rafmacalaba/datause-encoder-data huggingface dataset:
## Figure 3: Legal protection in the workplace

<!-- Start of picture text --> Protection against discrimination<br>on ethnic grounds<br>Protection against discrimination<br>on religious grounds<br>Protection against discrimination<br>on sexual orientation<br>Protection against discrimination<br>on gender identity grounds<br>Protection for equal<br>remuneration regardless of ethnicity<br>Protection for equal<br>remuneration regardless of religion<br>Protection for equal remuneration<br>regardless of sexual orientation<br>Protection for equal remuneration<br>regardless of gender identity<br>Bulgaria Mexico Morocco Netherlands Tanzania Vietnam<br><!-- End of picture text --> _Source_ : World Bank Equality Project.

# _4. Access to Property_

Access to property is the cornerstone of economic security. Land ownership is important not only as a means of capital accumulation but also as a source of income. Moreover, property reinforces bargaining power and agency. In addition, the ability to leverage property as collateral is important for obtaining credit and starting a business.

The access to property indicator seeks to capture the minority gap in the ability to own, manage, control, and inherit real property and to register and transfer a private business. The initial findings show that, although many property-related laws are apparently minority neutral, hidden regulations and gaps in the law are at the root of many forms of indirect discrimination regarding access to land and property rights.

In Mexico, “inequality has a particular effect on the most vulnerable sectors of the population, such as indigenous peoples—among whom the poverty rate is four times higher” (InterAmerican Commission on Human Rights 2015, 42). Article 27 of the Mexican constitution recognizes indigenous peoples’ lands, protects their property, and grants them preferential rights over their traditionally occupied territories. Still, the struggle remains in the way the property law translates into inclusion and economic empowerment. Mexico has not implemented a law on free, prior, and informed consultation of indigenous peoples regarding development projects Around one-quarter of the world’s urban population continues to live in slums and informal settlements. An increasing number of urban dwellers, especially the most poor and vulnerable groups, are living in precarious conditions, addressing their housing needs informally, lacking access to basic services and living space, isolated from livelihood opportunities and vulnerable to forced evictions or homelessness. ... Efforts to improve access to adequate housing for women, migrants, refugees, people with disabilities, indigenous and minorities have made little progress so far. (UN Habitat 2015) that are implemented in their territories or that affect them in a direct manner. That situation has a negative effect on their use of their land and property by causing instability. The Inter-American Commission on Human Rights acknowlAnti-Discrimination Law and Shared Prosperity 22
Since the net effect on savings is negative—due to the presence of altruistic transfers and endogenous fertility—the productivity externality on the wage rate diminishes, and consequently productivity growth will decrease (Figure 8). The fall in second period income, due to lower net returns to the declining capital stock, will render altruistic gifts relatively more important as a means of financing old age consumption. As a result, the gift rate in Figure 9 increases.

**Figure 8. Adult Survival & Productivity Figure 9. Adult Survival & Transfers**

<!-- Start of picture text --> g z<br>μ2 μ2<br>Source: Author Source: Author<br><!-- End of picture text --> Increases in, for instance, government health spending or general exogenous improvements in living conditions are likely to improve health, but it is not clear that such developments only cause higher survival rates for children and not for adults—or the reverse. It is more reasonable to assume that both survival rates increase when health investments increase. Having identified, and analyzed in isolation, the key dynamics of changes in child and adult survival rates on economic variables, we move on to study a composite change where both child survival and adult survival rates increase simultaneously.

# **3.4  Child and adult health**

The simultaneous and equi-proportional increase in child and adult survival will produce a combination of the effects analyzed above. An increase in _μ1_ ( _μ2_ ) led to a higher (lower) savings rate— thus the composite shock entails perfectly offsetting effects on the savings rate (Figure 10). This is because the ratio of the implicit prices on first and second period consumption remains unchanged.

