rafmacalaba/datause-encoder-data huggingface dataset:
|Greece<br>Iran|388.59<br>396.82|.0272<br>.1331|64<br>65|31<br>-5|27<br>-10|
|---|---|---|---|---|---|
|Oman<br>|460.90<br>|.5215<br>|66<br>|29<br>|45<br>|
|Afghanistan|472.13|.8091|67|-52|-27|
|<br>Bosnia|472.90|<br>.8094|<br>68|<br>14|<br>-6|
|Bolivia<br>|475.10<br>|.8101<br>|69<br>|-20<br>|-24<br>|
|Benin|479.11|.8116|70|-29|-1|
|Philippines|496.68|.0488|71|-6|-21|
|<br>Algeria|533.93|.1536|72|4|-4|
|<br>Croatia|539.26|.3286|73|31|18|
|Yemen|545.67|.6931|74|-18|-9|
|Cameroon<br>|576.26|.8407|75|-33|-33|
|Argentina|586.46|.0653|76|7|-1|
|<br>Macedonia|672.15|.8617|77|6|-5|
|Bulgaria<br>|696.83<br>|.3662<br>|78<br>|34<br>|35<br>|
|Vietnam|714.14|.1863|79|-11|-9|
|Sudan|775.28|.7726|80|-38|-32|
|Indonesia|846.45|.0586|81|-15|-26|
|Panama|884.80|.5075|82|20|32|
|Bahrain<br>|900.87<br>|.6414<br>|83<br>|52<br>|57<br>|
|Senegal|924.21|.8054|84|38|23|
|<br>Syria|955.45|.5925|85|2|27|
|Côte d'Ivoire|1061.6|.7538|86|-26|-30|
|Albania|1144.2|.8396|87|-1|-13|
|Burkina Faso<br>|1171.6<br>|.9181<br>|88<br>|39<br>|34<br>|
|Gabon|1176.1|.7748|89|15|21|
|Madagascar|1219.9|.9212|90|47|10|
|<br>Egypt<br>|1253.2<br>|.2575<br>|91<br>|19<br>|34<br>|
|Iraq|1268.1|.4917|92|-28|-17|
|<br>Azerbaijan<br>|1280.8<br>|.7911<br>|93<br>|-9<br>|-17<br>|
|Serbia|1356.3|.5968|94|31|30|
|<br>Kenya|1481.5|<br>.6669|<br>95|<br>-6|<br>-16|
|Bangladesh<br>|1549.4<br>|.6365<br>|96<br>|-18<br>|-24<br>|
|Haiti|1651.8|.7849|97|-20|-21|
|Niger|1812.2|.9463|98|14|-1|
|Georgia|18196|8480|99|38|-4|
|<br>Mozambique|.<br>1900.6|.<br>.9001|<br>100|<br>27|<br>21|
|<br>Burma<br>|1929.8<br>|.9495<br>|101<br>|-20<br>|-18<br>|
|Turkmenistan|1947.4|.9024|102|-14|-15|
|Zambia|1961.4|.9030|103|10|17|
|Nieria|21012|1354|104|-5|-10|
|g<br>Ghana|.<br>2191.0|.<br>.6941|<br>105|<br>46|<br>54|
|Chad|2207.3|.9557|106|-9|-15|
|Mongolia|2302.7|.9575|107|32|40|
|Tanzania|2307.3|.8408|108|28|35|
|Guinea|2410.7|.9203|109|5|-6|
|Djibouti<br>|2425.4<br>|.9596<br>|110<br>|29<br>|41<br>|
|Kazakhstan|2475.6|.4112|111|8|27|
|Congo<br>|3088.9<br>|.8234<br>|113<br>|8<br>|12<br>|
|Equatorial Guinea|3102.5|.8790|114|-8|-9|
|<br>Mauritania|3242.8|<br>.91164|115|<br>55|<br>52|
|Liberia<br>|3426.8<br>|.8152<br>|116<br>|10<br>|11<br>|
|Libya|3550.3|.5086|117|19|26|
|<br>Zimbabwe|4122.5|.9298|118|5|33|
|Uzbekistan|4290.9|.8695|119|9|13|
|<br>Cambodia|4645.2|<br>.9174|<br>120|<br>9|<br>16|
|Neal|53544|9633|121|43|25|
|p<br>Sao Tome & P.|.<br>5905.4|.<br>.9832|<br>122|<br>27|<br>41|
|Uganda<br>Mali|5911.4<br>63401|.9034<br>9388|123<br>124|29<br>58|28<br>54|
|<br>Malawi|.<br>8598.1|.<br>.9657|<br>125|<br>40|<br>47|

