling methodology and definitions used, do the data understate women's economic participation? In some countries, social and cultural attitudes restrict women's public activities, so that women are likely to be less visible in the economy despite an often large "work" contribution; there, an extra effort must be made to document women's roles, activities and contributions.

8. Often, women's reported labor force participation and productive - employment rates will be unduly low for reasons which may vary by country or culture. This is most likely to be true for the rural areas of low income countries, where the distinction between home subsistence production, part-time production for the market, and unpaid family work may be unclear. Some male household heads may prefer to tell enumerators that the women in their households do not work. Enumerators may classify certain work arbitrarily, or in accord with official standards, as non-work (e.g. only cash compensated employment may be defined as "work"). Women themselves, regardless of the range of their activities, may consider their primary responsibility to be that of homemaker; unless probed about the nature of their activities in further detail, they will answer as such leaving themselves out of the labor force. Depending on the timing of the enumerator's visit and the nature of the survey questions, large periods of seasonal work may be overlooked; this is more likely to be an issue for women than men, as a greater proportion of women are in non-market subsistence agriculture and market work is more seasonal.

9. Finally, a definitional concern may emerge. Many of the traditional "\women's tasks" - such as water and fuel collection, often requiring several hours per day of arduous work - would be counted in the service sector of GDP if delivered via the formal physical infrastructure or through trade channels. The fact that women's labor is available to their families without remuneration and that these tasks are undertaken only by women (and children) has allowed such tasks to be omitted from formal definitions of work for the purposes of lauor force participation estimates, national income accounting, and other statistical measures.
spread between lending and deposit rate was 10.3 percent in Africa and 8.2 percent outside. While there are many reasons that spreads and margins are higher in Africa, as we will discuss below, one important reason is higher operating costs. Specifically, overhead costs in the median African financial system stood at 5.5 percent of total assets, while they were 3.4 percent outside Africa. On the other hand, African banks are also more profitable than banks outside Africa. The return on assets (ROA) stood at 2.1 percent in the median African country, while it was 1.5 percent outside Africa. We will revisit this issue below with banklevel data.

The higher interest rate spreads go hand in hand with greater concentration and lower competitiveness in African banking markets. While the share of the five largest banks was 81 percent in the median African country in 2011, it was 64 percent outside Africa. While there are countries in Africa where aggregate data point to five banks making up the whole or almost the whole banking system (Cape Verde, Gambia, Lesotho, Swaziland and Togo), there are only a few such countries among the much larger group of non-African developing countries. Greater concentration can also partly explain the lower degree of competition within African banking system. The median Lerner index, which is the mark-up between marginal revenue and costs, is 30 percent in the median African country, while it is 25 percent outside Africa. It is important to note that the correlation between concentration and the Lerner index of market power is relatively low within Africa, at only 11 percent, suggesting that market structure is only one, and maybe not the most important, determinant of the lack of competition within Africa, consistent with cross-country evidence (Claessens and Laeven, 2004).

Africa’s banking systems focus mostly on the short-end of the yield curve, as illustrated by the maturity structure on both the asset and liability sides of African banks’ balance sheets (Beck et al., 2011). More than 80 percent of deposits are sight deposits or 9
**-36 -**

Figure 11, Africa's agricultural exports Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 = 100<br>1 70<br>150<br>1 30<br>1 10<br>90<br>70 1 ' I ' l I<br>1970 1972 1974 1976 1978 1980 1982 1984 1986<br>-FPE -UPE<br><!-- End of picture text --> Figure 12. Total agricultural production Comparing countries grouped by favorable and unfavorable policy environments <!-- Start of picture text --> Index 1979-81 = 100<br>125<br>115-<br>105 -<br>95<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988<br>FAO production index numbers<br>FPE -UPE<br><!-- End of picture text -->
labor market is an important factor contributing to the reduction in the fertility rate, which also produced a sharp drop in the dependency rate. However, fecundity is not homogeneous across Argentina’s provinces. The poorer provinces have a higher fertility rate than richer provinces; for example, Santiago del Estero, Misiones, and Formosa have a fertility rate above 3.2. Total desired fertility rates in poor provinces are lower than the actual fertility rate according to the author’s field visits in Chaco and Santiago del Estero. Similar findings are presented in Gacitua et al. (2001) for Salta and Misiones. This would indicate a substantial unmet demand for high quality and reliable family planning services, information, and resources.

