rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 100<br><!-- End of picture text --> <!-- Start of picture text --> Cases Involving Women and Children's Rights<br>Percentage of Cases in Favor of Complainants<br>100<br>80<br>60<br>40<br>20<br>0<br>1960 1970 1980 1990 2000 2010<br>Year<br>Percentage<br><!-- End of picture text -->

# Figure 6

<!-- Start of picture text --> Cases Involving SC/ST/OBC<br>Percentage of Cases in Favor of Complainants<br>100<br>80<br>60<br>40<br>20<br>0<br>1950 1960 1970 1980 1990 2000 2010<br>Year<br>Percentage<br><!-- End of picture text --> 19
Table 2: Length of school closures and average learning losses, by country

|**Country**|**Closure length weeks**|**Average learning losses**<br>**(SD)**|**Source**|
|---|---|---|---|
|Mexico|48|-0.55|Hevia et al 2021|
|Brazil|26|-0.32|Lichand et al 2021|
|Poland|20|-0.3|Jakubowski 2022|
|Russian<br>Federation|14|-0.27|Chaban et al 2022|
|Norway|7|-0.24|Skar et al 2021|
|China|7|-0.22|Clark et al. 2021|
|South Africa|22|-0.22|Ardington et al 2021|
|Switzerland|8|-0.2|Tomasik et al 2020|
|Italy|15|-0.19|Contini et al 2021|
|Belgium|9|-0.18|Maldonado and Witte 2022|
|Netherlands|10|-0.17|Haelermans et al 2021|
|Germany|10|-0.14|Ludewiget al 2022|
|United<br>States|23|-0.14|Average of: Bielinski et al<br>2021; Domingue et al 2021;<br>Kogan and Lavertu 2021;<br>Kuhfeld et al 2022; Locke et al<br>2021;Pier et al 2021|
|Czech<br>Republic|9|-0.11|Korbel and Prokop 2021|
|England|10|-0.09|EPI 2021|
|Spain|12|-0.05|Arenas and Gortazar 2022|
|Australia|8|0.00|Gore et al. 2020|
|Denmark|8|0.00|Birkelund and Karlson 2021|
|Japan|11|0.0|Asakawa and Ohtake 2021|

We also observed notable variation in learning loss by subject. The subjects most frequently analyzed in the reviewed studies are reading and mathematics. Of the 11 studies to include both, in 9 studies learning loss in mathematics exceeded that of reading, while reading losses exceeded mathematics in 2 studies.

Many studies have examined learning loss by gender, although the findings are not consistent. While some studies report greater learning loss among girls (Lichand et al 2021), others find
Although remittance flows are strong contributors to increased consumption and reduced poverty in origin countries, their magnitude has also raised questions about undesirable effects on the recipient economies. These issues are similar to those that emerge during periods of massive capital inflows. In particular, there is a concern that remittances could cause Dutch disease effects. In other words, the massive inflow of foreign currency could be associated with a real exchange rate appreciation and loss of international competitiveness, which, in turn, could lead to a decline in the production of manufactured and other tradable goods. Empirical studies looking at this question include include Acosta, Lartey, and Mandelman (2009), Lartey, Mandelman, and Acosta (2012), Amuedo-Dorantes and Pozo (2004), and Rajan and Subramanian (2005), all of which use cross-country data to document that real exchange appreciation follows remittance flows.

# **7 Institutions and social norms**

Finally, a thinner but rapidly evolving literature shows that temporary migration can impact origin countries’ institutions and social norms that matter for development. Foreign-educated individuals, for example may bring democratic change if they acquired education in democratic countries (Spilimbergo 2009; Mercier 2016). Using experimental data, Batista and Vicente (2011) show that emigration from Cabo Verde contributed to better governance at home. This is primarily driven by return migrants who lived in destination countries with good governance and democratic institutions. In Morocco, having a returnee from a Western European country in the household increases the demand for political and social change, after controlling for the double selection into emigration and return migration (Tuccio, Wahba, and Hamdouch 2019). Because better institutions usually translate into higher total factor productivity, such channels are partly captured by productivity responses. A positive impact of return migration on political participation rates and on electoral competitiveness has been found in Mali, which mainly stems from returnees from non-African countries (Chauvet and Mercier 2014). The impact of returnees on turnout was found to go beyond their own participation, and to affect electoral outcomes more strongly in areas where non-migrants are poorly educated, suggesting a diffusion of political norms from returnees to non-migrants.<sup>24</sup> In contrast with studies finding positive effects of temporary migration on institutions, an increase in remittance inflows associated with temporary migration can also lead to deterioration of institutional quality. In a cross section of 111 countries, a higher ratio of remittances to GDP leads to lower indices of control of corruption, government effectiveness,

> 24In contrast, Barsbai, Rapoport, Steinmayr, and Trebesch (2017) find that pro-democracy attitudes in Moldova were primarily transmitted due to spillovers from current migrants as opposed to return migrants.

