rafmacalaba/datause-encoder-data huggingface dataset:
primary and lower secondary. Besides, we include public spending related to a child care program ( _Centro de Atención Integral a la Familia_ - CAIF) that targets children of up to three years of age in low-income households. Overall, it adds up to 18.6 percent of total social spending and represents 3.8 percent of GDP.

# **Table 2.** Social Spending. Uruguay, 2017

|Concept|Percentage of<br>social spending|Percentage of GDP|Included in<br>analysis|
|---|---|---|---|
|**Direct transfers**|**11.81**|**2.54**||
|_Plan de equidad_|1.74|0.37|Yes|
|Non contributory pensions|2.62|0.57|Yes|
|Food|0.84|0.18|Yes|
|Other direct transfers|6.60|1.42|Yes|
|**Education**|**18.84**|**4.06**||
|Child care|0.78|0.17|Yes|
|Preschool and Primary level|7.07|1.52|Yes|
|Secondary level|7.09|1.53|Yes|
|Tertiary level|3.90|0.84|Yes|
|**Health**|**30.71**|**6.61**||
|Contributory - Public|3.98|0.86|Yes|
|Contributory - IAMC|15.21|3.28|Yes|
|Contributory - Private insurance|0.41|0.09|Yes|
|Noncontributory<sup>11</sup>|9.27|2.00|Yes|
|FNR (high risk sickness)|1.84|0.40|No|
|**Contributory pensions**|**38.64**|**8.32**|**No**|
|**Total social spending**|**100.00**|**21.54**||

Sources: BPS, MEF, Junta Nacional de Salud (JUNASA) Note: Public health services include funding from general public budget and FONASA.

Heath care public transfers accounted for 6.4 percent of GDP and, as educational transfers, excludes investment. Part of these transfers are funded by the general public budget and correspond to spending related to direct provision of health care services for poor people (noncontributory). Another portion of health care spending is funded by contributions to FONASA. Contributors to FONASA must choose an institution in which to take care of their and their families’ health. The health care institutions receive a transfer per beneficiary that varies with beneficiary’s age and sex. These institutions may be public institutions (the same that are also funded by general public budget), private enterprises in the mutual system ( _Instituciones de Asistencia Médica Colectiva_ - IAMC) and private insurance companies.

> 11 Includes ASSE (non-FONASA), Clinicas hospital, Military and Police hospitals.

7
found that 76%, 47%, and 18% of the harvested areas did not show yield improvement in the recent decade for wheat, rice, and maize, respectively.

Notwithstanding the prevailing, somewhat pessimistic, outlook in the studies reviewed above, it is essential to note that not all research aligns with such a perspective. In an influential work, Alexandratos (1999) contended that global agricultural production is poised to meet, or potentially exceed, the demands of food requirements. Alexandratos also emphasized that the critical concern lies in the persistent challenges of poverty in low-income countries. Similarly, an editorial in the journal _Nature_ (2020) offered a nuanced perspective on productivity growth and food security, noting that depending on research advancements in the sector, it is feasible to meet the 2050 global food demand at an acceptable cost. Ausubel et al. (2013) echoed similar views, highlighting the substantial increase in crop yields over the past 50 years, alongside a marked decline in caloric requirements relative to GDP.

To summarize, the literature provides mixed evidence on yield growth trends, with findings varying based on the specific commodities, countries, regions, and time periods analyzed. While some studies indicate a decline or stagnation for certain crops and regions, others report an acceleration or consistency with a linear growth trend. This raises a critical question: from a global perspective, has aggregate crop yield experienced stagnation or deceleration? Most studies have predominately employed weight-based yield to analyze productivity growth for single commodities. However, this approach does not adequately capture the rate of productivity change across all combined commodities, which is crucial for analyzing food security and sustainability challenges. In this study, we propose constructing global yield growth indices based on the calorific content of crops to assess aggregate yield growth patterns at the global, regional, and commodity levels. These patterns are discussed in the following sections.

