rafmacalaba/datause-encoder-data huggingface dataset:
to the use of overall adult mortality rates.<sup>6</sup> Thus, the dependent variable we use is (log of) ratio or women’s to men’s adult mortality rate (henceforth, women-to-men adult mortality rate). The data source for the variable is the WDI. Using the ratio of women–to-men adult mortality rate has the advantage that it eliminates economy-wide or macroeconomic factors common to both men and women’s mortality from spuriously affecting our results. The mean value of the dependent variable equals -0.37 and the standard deviation is 0.28. Across country-year, the variable ranges between 0.07 (Bangladesh, 1990) and -1.10 (Belarus, year 2008).

While the women-to-men adult mortality rate has some advantages, it does not tell us whether women’s mortality rate in absolute terms is increasing or decreasing with domestic violence legislation. In other words, it does not tell us if an improvement in the women-to-men adult mortality rate is due to an improvement in women’s adult mortality rate or due to worsening of men’s adult mortality rate. Hence, as a robustness check, we do report results using the absolute women’s adult mortality rate as the dependent variable. This equals the (log of) the adult mortality rate for women taken from WDI. The mean value of the variable equals 5.2 and the standard deviation equals 0.62.

Our main explanatory variable is a dummy variable equal to 1 if the country has domestic violence legislation and 0 otherwise. The data source for the variable is the Women, Business, and Law database, where the UN definition of domestic violence is followed. Within this definition, domestic violence encompasses physical violence, emotional or psychological violence, sexual violence, or financial or economic violence (see World Bank 2015 for details). The mean value of the dummy variable equals 0.31, implying that on average in any given year about 31 percent of

> 6 In a number of instances the Women, Business, and the Law database shows the far‐reaching consequences of restrictions against women embedded in countries’ laws. For instance, it shows that the larger the number of discriminations against women found in the law, the lower the secondary school enrolment rates of girls relative to boys and the lower the female labor force participation rates.

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<u>Table 6. Similarities and differences in confounding factors in four National Park Systems</u>

||Kafue|Lower<br>Zambezi|Bangweulu|Luangwa|
|---|---|---|---|---|
|_Variables with negative effects_|||||
|Percent of female headed household|22%|24%|36%|27%|
|Education in years|7.5|7.4|5.5|6.4|
|Value of livestock in ZMK|1,156,841|449,612|76,987|187,715|
|Distance to all-weather road in km|4.8|3.1|6.0|10.5|
|_Variables with positive effects_|||||
|Migration in 5 Years|11.4|10.3|5.5|1.7|
|Income from Nature-based Activities|115,253|412,158|695,262|860,078|
|Fees paid to Community Based Organizations|1,376|1,905|697|766|
|Income from Community Based Organizations|1,722|1,951|2,683|4,009|
|Involvementinthe CRB/VAGrelative toLuangwa|41%|43%|57%|100%|

Source: Authors’ calculations, Data from the IGMAW survey, 2006 The significance of these factors is that they are different in Bangweulu and Luangwa as compared with Kafue and Lower Zambezi. They do not directly explain why Bangweulu and Luangwa show the GMA and participation effect on welfare while Kafue and Lower Zambezi do not. The impacts are calculated after taking into account these and other confounding characteristics of the communities and households in the four areas.

# **4. Conclusions**

Game management areas in Zambia were conceived as controlled hunting grounds where people coexist with nature. In recent years ZAWA has started sharing some of the revenue and responsibilities with the local communities. The result of this devolution of responsibilities and resources has been uneven with respect to wildlife management (Simwanza, 2007). This study focuses on the human aspect of the impact of the devolution of rights and responsibilities in the GMA. To be precise, we look at the impact of GMA on household welfare of those living in it. For those households in GMAs, we also measure the impact of participating in two community institutions, CRB/VAGs, which allow households to actively take part in natural resource management and decide on how the revenue from it is spent within the community.