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# **2.2 Informal employment in Eastern, Western, Northern and Southern Europe**

It appears that informality is most prevalent in the South and least prevalent in the Nordic countries, whilst the difference between the West and the East is, on aggregate, surprisingly small, especially as far as population-weighted estimates are concerned. According to population-weighted estimates based on respondents‘ status _during the survey_ (Table 2, panel A, left), the proportion of employees without a contract among all employees in 2008-2009 varies from 2.7% in the Nordic countries to 9.5% in the Southern Europe, whilst it is just above 5% in the West and in the East alike; when those who did not respond to the question regarding the contract are treated as not having a contract (which is a plausible assumption), the prevalence of work without a contract becomes higher in the East (6.7%) than in the West (5.5%), whilst it does not change much in the North and in the South. Equally-weighted estimates are substantially higher than the population-weighted ones for the South and for the West, disregarding the treatment of non-response. This is due to very high proportions of employees without contracts in a few relatively small countries: Cyprus (almost half), Greece and Israel (about one third), Ireland (close to one fifth), and Austria (one tenth); see Table 3 for details.

Total informal employment (i.e., employees without contracts, non-professional selfemployed operating solely, employers with 5 or less employees, and family workers) accounts for about 10% of extended labor force in the Northern Europe, about 14% in the West and in the East, and about 25% in the South; equally-weighted averages are again higher for the South and for the West (Table 2, panel A, right). The overall population-weighted average for the 30 countries covered is 15.7%, and equally-weighted average is 17.4%. Hence one out of six labor force members (and about one out of ten adult residents) in Europe has been working informally during the surveys conducted in 2008-2009. See Table 2 for more details.

Informal employment is often irregular or seasonal. During the periods of employment, shadow workers might become hard-to-reach by the surveyors if they work long hours or work far away from their residence. This is why, in principle, _estimates based on engagement in informal work during the last 12 months_ (rather than during the survey week) are more reliable. In the case of ESS such an approach also helps to address the potential seasonality issue (the season of the field work varies by country, see Table 3). ESS data provide detailed information about the last job (if any) of respondents who are currently non-employed, so that those who were employed informally can be identified according to definitions in Table 1.

Unfortunately, it is not possible to apply the 12 months reference period exactly, because for each respondent we know the month of the interview but only the year of the last 11
# **_5.2 Bribe intensity_**

Table 2 reports a series of six OLS regressions, according to equation (3), which estimates the payment of bribes as a share of the firm’s annual sales (bribe intensity). All regressions control for sector and region. Results are robust and stable and support the hypothesis developed above. The bargaining hypothesis suggests that bribery payments as a share of total sales depend on the firm size. An inverted U-shape relationship between size and bribe intensity was also found and, corroborating with the descriptive statistics illustrated in Figure 4, medium-size firms are the ones that suffer most from corruption in Mauritania. Since harassment takes up public officials’ time, they may focus on large firms in order to receive higher returns for their (time) investments. However, it might be true that officials may be content to accept lower bribe rates (as shares of the firms’ annual sales) from large firms, given that these will translate into higher absolute amounts. Public officials may also be reluctant to try to extract bribes from large firms given their networks with higher ranking local or national officials.

This is confirmed when the estimations use firm discrete categories for size. Only medium-size enterprises present significantly higher bribe intensity than micro enterprises. Kuncoro (2006) found a similar result for Indonesian firms using data for 2001: the coefficients of the three firm size dummies used in his OLS estimations suggested some degree of non-linearity in the bribe intensity function.<sup>15,16</sup> In addition, the bargaining hypothesis also suggests bribe intensity to be dependent on the firm’s investments as a share of total sales. Indeed, a 1 percent increase in investments as a share of total sales is associated with a 0.3–0.5 percent increase in bribe intensity, _ceteris paribus_ .