# **3. Methods and Data**

Indices have been used in several contexts within commodity markets, such as the aggregation of various commodity prices into a single index and the construction of aggregate agricultural productivity indices. A critical issue in the aggregation process is the selection of appropriate weights. For instance, the weights used in the World Bank’s commodity price indices are based on the export values of emerging markets and developing economies (World Bank 2024). For agricultural productivity studies, weights are typically based on values derived from FAO international crop prices, measured in Geary-Khamis dollars per ton, also known as “international dollars” (Prasada Rao 1993). Numerous studies have employed FAO-based aggregation, including Adamopoulos and Restuccia _— 5 —_
15 foreign currency import prices and lower foreign currency export prices, the share of distortionary costs in total QR costs falls from 32 percent to 8 percent of total costs. Finally, adding unilateral tariff reduction now results in a welfare loss of $2.9 billion because of the dominating termsof-trade effect, a result similar to those found in the global simulations mentioned in section 2.

Our welfare cost estimates are significantly higher than previous GE estimates and than mcit PE estimates. This is so for several reasons. First we properly account for the $14 billion transfer associated with the US system of QRs. Second, our results are not dominated by terms-of-trade effects. 18/ Third, in comparison with most previous PE estimates, we have benefitted from the detailed work on premia estimates by Feenstra (1985a), Hamilton (1986) and Dinopoulos and Kreinin (1988) which are higher than previous estimates. 19,

# 5. Employment

Table 5 presents estimates of the employment effects of simultaneous removal of QRs in textiles, autos and steel for the central elasticity case. The simultaneous removal of QRe also points out to conflicting worker interests across the three industries subject to QRs. While the steel and textiles and apparel sectors would lose almost as many jobs when all QRs are removed than when they are removed in their sector alone, the autos sector gain. 1,900 jobs when the QRs on steel (and textiles and apparel) are removed concurrently with the VER on autos. This is so because steel, an input into auto production is cheaper and because the income elasticity of demand for autos is high. Both effects benefit the domestic auto industry which expands even when QR protection is removed.
# **5 Conclusion**

This paper investigates the role of product market competition and intellectual property rights protection in biasing the technological change toward specialization or diversification. The paper is suggestive of the role the government might play in order to foster economic progress: while the degree of product market competition and IPRs have been treated as exogenous in the analysis, they can be affected by government policies. If sectoral concentration and specialization are positively linked to development, policies that strengthen the degree of IPRs and/or that internalize the social returns of innovating vertically, can help to promote economic growth. If instead, diversification is positively related to development, policies that restrict the degree of product market competition, such as price ceilings, can foster economic progress.

# **References**

- [1] Acemoglu, D., 2002, ”Directed Technical Change,” Review of Economic Studies, 69 781-810.

- [2] Acemoglu, D. and F., Zilibotti, 1997, ”Was Prometheous Unbound by Chance? Risk, Diversification, and Growth,” The Journal of Political Economy, 105, (4), 709-751.

- [3] Aghion, P., Harris, C., and J., Vickers, 1997, ”Competition and Growth with Step-by-Step Innovation: An Example,” European Economic Review, Papers and Proceedings, 771-782.

- [4] Aghion, P., Harris, C., Howitt, P., and J., Vickers, 2001, ”Competition, Imitation, and Growth with Step-by-Step Innovation,” Review of Economics Studies, 68, 467-492.

- [5] Aghion, P., and P., Howitt, 1992, ”A Model of Growth through Creative Destruction,” Econometrica, 60, 323-61.

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(particularly primary education) in affecting fertility seems to be more robust in analyses that aim to identify a causal relationship between fertility and education.

Increasing the educational attainment of girls also reduces fertility rates by increasing the age of marriage and first birth. First, more highly educated girls marry later and have lower fertility. Second, higher educational enrollment rates may increase the opportunity cost of children for household work and thereby reduce the desire for large families. Improvements in female education are positively associated with lower rates of teenage pregnancy. Households in the bottom 40 percent of the income distribution (B40) tend to have lower female educational attainment than households in the top 60 percent of the distribution (T60). The B40 households are also seen to have higher rates of teenage parents than T60 households (Table 9). Higher education also increases the opportunity cost of having a child due to the potential for income from work, and so there is a delay in the first birth and marriage. A delay in the age at first birth has the effect of reducing lifetime fertility. Women living in households in the top 60 percent of the income distribution tend to have a higher median age at first birth than households in the bottom 40 percent. Delaying the age at first birth also has immediate benefits beyond reducing fertility rates, such as improving maternal health (U.S. National Research Council 1989).