_Notes_ : Index computed using all cases regardless of outcome and administration. Countries for which _PACI_ z = 0 have been ranked according to the negative of Pr zero_ cases zPACI_ <mark>.</mark> Countries for which _PACI_ z = 0 and Pr zero_ cases zPACI_ > 0.015 and countries for which _PACI_ z > 10000 have been excluded from the list. WB-CCI: World Bank Corruption Control Index, 2005. TI-CPI: Transparency International Corruption Perceptions Index, 2005.

16
18

|,e.,'<br>i.<br>'<br>in<br>'Aat.|ntEffic|ienc<br>R|at<br>:||
|---|---|---|---|---|
|Abatement**Efficiency Rate as**% of<br>**(C,ast **of<br>**P)**<br>|Pollution<br>|/capital<br>|||
|**50%**|100%|200%|500%||
|Foderal Xncpmaction<br>Required**(for**<br>**10094**<br>low pollution aversion; X-.1)|<br>100%|<br>100%|<br>100%||
|Federal<br>Penalty Level Required**an**<br>30.7%<br>**%**of originalVATrevenue|30.7%|**30.7%**|<br>**30o7%**||
|Net Federal Fine Revenue as % of<br>13.5%<br>original<br>VATrevenue (for a<br>conetant<br>fine)|<br>13.3%|<br>11.8%|<br>5.8%||
|Reculting State Monitorlng<br>**46%**|**48%**|<br>51%|<br>**71%**||
|Minimum Monitoring<br>required<br>**for**<br>firm toinvest**in**control<br>1.7%|2.3%|3.4%|6.6%||
|Net State<br>Revenue**as %**of<br>original<br>VAT**revenue**<br>251%|179%|<br>128%|<br>**98%**||
|Nominal Pollution Tax required to<br>reduce pollition to 0 with<br>perfect<br>SEPAmonitoring<br>10.4cts/lb|5.9cts/lb|2.6cte/lb|.7cts/lb||
|FlRM (at<br>the<br>level<br>of monitoring<br>implied<br>by the<br>**federal attitude)**|||||
|Inveotment<br>**in**Pollution<br>Control<br>as**%**oftotal capital<br>**28%**|**24%**|**16%**|<br>11%||
|Max.investment<br>**in**control<br>as a**t**|||||
|of total<br>capital<br>**67%**|50%|33%|<br>17%||
|Pollution Level am%of original<br>**55%**|55%|51%|<br>32%||
|Value added**am**%**of original**<br>**79%**|**87%**|**93%**|<br>**97%**||
|<br>3.2.2. Do States React to**Chanoes in**Federal F<br>The previous set of simulations establ|ines?<br>ished that|for dirty|industries|, the|
|federal government should monitor the states pe|rfectly whe|n the fines|are set at|**their**|
|optimal<br>level.<br>**But what**if the<br>fines<br>cannot<br>**be**|**set**at the|optimal<br>leve|l?<br>**This**im|plies|
|that the effectivefine,<br>considering<br>both inspe|ction andfin|e<br>levels,|will**also**be|**sub-**|
|optimal.<br>**This**will<br>**reducethestates'**<br>**incentive**|**to**adopt<br>the|<br>monitorin|g rate that|will|
|manituite<br>**social**welfare.**Since ltis**generally|**unrealistic**|to assume|that<br>**fines**|will|