We have identified three effects on savings following an increase in child survival, _μ1_ . Since the effect from intertemporal prices is absent for a composite increase in survival rates, the positive effect on savings is due to the effect from a lower price of children being larger than the effect from a higher marginal utility of first period consumption. The net effect of _μ2_ on savings, on the other hand, was negative and dominated by the fact that children could be used as an old age savings mechanism. Savings also tended to rise because an increase in _μ2_ would increase the marginal utility of second period consumption. The net increase in savings for a composite increase in survival rates will, for those reasons, generate similar effects on fertility, productivity growth and altruistic gifts as studied in 14
Policy Research Working Paper 10704

# **Abstract**

The World Bank’s analytical work has a strong reputation, but its knowledge products are also perceived to be of varying quality and relevance, and the drivers of this heterogeneity are only partially understood. Building on previous evaluations, this paper adopts a production function approach to assess how budget resources, time to completion, technical skills, and institutional responsibilities affect the internal ratings and external visibility of different types of analytical tasks at the World Bank. To this effect, the paper first matches records from three unconnected electronic platforms—for internal documents, budget codes, and external publications—to assemble a comprehensive database of knowledge products and their key characteristics. With analytical documents as its unit of observation, the exercise shows that: (1) devoting more resources to analytical tasks leads to both better ratings and greater visibility; (2) both outcomes are systematically worse when a greater share of resources comes from trust funds; (3) they are also consistently worse for tasks that take longer to complete; (4) more academically oriented team leaders underperform on ratings and overperform on visibility, whereas technically solid but less stellar team leaders overperform on ratings; and (5) everything else equal, performance varies systematically with the nature of the unit in charge. The findings of the paper can be read as a cautionary note against knowledge management that is based on the counting of analytical tasks. Instead, the findings call for much stronger information systems on knowledge products, a better alignment of incentives for the units in charge, and regular evaluations in the spirit of this paper.

This paper is a product of the Operations Policies and Country Services 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://www.worldbank.org/prwp. The authors may be contacted at mrama@worldbank.org.

_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
that can be applied to a wide range of countries and GDP per capita. For our purposes, the magnitude of the proportion of laborers in automation-prone employment is less important than identifying which countries have the highest proportions. Different results than ours are found by Ahmed and Chen (2017) in Hallward-Driemeier and Nayyar (2018:135) who apply the Frey and Osborne (2013) probabilities to STEP countries following Arntz, Gregory and Zierahn (2016). In this study, they find that the high-income countries in PIAAC have a higher proportion of workers at high risk of automation than the middle-income countries in STEP. Similar to our results, however, lower middle-income countries have a lower proportion of workers at high risk. Even if high-income countries have a higher proportion of workers in automation-prone employment, our main findings to do not change. First, high income countries lack a viable informal sector which mitigates potential negative effects of automaton. Second, what matters in our model is the quality of education systems, and as discussed in the next section, middle income education outcomes tend to be worse than those in high income countries.

# _Learning outcomes of education systems_

Our model and simulation as well as our analysis of the IPUMS-I data suggest that automation can be beneficial to middle income countries, but if their education systems are not sufficiently high quality, then automation will be the most detrimental for them. Research on learning outcomes for low and middle-income countries finds, in general, quite poor learning outcomes. For example, Graham and Kelly (2018) find very low proportions of primary school students can read a simple sentence with ease and comprehension. Banerji et al. (2010) suggest that most students in middle income countries are functionally illiterate. The OECD PISA data provide an instructive comparison. It categorizes reading achievement into proficiency levels. In their assessment, minimum reading proficiency is defined at Level 2 in which students can infer information from a text, recognize the main idea of a text, and make comparisons. Students at the advanced level of reading proficiency, Level 5 or higher, can retrieve deeply imbedded information from and critically evaluate a text (OECD 2016b:162). Figure 6 presents the proportion of 15-year-old students attaining minimum proficiency and advanced proficiency in PISA, for the 67 participants that have GDP per capita data available. As shown, the proportion of students attaining minimum proficiency ranges between 20 to 60 percent with the exception of 35
To what extent can the data presented in Table 1 be taken at face value? A flavor of the controversies surrounding the “stylized facts” depicted in the table is provided in Figure 3, which depicts a wide range of alternative inequality estimates for one country, Russia, drawn from different well-documented sources. The figure shows that, for the most recent period, Russia could be classified anything from a moderately high to a high inequality country or as anything from a country exhibiting rising to falling inequality, depending on the which source of data is chosen.