|Table 2.5: Average<br>|Number of C<br>|hildren of H<br>|ousehold H<br>|eads<br>|
|---|---|---|---|---|
|ByLevelof Education|in Dispersed|Rural Area|sin Argenti|na,2003|
||Total|Nonpoor|Poor|Indigent|
|No education|1.8 (1.9)|0.89 (1.5)|2.5 (1.9)|3.6 (1.8)|
|Primary completed|1.9 (1.8)|1.4 (1.5)|2.6 (2.0)|3.2 (2.2)|
|Secondary completed|1.7 (1.4)|1.3 (1.2)|3.8 (.98)|3.0 (0.0)|
|University completed|1.1(0.8)|1.3(0.8)|NA|NA|
|Note: Standard deviations in p<br>Source: Own calculation based|arenthesis. Chil<br>on RHS 2003.|dren are define<br>|d as persons b|elow age 18.|

Another important development is the decline in the fertility differential between more educated and less educated household heads. Survey data from four provinces (Chaco, Santa Fe, Santiago del Estero, and Mendoza; see Section 3 for more information on the survey) show that parents with no or incomplete primary education have 1.8 children while those with complete tertiary education have 1.1 children (Table 2.5). Hence, education plays a key role both directly through increased income and wages (see Sections 5 and 6) and indirectly through the reduced fertility rate in poverty reduction.

11
|Theinvestment/outputrelation:||
|---|---|
|l(t)=ICOR(t+l)*[y(t+1)- y(t)]|(6)|
|Privateconsumptionfunction:||
|Cp=c*(Y-T+NCTR+NFY)|(7)|
|Importfunctions:||
|IMi=f(yC,RERi)|(8)|
|Manufacturingexportsfunction:||
|Xm=f(yf,RERm)|(9)|
|Otherexports:||
|Xi=(I+gxi)*Xi(t-1)|(10)|

The model can be solved using one of several approaches. First, all resource flows could be specified exogenously and the model could then be solved for the implied growth and inflation rates.<sup>7</sup> Second, resource requirements (including the additional foreign exchange needs) could be estimated for given growth and inflation assumptions. Most users of the model adopt the latter approach for computational convenience. Either approach could be considered as a first step in an iterative process that converges to a feasible solution to the model.8 To evaluate the extemal sustainability of the country, a module that incorporates external debt infonnation supplements the flow-of-funds framework. This enables the calculation of the debt servicing capacity of the country taking into account the existing

> In World Bank terminology this is called the "availabilities" approach to model solution.

> Since no explicit objectivc function is used, the solition is best described as a feasible one. The importance of iteration in this process cannot be overemphasized.
- 14 -

85. In each of the tables, the estimates shown for changes In support prices or support and target prices are averages of the response by a given variable to plus and minus ten percent changes.

The 1981-82 results for productlon and prices given In Table 1 show strong responsiveness of maize and sorghum production to U.S. policy with maize production in the first year Increasing 3.3 percent In response to a 10 percent Increase In U.S. support prices alone or to a parallel ten percent Increase In support and target prices and sorghum production decreasing 2.5 percent In response to the same increases. The Induced change In soybean productlon is -1.2 to -1.4 percent. These effects are due to acreage substitution In favor of malze and against sorghum and soybeans.

In contrast to the greater response for foodgrains In the second year reported by Just (1989a), the MEXAGMKTS model results show a declining response In the second year. This difference In pattern of response may reflect the practice In Mexico of announcing guarantee prices before the start of the maln crop season In order to influence planting decisions. This could result In a pattern of a larger production response In the first year for an annual model when behavioral relatlons are estimated using historical data gathered under a different pollcy regime.

Support for this Interpretatlon of the pattern of response by crops Is provided by the elasticity estimates for livestock prices given In table 1, which show a pattern of significant Increases In the second year In accord wlth the pattern also reported by Just
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