. 3.2.2. Do States React to **Chanoes in** Federal Fines?
17 tech and high-skill products. For instance, many observers point to the fact that China’s export-product mix is more similar to that of the OECD countries than we would expect on the basis of China’s modest per capita GDP.<sup>17</sup> In a short- and medium term perspective, these worries are, however, exaggerated. China is a large _importer_ of hightech components, which are assembled to finished products with the help of low-skilled labor, for sales both in domestic and foreign markets. Thus, the value added of Chinese export of such products is still mainly low-tech and low-skill.<sup>18</sup> Developed countries will have an overwhelming comparative advantage, and a trade surplus, in high-tech and highskill products as long as China continues its large import of high-tech and high-skill components in domestically produced final products. Over time, the situation is, of course, bound to change. The value added share of Chinese export would be expected to continue to rise as a result of the gradual upgrading of the high-tech and high skill components of China’s export production. Trade between China and today’s developed countries will then increasingly consist of intra-sector trade based on specialization within different kinds of high-tech and high-skill product areas.

Two other characteristics of China’s economic system – not explicitly highlighted in Figure 1 – should also be mentioned. One is that _factor markets_ , in particular the market for capital and credit, have been reformed less than product markets. There is, however, an emerging labor market, with gradually more flexible relative wages and an increased return on investment in human capital; see, for instance, the discussion in Zhang et al. (2005) and Meng (2005). One background factor is that state firms, which used to shoulder the main responsibility for providing and securing jobs, increasingly adjust their workforce to what is needed for actual production, and this has resulted in large shedding of workers since the second half of the 1990s (Dong and Zu, 2006).<sup>19</sup> As a result, “lifetime employment” in state firms and truly collective firms is gradually becoming replaced by less permanent and more market-oriented labor contracts.<sup>20</sup> But the flexibility

> 17 For an attempt to measure this similarity and its development over time, see, for instance Schott (2005).

> 18 The research on this issue is still rather thin. See, however, Cao (2004); Hsiung (2002); Leydesdorff and Ping (2005); and Schwaag Serger and Widman (2005). 19 The number of workers in the SOEs fell from 110 to 66 million between 1995 and 2003, and the number of workers in collective enterprises from 31 to 10 million (Ministry of Labor and Social Security, 2005). 10-20 percent of those previously employed by state firms were still counted as “laidoff workers” ( _xiagang gong_ ) several years afterwards; see, for instance, Cook (2000).

> 20 About half the workforce of SOEs seems to have been on terminable labor contracts in the late 1990s (State Bureau of Statistics of China, 1998; Hussain, 2000a).
_Table 6: OLS Imputation Model Results for Ethiopia - Dependent Variable: GPS-Based Plot Area (HA)_