We find substantial gains associated with living in GMAs and participating in CRB/VAGs. Since GMAs are located in relatively remote areas on land unsuitable for intensive agriculture, comparison of average consumption welfare between households in GMAs and non-GMA areas does not reflect welfare differences attributable to the GMA institutions alone. We find significant welfare gains in some GMAs after controlling for household and community characteristics. However, these gains are unevenly distributed across various park systems around which GMAs are clustered. In particular, households living in GMAs near Bangweulu and Luangwa park systems appear to gain substantially from the GMA institution while those in GMAs near the other two park systems do not.

We find that not all households in GMAs gain equally. Those households who actively participate through CRB/VAGs gain substantially more than those who do not. The gains from 13
on household production, this type of model is inconsistent with the literature. The Apps and Rees (1997) conclusion is that collecting data on individual time use and consumptions within households is likely to be a much more reliable way to obtain estimates of household sharing rules.

We also show that unless household production is an integral part of the model, one obtains a wholly distorted picture of the nature of household economic activity as well as of the intra-household welfare distribution.<sup>26</sup> When time is assumed to be divided simply between market work and leisure, a mother with a low money income due to a low market labor supply must be enjoying a substantial amount of leisure as well as receiving a generous transfer of market goods from her partner. Thus, we would argue, the extension to two person households is _necessarily_ accompanied by the explicit modeling of household production. The Apps-Rees formulation allows her consumption to be financed from an implicit payment made in exchange for the output of her domestic work. And the model allows for the possibility that a woman specializing in work and care activities at home may, in effect, be financing a transfer to her partner, by working longer hours. Since time use data, for both developed and developing economies, indicate that, on average, women work longer than men, this possibility is also highly probable. The issue is discussed further in Section 5 in the light of evidence from time use surveys for South Africa and Nicaragua.

A further fundamental implication of recognizing production for exchange within the family is that in an economy with an outside labor market, a partner specializing in household production can switch to market work in response to a fall in the implicit wage for domestic work.<sup>27</sup> She does not have to exit the household or choose an allocation at a non-cooperative Nash equilibrium, which are among the limited options of a model that omits household production for exchange. The approach therefore allows the analysis of the intra-household effects of policies that limit her outside opportunities, while she remains within the household.

> 26 See Apps and Rees (1996) for an analysis based on time use data.

> 27 And, in response to a fall in the outside net wage due to government policy, she may switch from market to domestic work.

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# **Figure 5.  Loss Exceedance Curves of Portfolio of Selected Public Buildings, before and after Structural Investments**

<!-- Start of picture text --> 20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4 Unretrofitted Retrofitted<br>2<br>0<br>0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000<br>Return period (years)<br>Exceedance probability (percent)<br><!-- End of picture text -->

# **_Probabilistic Cost-benefit Analysis of the Risk Mitigation Project_**

Approximately US$160 million has been earmarked for the structural and functional strengthening of 134 schools (including kindergartens), 32 hospitals, and 2 fire stations. Risk mitigation measures under this project are planned to be implemented within a fouryear period. The time horizon of this project is 30 years, which is less than the average lifetime of a standard building.

The average annual return on mitigation investment is defined by where _i_ is the type of mitigation investment (structural, structural and functional), and _Lw_ _~~i~~_ / _o_<sup>(</sup> _Lw_ _~~i~~_ ) is the annual average earthquake loss before (after) mitigation investment and _I i_ is the investment amount.

Simulations from the probabilistic earthquake risk assessment model show that structural investments generate an average annual return equal to 14.5 percent for retrofitted schools and 19.1 percent for retrofitted hospitals and fire stations (see Table 1). This means that a US$100 investment will reduce the average annual property loss caused by an earthquake by US$14.5 for schools and by US$19.1 for hospitals and fire stations.

Structural and functional investments not only provide for reduction in property losses, but may also save lives and reduce the number of injuries.<sup>3</sup> In the case of an earthquake

> 3 Most conventional cost-benefit models assign a monetary value to human life, which commands a much greater monetary amount than property.  Based on recent studies, the value of a statistical life in Colombia

11
to a bifurcation where the children born into parents with higher education and nonfarm occupation enjoy a clear and cumulative advantage in education, while the children of uneducated farmers are trapped in low educational attainment.