**Table 9 Demographic patterns and sharing** **<u>prosperity</u>**

|||1. Share of females aged 15-19<br>who aremothers, percent|2. Women’s median age<br>atfirst birth|
|---|---|---|---|
|||B40<br>T60|B40<br>T60|
|1|LIC|24.23<br>16.94|19.73<br>20.18|
|2|LMC|19.33<br>10.51|19.90<br>21.24|
|3|UMC|16.08<br>8.09|21.08<br>22.39|
|||3. Average number of births<br>per woman|4. Share of women who do not want to<br>become pregnant again but not using<br>contraception, percent|
|||B40<br>T60|B40<br>T60|
|1|LIC|6.12<br>4.68|27.39<br>24.95|
|2|LMC|4.74<br>3.14|24.27<br>19.29|
|3|UMC|3.97<br>2.52|18.87<br>13.24|

_Source:_ World Bank staff.

_Note_ : Data are from Demographic and Health Surveys. B40 refers to households in the bottom 40 percent of the wealth distribution, while T60 refers to households in the top 60 of the wealth distribution. Unmet need for family planning is defined as the percentage of women who do not want to become pregnant but are not using contraception As the household’s child dependency ratio falls and the share of working-age people increases, per capita income is likely to increase. This in turn relaxes the social and household budget constraints. Families who have fewer children will have more per capita resources at their disposal for consumption as well as investment. An increase of 1 percentage point in the share of working age population is estimated to be associated with a reduction of 0.75 percentage point in the poverty rate (Table 10). Yet, results are significant only at 90 percent of confidence in one of the GMM 21
reported in Table 5. It is important to note that in order to secure the dominance of the GIT across the entire rectangular area, parameter _m_ must be very small, making the GIT very similar to an absolute quota.

Additionally, the figure indicates that the value of _m_ needed to ensure that a GIT dominates a quota for relative costs is always lower than the one required for the marginal abatement costs. If we take again the previous example— _σY /σE_ = 2 and _ρ_ = 0 _._ 3—, _m_ needs to be lower than 0 _._ 075 for a GIT to dominate a quota vis-`a-vis the uncertainty on total relative costs, to be compared with 0 _._ 3 when uncertainty on marginal abatement costs is considered.

Similarly, the optimal parameter _m_<sup>_∗_</sup> that minimizes the variance, when it exists, takes a very wide range of values over the set of plausible values for _ρ_ and _σY /σE_ : from 1 to nearly 0 according to Eq.(20). Thus, we find again that setting an optimal GIT cannot be done properly without first reducing the uncertainty on the values of _ρ_ and _σY /σE_ .

# **6.3 Quantifying Uncertainty**

In this section, we present numerical values for the absolute level of uncertainty on output parameters over our set of policy variables. To do so, we build three representative cases, the characteristics of which are summarized in Table 8. The first case (‘average’) is based on the average of the parameters for industrialized countries reported in Table 5. By contrast, the ‘pro-intensity’ case is constructed to be the most favorable for intensity targets, i.e., with the highest value of _σE_ and _ρ_ for industrialized countries from Table 5, the highest value for _α_ and the lowest value for _σY_ . The ‘pro-cap’ case is the exact opposite. As in the previous section, we use _Q_ = _q_ = 0 _._ 75.

Table 9 presents the normalized standard deviations for each output variable under each of the instruments—subject to the parameter values of one of the three cases. It shows that an intensity target (LIT or GIT) leads to non-negligible uncertainty on emissions, especially—by construction—in the pro-cap case (0 _._ 18). The uncertainty on the abatement effort is larger, giving rise to very high uncertainties on marginal abatement costs, ranging from 68% (pro-cap case, GIT) to more than 100% (pro-cap case, LIT). Uncertainty on total relative costs is higher 29
**Box 2. Pairwise Correlations for Components of Related Lending Index**

|Single Owner Limit|Single Owner Limit<br>1<br>[n=74]|Related Party<br>Limit|Non-Financial Firm<br>Ownership of<br>Banks|Bank Ownership of<br>Non-Financial Firms|
|---|---|---|---|---|
|Related Party Limit|0.758***<br>[n=74]|1<br>[n=74]|||
|Non-Financial Firm|0.474***|0.482***|1||
|Ownership of Banks|[n=74]|[n=74]|[n=74]||
|Bank Ownership of|-0.027|0.087|0.079|1|
|Non-Financial Firms|[n=74]|[n=74]|[n=74]|[n=74]|