||**Full Sample**|**Plots[d <= 1km]**|**Plots[d <= 500m]**|
|---|---|---|---|
|**_Plot Area_**||||
|Farmer-reported plot area (Ha)|0.871***|0.827***|0.865***|
||(0.004)|(0.004)|(0.004)|
|Unit reported: Square Meters †|0.278***|0.270***|0.380***|
||(0.021)|(0.023)|(0.027)|
|Unit reported: Timad†|0.241***|0.253***|0.371***|
||(0.018)|(0.021)|(0.025)|
|Unit reported: Boy †|0.160***|0.167***|0.285***|
||(0.020)|(0.022)|(0.026)|
|Unit reported: Senga †|0.301***|0.247***|0.336***|
||(0.024)|(0.027)|(0.031)|
|Unit reported: Kert †|0.188***|0.200***|0.295***|
||(0.028)|(0.030)|(0.034)|
|**_Plot Manager Characteristics_**||||
|Female †|-0.011*|-0.007|-0.008|
||(0.007)|(0.007)|(0.007)|
|Age (Years)|-0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|
|Education (Years)|-0.002**|-0.002***|-0.002*|
||(0.001)|(0.001)|(0.001)|
|Religion: Orthodox †|0.023|0.037|0.031|
||(0.024)|(0.025)|(0.028)|
|Religion: Protestant †|0.030<br>|0.046*<br>|0.043<br>|
||(0.025)|(0.025)|(0.029)|
|Religion: Muslim †|0.022|0.024|0.019|
||(0.024)|(0.025)|(0.028)|
|Religion: Traditional †|0.028|0.045|0.039|
||(0.037)|(0.039)|(0.042)|
||0.030|0.046*|0.043|
|**_Plot Characteristics_**||||
|Cultivated †|0.037***|0.030***|0.028***|
||(0.008)|(0.008)|(0.009)|
|Pasture †|0.078***|0.073***|0.066***|
||(0.011)|(0.010)|(0.011)|
|Fallowed †|0.047***|0.047***|0.040***|
||(0.013)|(0.013)|(0.015)|
|Soil quality good †|<br>-0.013*<br>|<br>-0.007<br>|<br>-0.012<br>|
||(0.007)|(0.007)|(0.008)|
|Use of organic fertilizer †|-0.040***|-0.044***|-0.035***|
||(0.008)|(0.008)|(0.009)|
|Use of hired labor †|0.070***|0.063***|0.051***|
||(0.010)|(0.010)|(0.012)|
|Irrigated †|-0.022|-0.014|-0.010|
||(0.015)|(0.015)|(0.016)|
|**_Household Characteristics_**||||
|# of HH members - [0,5]|0.003|0.003|0.003|
||(0.003)|(0.003)|(0.003)|

18
the likelihood of being employed in farm activities, but reduces (increases) the likelihood of being engaged in informal (farm) non-farm activities. The positive impact of literacy however is mostly in small business, business and being self-employed. While literacy does have a positive impact on white collar activities, the coefficient is an eighth in magnitude to that of secondary education. In conjunction with the limited impact of literacy on being engaged in any non-farm work, this points to a higher demand for skills (measured using educational attainment) in non-farm activities over time, even amongst rural workers.

Considering the effect of being a gender or social minority, we note that consistent with the historical data from the NSS, women have a significantly higher likelihood of being employed in the farm sector. Thus, conditional on participating in the labor force, women have a 7 percentage point higher likelihood of being engaged in farm activities than men. In a departure from the findings using the NSS data, women however have a higher likelihood of being employed in formal non-farm activities, driven in particular by white collar occupations.

This is contrary to the situation for religious or social minorities (Muslims and SC/ST), who have a significantly lower likelihood of being employed in farm activities, and a significantly higher likelihood of being engaged in informal non-farm activities. For SC/STs, this – takes the form of wage labor thus, hailing from the SC/ST community increases a rural worker’s likelihood of working as a non-farm wage laborer by over 30 percentage points. Rural Muslim workers, in addition to having a higher likelihood of being employed as wage laborers, also have a higher likelihood of operating a small business. Workers from both groups continue to be excluded from more remunerative occupations such as business ownership, qualified self-employment, and white collar jobs. In conjunction with our findings in Section 3.1, this indicates significant limitations on upward mobility for individuals hailing from gender and social minorities.

# **3.5.2 Heterogeneity by Education and Bank Account**

Section 3.5.1 established the positive and significant impact of education on rural workers’ likelihood of being engaged in non-farm activities. Alternatively, it showed that women and minorities are significantly less likely to be engaged in such activities. Similar to the historical data from the NSS, only 25 (28) percent of rural women (minorities) aged above 20 have completed secondary education, raising concerns as to whether these individuals are unable to engage in non-farm activities due to the lack of human capital skills (as measured by education), or due to structural barriers which impede their participation. We conclude our empirical analysis by testing for heterogeneity across educational attainment using 2015 data to understand whether education can ameliorate the social disadvantages faced by these 29
around Rs.5,00,000.