**Figure 3.  Russia: Evolution of Gini index from various sources, 1992-2004**

<!-- Start of picture text --> 0.5<br>Officially published,<br>nominal per capita<br>0.45 incomes<br>0.4<br>Per capita<br>expenditures in<br>RLMS, direct data est.<br>0.35<br>0.3 HBS/NOBUS* per<br>capita consumption<br>corrected for regional<br>price differences<br>0.25<br>1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> Sources: Goskomstat, Poverty Assessment (World Bank ); RLMS- Russian Longitudinal Monitoring survey; HBS- Household Budget Survey; NOBUS – National Survey of Social Programs and Participation.

The example clearly illustrates the point that published data on income distribution should be treated with great care and this, following Atkinson and Micklewright (1992), for at least six different reasons: First, published data from different countries rely on different imputation and adjustment procedures. In Ukraine, for example, significant and rather unusual imputations are undertaken with reported in-kind components. In some countries total incomes include imputed rents, whereas in others they do not: which option is chosen can have large effects. In Russia again, inclusion of owner occupied rents in 1993 reduced the Gini index from 0.42 to 0.35 (Buckley and Gurenko [1998]). Thus different rows in Table 1 cannot be compared with each other, and a higher country Gini does not necessarily translate into higher inequality for a comparable concept of welfare.

Second, in all the EU-8 countries, wages account for over 60 percent of household incomes. In contrast, among the low income countries of the CIS, wages represent less than 15 percent in some cases. At the same time, while public transfers are a much more important component of income in the EU-8, where they comprise 25 to 30 percent of total incomes; their importance has shrunk dramatically in the low income CIS countries to the point where 7
- 4 -

discusses the main estimation results and some policy implications. Then, Section VI concludes.

# **II. SPATIAL PRODUCTION ALLOCATION MODEL (SPAM) UPDATE**

The paper relies on the spatial production allocation model (SPAM) developed by the International Food Policy Research Institute (IFPRI) for generating geographically highly disaggregated crop production data. The SPAM is a spatial model to allocate crop production derived from large statistics reporting units, such as country, province and district, to a raster grid at a spatial resolution of 5 minutes of arc (approximately 9 km at the equator (normally referred to as 10km x10km pixel for simplicity). To infer likely production locations, the model uses a cross-entropy method (Shannon 1948). Given initial allocations, the crossentropy method minimizes the cross-entropy distance—entropy is referred to as a measurement of uncertainty of expected information—between different probability distributions of the variables in the analysis, under different spatial constraints. As a result, the SPAM allows the simulation of the most plausible agricultural production locations given all available data at different levels.

Various input data are taken into account in the cross-entropy procedure, such as sub-national crop production statistics, satellite data on land cover, maps of irrigated areas, biophysical crop suitability assessments, population density, secondary data on irrigation and rain-fed production systems, cropping intensity, and crop prices. Specifically in the SPAM, we start with crop production statistics at the large administrative (geopolitical) units ( **Figure 1** ).<sup>1</sup> These are typically national or sub-national. Key information to determine where and how much agricultural land exists at the pixel level comes from the existing land cover imagery, which is divided into crop land and non-crop land. With this crop land combined with the crop suitability data based on local climate, terrain and soil conditions, the crop-specific land