A broader conception of parental economic status that combines both education and occupation is equally desirable from a “measurement perspective”, because it provides a more complete measure of permanent income of parents.<sup>6</sup> An important lesson from the rich literature on intergenerational income persistence in developed countries which partly motivates our analysis is that income data available from cross-section household surveys or short panels yield estimates which are severely biased downward (see Solon (1992), Mazumder (2005), Atkinson et al. (1983); Solon (1992); Zimmerman 1992)). It is extremely difficult, in fact nearly impossible, to find long-term panel data on parents’ income in developing countries.<sup>7</sup> A natural alternative, surprisingly ignored in the existing economics literature, is to combine two most salient indicators of parents’ socio-economic status routinely available in standard household surveys: education and occupation.<sup>8</sup> We develop a research design in terms of binary indicators of parents’ education and occupation: higher education ( _Ei_<sup>_p_= 1,ifatleastoneparentofchild</sup><sup>_i_haseducationmore</sup> than a threshold such as primary or middle), and non-farm occupation ( _Oi_<sup>_p_= 1</sup><sup>_,_ifatleast</sup> one parent’s primary occupation is non-farm).<sup>9</sup> We thus split the sample into four mutu6Parents’ education and occupation are also among the most salient ‘circumstances’ in the related but distinct literature on ‘inequality of opportunity’. For recent contributions in that literature see, for example, Brunori, Ferreira, and Peragine (2013), and Asadullah and Gaston (2012).

> 7Income data are especially prone to measurement error in villages in developing countries due to large informal and non-market economy (Deaton (1997)).

> 8Many household surveys in developing countries contain income information only for a single year (the survey year). As shown by Mazumder (2005) in the context of USA, income data for a period of a decade and half may be required to tackle the measurement error. Since measurement error is likely to be a much more severe problem in income data from developing countries, one probably needs data over a longer period of time.

> 9A reader might wonder whether it would be best to include all of the available indicators of parental socio-economic status and create an index using statistical techniques such as principal components. It is, however, difficult to interpret the estimates based on a principal component index, because ranking

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> in the number of hours worked, the time of day/night worked, or the likelihood of sleeping in the street (Araujo, 1998).

# **The reasons children work**

26. Children work for a variety of reasons though it is commonly believed that one of the main reasons is poverty.<sup>8</sup> Poor families are forced to send their children to work to contribute to family income (see Table 8). Many families do not see themselves as having an altemnative, as children's earnings may be necessary for family survival. The constraint to work is placed on children when the opportunity cost of receiving an education (not working) is extremely high. Other factors that drive child labor include organization of work activities, social norms that encourage children to work, inability to pay school fees supplies or uniforms, inability to access schools due to distance/transportation difficulties and poor quality of school and teachers.<sup>9</sup>

**Table 8: Incidence of Child Labor, by Income Quintile, Area and Gender - (percentage of all children between the ages of 10-14)**

|~$iome<br>F-ov'<br>Ok|~~4<br>Bo~|idT|oa|Bb,|L4|_M~F_|o<br> _WM_|l-T<br>ir<br>:|iU<br>---<br>To|
|---|---|---|---|---|---|---|---|---|---|
|**JSt**quintile|14|6|10|52|24|38|31|14|22|
|2ndquintile|14|6|10|40|22|31|19|9|14|
|3rdquintile|12|6|9|40|21|31|15|8|12|
|4thquintile|9|5|7|36|21|29|11|6|8|
|**5th**quintile|6|4|5|28|18|23|7|5|6|

> Source: Calculations based on PNAD, 1996

> 27. A parent's decision to put a child to work is also influenced by the organization of a family's

> productive activities, induding the nature of contracting and payment systems. Child labor becomes part of the famnily labor, which is particularly true in rural areas. Another critical factor is the social

> norms that uphold child labor, which is viewed as an integral and positive part of a child's development. A working child is perceived as receiving discipline and training for his or her future

> 8 See for example Graiter and Lerer (1998), Grootaert and Patrinos (1999), Jensen and Nielsen (1997), Psacharopoulos (1997) and Tzannotos (1998).
# The Speed of Justice<sup>_∗_</sup>