The first SHG leader raised three issues followed by the second SHG leader who raised five issues, two of which (italicized ones) were raised exclusively by her (not raised by men who spoke preceding these women). Of these two issues, one was specific to the group while the other was relevant to improving community hygiene. Note the panchayat president’s affirmation of SHGs by making a special mention of their active participation in the _gram sabha_ and his informative response as he explains the resource constraints and the action that has been taken.

_Cluster talk_ is another important feature that emerged from our analysis of issues raised. This pattern is observed across all the _gram sabhas_ recorded in Tamil Nadu, and it is most prominent following the verbal interjection of a SHG President, who is a leader with perceived authority. This pattern raises the frequency of an issue being mentioned by women and is a likely explanation for the relatively high percentage of civil works issues mentioned by all women. The following extract provides an example of cluster talk (separate issues raised by the speakers are numbered).

[Marudur Round 2 [medium literacy], Karamadai, Coimbatore): 15 women & 35 men in attendance; meeting time 60 minutes– Several women spoke at the meeting. The SHG leader was the first member of the public to speak up at the meeting as soon as the president finished a long verbal report on the works completed and efforts made by him to address complaints made in the previous _gram sabha_ .] 29
majority of the inbound non-manufacturing investment stays on in Singapore while majority of the manufacturing investment coming into Singapore is re-invested in neighboring countries.

For aggregate inbound FEI, the interaction between GDP differences and skill differences is negative and significant for 1984-2003, but insignificant for manufacturing FEI. For the 1994-2003 period aggregate sample it is also insignificant, suggesting that this finding may be related to the time periods of the samples. A reduction in the difference in size between the parent countries and ASEAN (ie., an increase in the size of ASEAN) reduces the sensitivity of investment stocks in Singapore to Singapore’s skill differences. During the 1980s and again during the Asian financial crisis of 1997-1998, ASEAN was smaller in size relative to most parent industrialized countries. Over time as the ASEAN hosts, (with the exception of the 1997-98 period), grew in size relative to parent investors, the relationship between Singapore skill differences and inward investment stocks should get smaller or becomes negative (depending upon the partner country), ie., Singapore attracts more horizontal FEI. Finally, the interaction terms between the square of skill differences and host country trade costs are insignificant<sup>14</sup> .

More insight may be gained by computing total derivatives of FEI stocks using ASEAN GDP for inbound investment from industrialized countries, valued at the sample means of all variables in 1994 (the mid-year of the sample), and taking account of the sign of skill differences for particular industrial country investment partners in Table 5. Plugging in these values into equation (2) we find the following result: over the sample period an

> 14 These equations were re-estimated using absolute skill values but did not change the basic results.

29
be extracted. First, we received formal employment status, and earnings reported for social security, for the month of August 2013. This corresponds to a time period of approximately one and a half years after our follow-up survey, and two and a half years after the end of training. Secondly, in December 2013, we were given data on the first date that a worker was registered in the social security system after the end date of the course which he or she had applied for, as well as the date that they left this job, if this job ended. This data effectively covers the period from January 2011 up to the end of November 2013. It tells us month by month whether an individual was ever formally employed post-training, but since it does not report on second and subsequent formal jobs, not current formal employment status. We use this data to measure whether an individual had been formally employed by January 2012 (the mid-point of the follow-up survey), as well as to examine the trajectory of entry into formal employment.

Although we cannot fully capture the longer-term impact of training on total employment, our follow up survey indicates that, conditional on being employed at 20 hours a week, 82% the sample is formally employed. In addition, to the extent that training has any impact in measures from the follow up survey, it is concentrated in employment quality measures, such as formal employment.