*** indicates p<0.01.

# <u>C.  Rule of Law</u>

In order to measure the strength of the rule of law we use the index developed by Kaufmann, Kraay, and Zoido-Lobaton (1999, hereafter KKZ). The KKZ data are available for 1996, 1998, 2000, and then annually from 2002 to 2008.<sup>5</sup> We use the index values from 2004 because it is close to coterminous with the 2003 BCL survey, and because it offers wider country coverage than the 2002 survey. We note that our results are not sensitive to the index year chosen, because the KKZ rule of law index does not vary dramatically over time.<sup>6</sup>

# <u>D.  Depositor Monitoring</u>

We use the level of deposit insurance to measure the intensity of depositor monitoring. More specifically, we compute, for each country _i_ , the inverse of the ratio of deposit insurance coverage to per capita GDP:

> 5 KKZ data set and its description are available at http://www.worldbank.org/wbi/governance/data.html. See Haselmann et al. (2010) on the effects of alternative legal changes on bank lending in transition economies. 6 The correlation between the 2004 data and those from other years runs from .91 to .98.

9
### Where:

Prob (A) is the likelihood of the respondent _i_ preferring Job A, the baseline scenario B is the randomly allocated hypothetical job description _j_ from 1 to 4 to individual _i_ εε is the error term We further analyze the correlates of the preference for a more flexible job description by estimating a Linear Probability Model with the following specification: Equation 2

### Where:

Prob (FLEX) is the likelihood of the respondent _i_ preferring the more flexible job option between the two choices presented, implying also a certain percentage cut in earnings compared to the less flexible option B is again the randomly allocated hypothetical job description _j_ from 1 to 4 to individual _i_ J is the matrix of self-reported characteristics of individual _i_ ’s current job P is the matrix of individual _i_ 's valuations of alternate job characteristics X is the matrix of individual (demographic and economic) characteristics εε is the error term

# 3. Results

## a. Descriptive Statistics

Table 1 below presents the mean and standard deviation of key demographic characteristics of the sample, separately for freelancers and those in standard employment. For most variables we dint observe any stark differences in the two groups, with a few exceptions; freelancers tend to have slightly higher self-assessed risk appetite and are less likely to prefer receiving a delayed but higher payment compared to an immediate but lower one. They are also more likely to be single, and not to have children.

Table 1 Descriptive Statistics

||Freela|ncers|Standard E|mployment|
|---|---|---|---|---|
||Mean|Std Dev|Mean|Std Dev|
|Age|35.577|10.655|36.183|9.734|
|Female|0.453|0.498|0.500|0.501|
|Single = 1|0.487|0.500|0.373|0.484|
|Has children = 1|0.382|0.486|0.560|0.497|

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# **Cholera Risk in Lusaka: A Geospatial Analysis to Inform Improved Water and**

# **Sanitation Provision**

## **A1 AUTHORS & AFFILIATIONS Peter W. Gething**<sup>**i,ii §**</sup> **Sophie Ayling**<sup>**iii**</sup> **Josses Mugabi**<sup>**iii**</sup> **Odete Duarte Muximpua**<sup>**iii**</sup> **George Joseph**<sup>**iii**</sup> **Solomon Sitinadziwe Kagulura**<sup>**iv**1</sup>