> 1 See You and Wood (2006), You _et al._ (2009), and You et al. (2014) for further details.
5 <!-- Start of picture text --> DLagram I<br>INSTITUTIONAL ANALYSIS<br>ANALYTICAL FRM_EWOKS<br><!-- End of picture text --> <!-- Start of picture text --> SUSTAINABILITY<br>WITEIN THE BROADER<br>ENVIRONMENT<br>TRANSACTIONS WITH<br>OTHER ORGANIZATIONS!<br>GROUPS<br>4 -COMPETITION<br>-COLLABORATION<br>SWSTAINABILITY -COORDINATION<br>ANALYSIS<br>LEGAL LEGAL<br>FRAME-NRI1 FRAMEWORK<br>-GOVERNING THE -GOVERNING THE<br>ORGANIZATION ORGANIZATION<br>-OTHER RELEVANT -OTHER RELEVANT<br>INSTITUTIONS INSTITUTIONS<br>RESOURCES RESOURCES<br>-LEADERSHIP -LEADERSHIP<br>-MAUNPOWER -MANPOWER<br>-FUNDS -FUNSl<br>-TECHNOLOGY -TECHNOLOGY<br>3 -INCENTIVES -INCENTIVES<br>-CAPACITY TO RELATE -CAPACITY TO RELATE<br>STRUCTURAL RESOURCES TO RESOURCES TO<br>ANALYSIS OBJECTIVES OBJECTIVES<br>ORGANIZATION ORGANIZATION ORGANIZATION<br>STRUCTURE STRUCTURE STRUCTURE<br>-DIFFERENTIATION -DIFFERENTIATION -DIFFERENTIATION<br>& INTEGRATION & INTEGRATION E INTEGRATION<br>OF FUNCTIONS OF FUNCTIONS OF FUNCTIONS<br>-SPECIFICATION -SPECIFICATION -SPECIFICATION<br>2 OF AUTHORITY OF AUTHORITY OF AUTEORITY<br>& RESPONSIBILITY E RESPONSIBILITY E RESPONSIBILITY<br>ANALYSIS OF<br>FUNC". IONS/SYSTEMS<br>FUNCTIONS FUNCTIONS FUNCTIONS FUNCTIONS<br>-PERSONNEL -PERSONNEL -PERSONNEL -PERSONNEL<br>-FINANCE -FINANCE -FINANCE -FINANCE<br>-MARKETING, etc. -MARKETING, etc. -MARKETING, etc. -MARKETING, etc.<br>SYSTEMS SYSTEMS SYSTEMS SYSTEMS<br>-RECRUITMENT -RECRUITMENT -RECRUITMENT -RECRUITMERT<br>I -EVALUATION -EVALUATION -EVALUATION -EVALUATION<br>-BUDGETING -BUDGETING -BUDGETING -BUDGETING<br>SKILL GAP<br>ANALYSIS PROCEDURES PROCEDURES PROCEDURES PROCEDURES<br>TECHNICAL TECHNICAL TECHNICAL TECHNICAL TECHNICAL<br>AND AND AND AND AND<br>MANAGERIAL MANAGERIAL MANAGERIAL MANAGERIAL MANAGERIAL<br>MANPOWER MANPOWER MANPOWER MANPOWER MANPOWER<br>GAPS GAPS GAPS GAPS GAPS<br><!-- End of picture text -->
deviation increase for mentoring and 0.104 standard deviation increase for training alone. These impacts are very similar in magnitude, and we cannot reject equality (p=0.901).

# **5.2 Impacts on Secondary Outcomes at the Individual Level**

To examine the extent to which these improvements in profits and sales translate into broader individual wellbeing, our pre-analysis plan specified four domains of secondary outcomes to examine. The impacts on these outcomes are shown in Table 3.

The first set of secondary outcomes concerns employment, whether in self-employment or wage work. Column 1 of Table 3 shows that 88.5 percent of the pure control group are engaged in some form of employment for pay at the time of the three-year follow-up surveys, and the increase in firm survival also translates into an increase in any employment. Column 2 looks at all income from work, which combines profits from self-employment with any earnings from wage labor. We see the higher profits are not being offset by lower wage earnings, as total income from work increases by 389 KSH per week, or 18.1% of the control mean.

The training emphasized a number of topics and approaches that had the goal of empowering women in terms of decision-making around finances and business. We measure 10 different outcomes in this domain (e.g. are they compelled to spend money on their husband or family, do they need someone’s permission to travel to sell a business asset, do they have money they have sole control over, etc.). The average individual in the control group is able to do 7 out of 10 of these decisions, and Column 3 shows that training is not found to have any sizeable or significant impact on this measure of empowerment at either time horizon. This is consistent with the qualitative assessment, which noted that training did not appear to change individual or household decision-making dynamics (ICRW, 2015).