Florence Kondylis<sup>_†_</sup> Mattea Stein<sup>_‡_</sup> _Updated_ December 10, 2020

**Keywords** : Economic development, Firms, Judicial efficiency **JEL Classification** : K41, D73, O12

> _∗_ We benefited from generous suggestions from a large number of colleagues, especially Pierre Bachas, Matthieu Chemin, Daniel Chen, Decio Coviello, Jishnu Das, Simeon Djankov, Esther Duflo, Pascaline Dupas, Matthew Jackson, Amit Khandelwal, Sylvie Lambert, Arianna Legovini, John Loeser, Karen Macours, Ben Marx, Simon Quinn, Andrei Shleifer, and Christopher Woodruff. We are grateful to the Ministry of Justice of Senegal for their leadership in this work. We are indebted to Presidents Ly Ndiaye and Lamotte of the Court of Dakar and their staff for making all court files and archives available to us. We benefited from the advice of eminent magistrates throughout the study period, especially Mandiogou Ndiaye, Souleymane Teliko, and Klaus Decker. The tax administration data would not be available to us without support from the WWID team at PSE, in particular Bassirou Sarr and Leo Czajka, and the leadership of Bassirou Niasse and Mor Fall at the DGID. The authors acknowledge financial support from the EHESS Paris, KCP, RSB, the Senegal office of the World Bank, and the i2i fund. None of this work would not have been possible without support from the DIME team. We thank Chlo ̈e Fernandez, Felicit ́e Gomis, Pape Lo, Molly OfferWestort, and Violaine Pierre for superb field work management. Edina Mwangi, Romaric Sodjahin, Sakina Shibuya and Cyprien Batut provided excellent research assistance. All usual disclaimers apply, particularly that the views expressed in this paper do not engage the views of the World Bank and its members.

> _†_ Development Impact Evaluation, World Bank

> _‡_ University of Naples Federico II and Center for Studies in Economics and Finance (CSEF)
-7a-

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Gini index, circa 10 for the Mean Log Deviation and the Atkinson index, and 29 for the Theil index. Poverty indices are found to be less sensitive. While these findings are, of course, specific to the surveys that were included in the exercise, they give empirical support to more general considerations, with important implications for the practice of applied welfare measurement, in particular for welfare comparisons: a common framework for detecting and treating extreme values is essential for both international comparisons and within-country time trends.

The paper is organized as follows: section 2 illustrates the method for outlier detection that is used in the rest of the paper; section 3 presents the command `outdetect` ; section 4 shows international evidence on the sensitivity of poverty and inequality estimates to extreme values of welfare indicators; section 5 offers some concluding remarks.

# **2 Outlier detection**

In this section the variable of interest – the target for outlier detection – will be denoted by x, and its probability density function by f(x). For expositional simplicity, we shall refer to x as ‘consumption’, but nothing prevents one from thinking of x as standing for price, unit value, quantity, wage, income, any expenditure component, or any other continuous variable whose extreme values are seen as potentially problematic.

How exactly should one identify outliers in the distribution of x? When does a high or low value of consumption qualify as ‘extreme’, that is, ‘too far away’ from the bulk of the distribution, so much as to arise suspicion as to whether it is genuine?

Out of the many criteria proposed in the literature, `outdetect` uses one that is based on the construction of an _outlier region_ with reference to f(x) (Davies and Gather, 1993; Gather and Becker, 1997). If the distribution of interest is known – for example, if f(x) is Normal – then one can consider an observation to be an outlier if it falls into a range of values that occur with arbitrarily low probability. An observation xi (i= 1, ... , n) falling into a range defined in this way could conceivably be produced by the theoretical distribution f(x), but that would be a rare occurrence, making xi an extreme value, or an outlier. A conventional application of this criterion identifies the bounds of the outlier region for a Normal distribution as the mean, μ, plus or minus three times the standard deviation, σ (each tail region defined in this way has a probability of about 1%).