JEL: L95;C21; C19: I18 Key Words: Water, Sanitation, Utilities, Urban, Spatial Modelling, Targeting, Infrastructure, Cholera, 1 i. John Curtin Distinguished Professor, Curtin School of Population Health, Faculty of Health Sciences, Curtin University, Kent St, Bentley, WA 6102, Australia ii. Telethon Kids Institute, Perth Childrens Hospital, Nedlands, WA6009, Australia. iii. Water Global Practice, World Bank Group, 1818 H Street NW, Washington DC, 20433, USA. § Corresponding author The authors gratefully acknowledge Lusaka Water and Sanitation Company for their collaboration namely Jonathan Kampata (Managing Director, LWSC), Kennedy Mayumbelo (Project Manager – LSP), Mwansa Nachula Mukuka (Sanitation Specialist, LSP) Kalikeka Malate (Senior Engineer, Project Planning, LSP), Lusungu Nyirenda (Wastewater Specialist, LSP). The cholera case data was made available with thanks to the Zambian Ministry of Health and Mazyanga Mazaba liwewe, Prof Victor Mukonka, Prof Nathan Kapata and Dr Muzala Kapina from the Zambian National Institute of Public Health (ZNPHI). The household water and sanitation survey data collection was made possible with then Field Coordinators Sensio Banda and Gertrude Namitala; survey firms Fibonacci Engineering and Palm Associates.

From inside the World Bank, the work was made possible with the support of operational Task Team Leaders (TTLs) Josses Mugabi, Odete Muximpua, Ruth Kennedy Walker and Ai-ju Huang from the Water Global Practice of the World Bank as well as Collins Chansa, Senior Economist, Health Global Practice. Comments were gratefully received from Solomon Sitinadziwe Kagulura, Senior Health Specialist, Health Global Practice. This work was financed by the World Bank Global Water Security and Sanitation Partnership (GWSSP).

The findings, interpretations and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments whom they represent. The World Bank does not guarantee the accuracy of the data included in this work. Official delimitation of areas and borders might not reflect the official position of the World Bank Group. Country borders or names do not necessarily reflect the World Bank Group's official position. These maps are for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.
the reference group used to correct for the so-called “period effects,” which refer to the impact on earnings of changes in the general conditions of the US labor market over a decade. Assuming that overall economic circumstances affect immigrants and natives in similar ways at the same point in time, period effects can be taken care of by evaluating immigrants’ performance _relative_ to the performance of a reference group, usually composed of natives. As an illustration of how the choice of the reference group differs in the literature, while Borjas (1987) uses earnings of a fixed cohort – persons who migrated before 1950 – to normalize earnings in the census years 1970 and 1980, LaLonde and Topel (1990) recommend that wage growth of immigrants be normalized by wage growth of immigrants of the same ethnicity who have been in the US for more than 30 years or of ethnically similar natives.<sup>15</sup> Duleep and Regets (1994) open a new debate related to cohort effects by claiming that research showing sluggish growth rate of US male immigrants’ versus natives’ earnings makes an incorrect assumption according to which different US immigrant cohorts experience the same growth in earnings. Duleep and Regets suggest, that in reality the earnings growth rates differ by cohort and are inversely related to entry earnings: the lower the entry earnings the higher the earnings growth rate, therefore the more likely the convergence between immigrants’ and natives’ performance levels. In the authors’ opinion, Borjas’s belief that entry earnings proxy reasonably well the cohort quality is a false premise to start with, because entry earnings hide unobservable skills, not immediately valued in the US labor market but useful for the acquisition of US skills. Otherwise stated, the low initial earnings of immigrants arriving in recent cohorts may be due to more intensive human-capital investment, which will lead to higher earnings later in life. As a continuation of this argument, Duleep and Regets (1997, 1999) design a Immigrants Human – Capital Investment (IHCI) model, the thesis and central finding of which is that, conditional upon education and age, immigrant entry earnings and earnings growth are inversely related. In response to criticism from Duleep and Regets (1994), Borjas (2000) relaxes the constraint of constant growth in immigrant’s earnings by cohort and evaluates whether the rate of wage convergence exhibits cohort effects, i.e. whether most recent immigrant cohorts experience faster or slower wage growth compared to earlier ones. His analysis of US Censuses 1970, 1980 and 1990, indicates presence of conditional convergence in the immigrant population, in the sense that the wages of immigrants having the same initial level of human capital converge over time. However, this finding is not robust to simple attempts to control for the bias introduced by

> 15 Other studies that have shown particular interest in addressing the period effects bias are Baker and Benjamin (1994) and Bloom and Gunderson (1991) both of which focus on the immigrants to Canada.

7