The third domain we examine is subjective well-being and mental health. We measure subjective well-being today and anticipated subjective standard of living in 5 years’ time on a Cantril ladder, and mental health using the MHI-5 index of Veit and Ware (scored so that higher scores indicate better mental health). Respondents show a great deal of optimism about the future, seeing themselves as being on step 5.0 out of 10 on the life ladder currently, but expecting to be on step 8.0 in five years’ time. Training increases both current and future subjective well-being by 0.2 to 15
export restraints were supposed to be phased out by the end of 1998; and the remaining restraints in developed countries appear to be directed in significant measure against non-WTO Members.

The total frequency ratios for core non-tariff measures in developed country markets are presented in Table 15 This ratio, is the same frequency measure used in the context of developing countries in the previous section but relates to the ratio of tariff lines affected by a core non-tariff and the measure relative to the total tariff lines.<sup>2</sup> As can be seen from the table there has been a decline in the frequency of imposition of non-tariffs in all developed countries and for all measures between 1993-1996. In two countries, Australia and Switzerland, the markets are virtually free of any non-tariff barriers. In the Quad countries the total frequency ratio of border non-tariff measures ranges from a low of 1.2 per cent in Canada to a 3.8 per cent in the European Union. Comparison with Table 8 as well as Appendix Table 2, and keeping in mind the methodological limitations in some of these measures, strongly suggests that non-tariff measures are much more pervasive in developing than in developed country markets.

The decline in the pervasiveness of NTMs reflects several factors: (a) the tariffication process in agriculture which reduced quantitative controls as well as price related measures such as variable charges (though, of course, it also increased tariffs); (b) the termination of VERs - some, e.g. the VER on sardines by the EU has already been terminated; others were supposed to expire at the end of 1998 - and presumably will lead to further declines in the frequency ratios reported in Table 14; (c) other ad hoc reductions of NTMs.

While the vast majority of products enter developed countries free of non-tariff controls, such controls are pervasive in a few sectors. These sectors include apparel and clothing in the US, EU and Canada, and silk and man made staple fibers in Japan (OECD, 1997, p.61). This reflects to a large extent

> **22** As with the developing country data, the information assumes that measures apply across the board to imports from all countries, and is not based on transaction by transaction information.

51
**Figure 1: Comparison of authors’ estimated temperature and precipitation (using quadratic distance weighted model) with Meteorological Department climate data for Sri Lanka (by district)**

<!-- Start of picture text --> 350 28<br>27.5<br>300<br>27<br>250 26.5<br>26<br>200<br>25.5<br>150<br>25<br>100 24.5<br>24<br>50<br>23.5<br>0 23<br>Jan Mar May Sept Nov<br>Precipitation Estimated Precipitation from Met<br>Temperature Estimated Temperature from Met<br>Notes: Authors’ estimation using SLIS 1999-2000.<br>Figure 2: Aggregate National Impacts of Climate Change Based on Various<br>Climate Models<br>100.00 25<br>80.00 20<br>60.00 15<br>10<br>40.00<br>5<br>20.00<br>0<br>0.00<br>-5<br>HAD3 CCSR PCM CSIRO<br>-20.00<br>-10<br>-40.00<br>-15<br>-60.00 -20<br>-80.00 -25<br>-100.00 -30<br>Standard Error (95% level)<br>Change in Net Revenue<br>Percent Change in Net Revenues (Right-axis)<br>Precipitation (mm) Temperature (C)<br>Change in Net Revenues (%)<br>Change in Net Revenue ($/HA)<br><!-- End of picture text --> Notes: Authors’ estimation using SLIS 1999-2000.

**Figure 2:  Aggregate National Impacts of Climate Change Based on Various Climate Models**

Notes: Authors’ estimation using SLIS 1999-2000.

30