> 15 Kuncoro (2006) also created four size categories in terms of annual sales (i.e., small, smaller medium, larger medium, and large). In his specifications for bribe intensity, “small” (annual sales lower than Rp 1 billion) was the omitted category and the firm size (negative) coefficients that statistically differed from the omitted category were “larger medium” (annual sales between Rp 5 billion and Rp 10 billion) and “large” (annual sales greater than Rp 10 billion).

> 16 Svensson (2003), analyzing corruption among Ugandan enterprises, developed five OLS corruption regressions having graft in absolute terms (US$) as the dependent variable and he found firm size in terms of employment to be one of its determinants. Everything else held constant, one additional worker would be associated with an increase in bribes paid to government officials between of US$10.2 and US$16.4.
4

**Table 1: World Merchandise Exports and Shares by Income Group and Region**

||19<br>|80<br>|199<br>|0<br>|20<br>|00<br>|20<br>|10<br>|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|Exports<br>($bill)|Share<br>in %|Exports<br>($bill)|Share<br>in %|Exports<br>($bill)|Share<br>in %|Exports<br>($bill)|Share<br>in %|
|**BY INCOME LEVEL**|||||||||
|Developed economies|1,314|66.6|2,653|76.4|4,418|68.4|8,909|58.5|
|Developing economies|597|30.2|837|24.1|2,035|31.5|6,268|41.2|
|High income developing economies|254|12.9|363|10.5|854|13.2|2,108|13.8|
|Upper middle income economies|211|10.7|332|9.5|873|13.5|3,175|20.8|
|Lower middle income economies|120|6.1|128|3.7|285|4.4|908|6.0|
|Low income economies|12|0.6|14|0.4|23|0.4|77|0.5|
|Of which: Least developed countries|15|0.8|18|0.5|36|0.6|162|1.1|
|**Memo:**|||||||||
|Developing econ, excl. China & India|570|28.9|757|21.8|1,744|27.0|4,471|29.4|
|Upper middle income, excl. China|193|9.8|269|7.8|624|9.7|1,597|10.5|
|Lower middle income,excl. India|112|5.7|110|3.2|243|3.8|689|4.5|
|**BY REGION**|||||||||
|World|1,973|100.0|3,473|100.0|6,456|100.0|15,228|100.0|
|Developing economies|597|30.2|837|24.1|2,035|31.5|6,268|41.2|
|Asia|165|8.3|459|13.2|1,285|19.9|4,005|26.3|
|Latin America & Caribbean|104|5.3|145|4.2|362|5.6|886|5.8|
|Europe, Middle East & North Africa|250|12.7|165|4.8|293|4.5|1,035|6.8|
|Sub-Saharan Africa|78|3.9|68|2.0|94|1.5|341|2.2|
|**Memo:**|||||||||
|BRICS 5 countries|72|3.7|203|5.8|482|7.5|2481|16.3|
|OPEC 12 members|280|14.2|174|5.0|311|4.8|1,077|7.1|

Source: Based on UN COMTRADE database.
surprisingly, child poverty rates are highest in low-income countries, where 41.6 percent of children are extremely poor. The percentage decreases to 22.2 percent for lower middle income countries and 3.9 percent for upper middle income countries.

**Table 7: Extreme child poverty by country income status (India and China shown separately)**

||**Extreme poor**<br>**children**<br>**(millions)**|**Children**<br>**headcount**<br>**poverty rate (%)**|**Share of**<br>**extremely poor**<br>**children (%)**|**Share of**<br>**sample child**<br>**population (%)**|
|---|---|---|---|---|
|Low income|122.6|41.6|37.3|17.5|
|Lower middleincome|186.3|22.2|56.7|49.7|
|_Of which: India_|_99.7_|_22.1_|_30.3_|_26.8_|
|Upper middleincome|19.5|3.9|5.9|29.7|
|_Of which: China_|_5.8_|_2.0_|_1.8_|_16.7_|
|High income|0.3|0.6|0.1|3.2|
|Total|328.7|19.5|100.0|100.0|

Source: GMD, UNDESA, WDI, PovcalNet Note: Totals reflect sample of 89 countries.