# **4.3 Balance and Baseline Characteristics**

Random assignment to treatment and control occurred at the individual level and was done by computer using code written by the evaluation team. Consistent with this, the first few columns of Table 2 show balance on course and demographic characteristics using the administrative data available for the full sample. The remaining columns then examine balance for the sample interviewed at baseline, and for the sample interviewed at follow-up. Given that some baseline interviews took place after the course had started, we focus on comparing either time invariant or slow-moving individual characteristics collected during the baseline survey. The differences between treatment and control are small and magnitude, and the only significant difference at the 10 percent level is that the treated group surveyed at baseline is marginally less likely to have worked before. Given the number of variables tested, we view this as the result of chance, and that randomization has succeeded in providing comparable treatment and control groups. Applicants for the evaluation courses are, in general, relatively young and well-educated. The average applicant is 27 years old, and approximately 73 percent of them have completed high 11
# **Women’s Legal Rights over 50 Years: Progress, Stagnation or Regression?**

<mark>“Life must be lived forward, but it can only be understood backward.”</mark> _<mark>Kierkegaard</mark>_ “Study the past if you would define the future.” _Confucius_

## **Introduction**

_"Gender equality means having the same rights and opportunities." "It means the same rights at work and in the home." Two urban adult women, Lambayeque, Peru_ .<sup>2</sup> Discriminatory laws in many countries across the world continue to act as a significant brake on women’s entrepreneurship. Gender differences are not found in business laws, which are generally gender-neutral, but in laws that are ostensibly supposed to regulate the private space, such as family and inheritance laws. These laws can determine the amount of assets controlled and owned by women, restrict their mobility, and limit their autonomy to make everyday decisions, such as getting a job outside the home or going to court. The restrictions can trigger marriage resulting in a paradox; a married woman can have a similar legal capacity as a child when it comes to opening a bank account or signing a contract in that she cannot do it on her own.

This study tracks the evolution of key constraints to women’s and girls’ equal rights to property and restrictions on their legal capacity over the past 50 years across 100 countries. The database of legal indicators represents all geographic regions, legal traditions, and income levels (see annexes 1-3 for more details on methodology and country coverage).

The paper describes the nature and implications of these legal restrictions and analyzes how their prevalence has changed across countries and over time. It demonstrates that change is possible; over half of the constraints that were on the books in the 1960s were removed by 2010. Whereas 75 countries in the initial period had at least one constraint to women’s economic rights, only 47 did in 2010. Of the 75 countries with some initial inequalities, all but 18 countries had closed at least one legal gender gap. And 28 countries had eliminated all the inequalities analyzed here.

The extent of progress, however, is uneven. Eastern Europe and Central Asia, a region that began with very few inequalities, no longer has any that are followed here. Many high-income OECD countries started with several inequalities, but all have removed them and countries in Latin America have also made tremendous progress. Countries in Sub-Saharan Africa, which had a higher rate of constraints than any other region, have removed more than half of the constraints that had been on the books. East Asian countries have also halved the number of constraints. However, in the Middle East and North Africa and in South Asia, much less progress has been realized.

> 2 World Bank Dataset 2011a.

2
19

# 3.2 Comparison with the Conventional

# Measure of Private Savina.

Despite the sharp decline in private saving<sup>observed in 1990 at 15% the</sup> private saving rate is somewhat below the average<sup>of the decade but well above</sup> its laws in 1983 and 1987. This is in sharp contrast with what the conventional measure of private saving suggests (Figure 9):<sup>according to this</sup> measure private saving rates in 1990 are<sup>at their lowest level since 1981.</sup> The conventional private saving measure is systematically higher than the one proposed. This is largely due to the inflation-adjustment of domestic and foreign interest income and the inclusion of<sup>the inflation tax in our measure;</sup> note that the private sector a net creditor<sup>of</sup> both the public and foreign sectors. As Figure 10 shows the discrepancy between<sup>the two measures widens</sup> with inflation.

<!-- Start of picture text --> CONVENTKONAL L-ASURES OF SAVNG.<br>(In 1980 pesos as X of GOP)<br>0.35<br>0.3<br>0.25.-<br>0.2<br>0.15 . / Ne-<br>0.05 _ 0 I<br>-0.15<br>1980 19811982 198319841685 1986 1987 1988 1989 1990<br>ye"<br>-Private s"in -+ PLMc Sm* - Forin Sw4V<br><!-- End of picture text --> Figure 9