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services. As discussed, one key aspect of preserving those resources is to limit access to common-pool resources in a way that precludes a tragedy of the commons. In principle, one way to do this is to charge an appropriate price for the use of the resource to “internalize the externality,” but as discussed earlier, examples of this in developing countries are rare in practice and experimental in nature. The authors of the India case study mention this possibility but conclude that it would be impractical given current institutional shortcomings.

Another approach to managing natural resources is to grant some form of ownership over a common-property resource – thereby creating an incentive for the owner to conserve it. In the traditional systems identified by the case studies, group ownership is implied and group leaders are obligated to ensure that resources are used in a sustainable way. Examples from the studies include the role of traditional authorities in Madagascar and India. Still, these informal arrangements can be fragile. In particular, evidence from Madagascar suggests that some types of internal migration can result in reduced social cohesion that undermines traditional institutions.

A more common approach is to create formal institutions that provide title or other use rights. Conceptually, such rights could be granted to groups rather than individuals; this was the idea behind the Gelose program in Madagascar. More commonly, resource management approaches identified in the case studies involved private use rights, usually in connection with restrictions on how the resources can be used. In the case of some natural resources, private ownership can go a long way to solve the problem of overuse. An example is the contract-farming case study from Madagascar, which illustrates how private ownership creates incentives for farmers to adopt practices that preserve soil fertility. More generally, positive or negative externalities are sometimes associated with the private use of natural resources, which has motivated restrictions on their use. Often, regulations are intended to stem specific pollution problems. For example, the study from South Africa notes that sugar mills are required to cool water used in processing before returning it to rivers in order to stem thermal pollution in natural habitats; in India, the Supreme Court intervened to limit particular technical approaches to shrimp farming because of adverse environmental consequences. In Ca Mau, the government stepped in to ban the use of certain types of antibiotics in shrimp production.

30
allowance is also included for remittances to the family back home that would not be incurred without migration; we refer to this as the “remittance levy.” Thus, the number of buyers is: = ∑ M MU w M U MM ] (5) mm bb mm=1 rr ww mm MM mm <mark>A potential migrant is assumed to base the decision on his net income in the U.S. In (5),</mark> NN 1[(1 −ττ )(1 −ττ )ww� > pp(1 + ττ ) + CC + CC + ww <mark>τ</mark> ≥0 is the remittance levy, ≥0 is the tax on a migrant’s earnings in the U.S., w ≥0 is the tax a rr migrant pays on a purchase of the WP (Section 2), ww M is the out-of-pocket cost of moving to ττ ττ the US, that includes travel expenses to the U.S. and back and visa fees, MM U is the cost-of-living CC adjustment for the US, and MM is the migrant’s wage rate in Mexico. CC The market-clearing price of the work permit ( ) minimizes the difference between the ww numbers of sellers and buyers: pp<sup>∗</sup> = argmin | (6) ss bb <mark>We apply standard U.S. tax rates for the expected wages of a migrant (as given in the</mark> pp<sup>∗</sup> (ww) |NN −NN

<mark>We apply standard U.S. tax rates for the expected wages of a migrant (as given in the Appendix). T</mark> here is more uncertainty about the remittance levy. Yang (2011) reports that Mexican migrants in the U.S. remit, on average, 31% of their U.S. earnings. Undoubtedly, some of this would have been incurred even if the migrant had not migrated, so these should not be counted as costs of migration. We will allow alternative values of over a range up to 30% of post-tax earnings in the U.S. <mark>A reasonable assumption for th</mark> e out-of-pocket cost of moving (and rr ττ returning) is $4,000, covering <mark>costs of obtaining a visa</mark> as well as travel and relocation costs.<sup>10</sup> <u>Clemens et al. (2019) provide a useful clue to the plausible inner range of values for</u> migration costs by comparing the wages in the mainland U.S. of migrants from Puerto Rico and Guam with their wages back home. Since there are no governmental restrictions on migration, the implied wage differential (in equilibrium) reflects migration costs. <u>Clemens et al. (2019)</u> obtain predicted wage differentials for Puerto Rico and Guam in the region 1.3-1.5.

> 10 <mark>We took an approximate amount of $1,700 for processing of H1B visa.</mark>

8