Table 8 shows similar figures by region. Sub-Saharan Africa (SSA) stands out as the region with the highest child poverty rate, at 48.7 percent. Over half, 51.7 percent of all extremely poor children in the sample, live in an SSA country, despite the region only containing 20 percent of the child population. Poverty rates in South Asia (SAR) are also high, as 19.7 percent of the children in the region are poor. SSA and SAR, combined, account for over 87 percent of the extremely poor children. While there are 170 million poor African children in the sample, this seriously understates the true number of poor children in Africa because the sample covers only 26 of the 48 countries, or 74 percent of the children, in the region. If the African child poverty rate were applied to all African countries, the estimated number of poor children in SSA would rise to 229 million.

**Table 8: Extreme child poverty by country region (India and China shown separately)**

||**Extreme poor**<br>**children**<br>**(millions)**|**Children**<br>**headcount**<br>**poverty rate**|**Share of**<br>**extremely poor**<br>**children(%)**|**Share of**<br>**sample child**<br>**population(%)**|
|---|---|---|---|---|
|EastAsiaPacific|25.6|5.6|7.8|27.2|
|_which includes China_|_5.8_|_2.0_|_1.8_|_16.7_|
|South Asia|117.2|19.5|35.7|35.7|
|_which includes India_|_99.7_|_22.1_|_30.3_|_26.8_|
|Sub-Saharan Africa|170.0|48.7|51.7|20.7|
|Latin America and Caribbean|14.3|8.1|4.4|10.5|
|Europe and Central Asia|1.3|1.4|0.4|5.6|
|Total|328.7|19.5|100.0|100.0|

Source: GMD, UNDESA, WDI, PovcalNet 11
probability of a household choosing migration state **_s_** could be estimated by a standard multi-nomial model. The inferences about the aggregate impact of work migration on poverty and inequality might also be obtained (see, for example, Adams 1991, 2005 and Taylor and Wyatt 1996).

However, some unobserved household and/or potential migrant characteristics could affect both the household’s decision to migrate and the household’s consumption.<sup>8</sup> For example, it might be optimal for a household to send a member with high entrepreneurial abilities abroad. These abilities, which are usually unobserved by a researcher, could also allow a migrant to earn higher wages in comparison with the average migrant worker and send more money back home. The challenge for our empirical strategy is to estimate the system of equations (2)–(4) controlling for such unobserved factors.

Being incorporated into the error terms in equations (2) and (4), the unobserved factors can be correlated. If error terms μ ’s and η ’s are not independent, the nonrandom selection of households into different states will result in a correlation between the explanatory variables _X_ and errors μ ’s in equation (4). In that case, the OLS estimates of consumption equation (4) are biased. To obtain unbiased and consistent parameter estimates under an assumption of joint dependence of the error terms, we use the method of full information maximum likelihood (FIML). The method estimates the household consumption equations jointly with the equation describing the household choice of migration state allowing for the correlation of the error terms across equations. The detailed discussion of our estimation methodology is shown in Appendix 1.

To estimate the impact of remittances and migration on poverty and inequality in Nepal, we simulate the counterfactual expenditure distributions under different migration scenarios. The FIML estimation of equations (2)–(4) identifies the parameters of fivevariate distribution of the error terms. The observed outcomes of the migration decision

> 8 Migrant selection was studied by Chiswick (1978), and Borjas (1987, 1990, 1991) developed a model of self-selection based on unobserved migrant characteristics. The problem of self-selection of migrants was also studied by Docquier and Rapoport (1998), Aydemir (2003) and Kanbur and Rapoport, (2005). Barham and Boucher (1998) build their model on the assumptions of potential endogeneity of household’s migration and labor force participation decisions. A recent study by McKenzie, Gibson, and Stillman (2006) using the survey of the winners of a migration lottery concludes that migrants are positively selected in terms of both observed and unobserved skills.

13
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

|Sanitation<br>|SSA|—|—|—|30|35|0.4|
|---|---|---|---|---|---|---|---|
|(% of households with access)|SAS|—|—|—|31|47|1.3|
||EAP|—|—|—|57|53|–0.4|
||ECA|—|—|—|86|80|–0.5|
||LAC|—|—|—|64|76|1.0|
||MNA|—|—|—|69|75|0.5|

Sources: As for table 2.1.

- Note: SSA = Sub-Saharan Africa, SAS = South Asia, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and Caribbean, MNA = Middle East and North Africa.

- a. Water and sanitation data are from 2002.

- b. Calculated over entire period available for each sector.

- = data not available.

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|Dehradun|No impact on revenues.|
|---|---|
|Surat|Yes, it impacts revenues but not to a large degree. As the discount value is only 1% of the<br>transaction value, the total loss would be to the tune of 5 to 6%.|
|Delhi|Officials do not know the amount foregone in benefits to females and, likewise, cannot tell if<br>this has impacted revenues.|
|Pipili|Yes, it has impacted revenues, but this is twofold; there’s a loss of 1% on every transaction<br>due to the discounts, but the number of registrations is increasing, which could be offsetting<br>this loss.|
|Gurugram|Benefits to females lead to 2% of foregone revenue in every such transaction, but overall, it<br>has not impacted on revenues. This is because more citizens are coming forward to utilize<br>these discounts, and the number of registrations has thus increased. Technically, revenues<br>have increased.|
|Pune|From total revenue collection, an estimated 18% of the amount is foregone in benefits to<br>females. It has therefore impacted revenues negatively.|

# 6. Main conclusions

Property ownership among females in India remains low, although some evidence indicates an increase in the proportion of female homeowners over the last decade (both single and joint ownership). Genderdisaggregated data collected during the study from government departments across various states suggest home ownership for females currently ranges between 20 percent and 30 percent of total homeowners.

## 6.1 Impact of incentives on property ownership

### 6.1.1 Stamp duty discounts may have a positive impact on female property ownership

**_Stamp duty and registration discounts positively impact female property ownership_** . The study found that 9 of the 12 cities surveyed had stamp duty discounts available to females registering a property in their names and that in Ahmedabad and Surat females do not pay any registration charges. Most interview participants and female and male FGD participants in these cities reported that stamp duty and registration fee discounts were a core factor behind the registration of properties in the females’ names, for both joint and single ownership. This trend is noted also by government officials (state revenue and land administration officials) in states providing stamp duty discounts to females. Such assessments are based on years of experience and personal judgment, and government officials in these states confidently

26
|Livestock<br>Non-Housing<br>Wealth|(3)<br>(4)|(-2.77∗∗∗)<br>(-0.37∗∗∗)|-(0.50)∗∗∗<br>-(0.17)∗∗∗|(-0.32∗∗∗)<br>(-0.07∗∗∗)|-(0.87)∗∗∗<br>-(0.26)∗∗∗<br><br>(-2.61∗∗∗)<br>(-0.70∗∗∗)|-(0.80)∗∗∗<br>-(0.21)∗∗∗<br><br>(-0.16∗∗∗)<br>(-0.92∗∗∗)|-(0.08)∗∗∗<br>-(0.03)∗∗∗<br><br>-13156∗∗∗<br>-8230∗∗∗<br>-0.31∗∗∗<br>-0.34∗∗∗<br>usehold/Household-head controls include gender, age,<br>ance levels: ∗∗∗p<0.01, ∗∗p<0.05, ∗p<0.1.|
|---|---|---|---|---|---|---|---|
|Gold &<br>Silver|(2)|)<br>(-0.45∗∗∗)|∗<br>-(0.22)∗∗∗|)<br>(-1.97∗∗∗)|∗<br>-(0.89)∗∗∗<br>)<br>(-0.04∗∗∗)|∗<br>-(0.67)∗∗∗<br>)<br>(-0.90∗∗∗)|∗<br>-(0.07)∗∗∗<br>∗<br>-13156∗∗∗<br>-0.18∗∗∗<br>in parentheses. Ho<br>f dwelling. Signifc|
|GSL|(1)|(-2.27∗∗∗|-(0.48)∗∗|(-0.09∗∗∗|-(0.83)∗∗<br>(-1.09∗∗∗|-(0.79)∗∗<br>(-0.47∗∗∗|-(0.08)∗∗<br>-13156∗∗<br>-0.22∗∗∗<br>dard errors are<br>old, and type o|
|Variables||Variance of Income (log)||Confict×Variance of Income (log)|Confict|Income (log)|Observations<br>R-squared<br>The table shows the estimated coefcients of equation (2). Robust stan<br>marital status, education, household size, number of males per househ|

26