rafmacalaba/datause-encoder-data huggingface dataset:
(NWI) consists of six domains (Tilouine, Cummins & Davern, 2006): (i) satisfaction with economic situation of the country or city, (ii) state of the environment, (iii) social conditions, (iv) satisfaction with national or local government, (v) business, and (vi) security. Wills (2006) has applied these in Bogotá (see Annex 4).

Seemingly, intractable problems, especially those of a societal or political nature, reflect the public’s collective will, or fear, of proposed changes, in addition to their level of well-being and the degree of trust within the community. A community’s responses will be tempered by the ability of city leaders and practitioners to mobilize public support for alternatives. Cities will be increasingly called upon to provide global leadership over the next fifty years. Measuring the attitudes and receptiveness of citizens toward challenging social changes, especially as they progress over time, would be very useful. However this may be well-beyond the scope of typical city indicators, given that measuring subjective well-being and trust is complex, requiring expensive surveys.

### **6.3.2 Measuring Trust**

Each year a 28 country survey of over 750 companies is completed to determine the ‘best places to work’<sup>18</sup> . Consistently the most important aspect for a positive work environment and wellfunctioning organization is _trust_ . The importance of trust within a city is usually not well-defined, recognized, or measured.

If a sound mechanism could be designed, it would also be useful to include other difficult-tomeasure but crucial aspects of a city within city indicators. Conflict resolution, innovation, and local and national cooperation, are also important indicators of a city’s performance.

## **Next Steps**

<mark>The World Bank hopes to partner with several key agencies, governments, and pilot cities over the next two years to develop a set of draft city indicators that could provide a snap-shot of a city’s performance and quality of life. Sustainability and broad adoption of the indicators would be enhanced through: city ownership; mechanisms for third-party verification; internationally wide-spread acceptance and relevance; and initially a greater focus on the methodology and city support, rather than on specifics of individual indicators.</mark> <mark>This proposal builds on UN-HABITAT’s extensive work on indicators, and is strengthened through key partners such as the governments and standards associations of Brazil, Canada, and Colombia; the financial and technical support of the Government of Japan; the Federation of Canadian Municipalities; ICLEI; the World Economic Forum; and especially the staff of all pilot cities.</mark>

# **7.  Proposed Timetable**

The following schedule reflects the World Bank’s proposed work plan to help facilitate the development of a standardized set of city indicators. Support from participating pilot cities will be critical and will largely determine actual schedules.

> 18 Great Place to Work Institute, San Francisco – from Canadian Business, April 10-23, 2006, p 89.

18
# **-** **<u>Table B4.    Average estimated collected tariff rates in Russia in 2001 2005 at industry level</u>**<sup>**a**</sup>

||||||**_Year_**<br>|<br>|||||
|---|---|---|---|---|---|---|---|---|---|---|
||**_20_**|**_1_**|**_20_**|**_2_**|**_20_**|**_03_**|**_2_**|**_04_**|**_2_**|**_05_**|
|**_Industry_**|_mean_|_weighted_<br>_mean_|_mean_|_weighted_<br>_mean_|_mean_|_weighted_<br>_mean_|_mean_|_weighted_<br>_mean_|_mean_|_weighted_<br>_mean_|
|_Electric industry_|2.0|2.0|3.2|3.2|2.6|2.6|2.6|2.6|2.0|2.0|
|<br>||_(0.0)_||_(0.0)_||_(0.0)_||_(0.0)_||_(0.0)_|
|_Oil extraction_|1.7|0.0|1.7|0.0|1.7|0.0|0.0|0.9|0.3||
|<br>|_(2.9)_|_(0.0)_|_(2.9)_|_(0.0)_|_(2.9)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.5)_||
|_Oil processing_|4.9|4.5|4.6|4.7|4.6|4.7|4.9|4.8|4.7|4.8|
|<br>|_(0.5)_|_(0.6)_|_(1.8)_|_(0.7)_|_(1.4)_|_(0.6)_|_(1.4)_|_(0.6)_|_(1.6)_|_(0.6)_|
|_Gas_|5.0|5.0|5.0|4.5|5.0|4.2|4.3||3.9||
||_(0.0)_|_(0.0)_|_(0.0)_|_(1.2)_|_(0.0)_|_(1.7)_|_(1.7)_||_(2.1)_||
|_Coalmining_|2.2|3.0|2.5|4.6|2.2|4.9|1.7|2.3|2.2|0.9|
|<br>|_(2.5)_|_(2.4)_|_(2.6)_|_(0.8)_|_(2.5)_|_(0.5)_|_(2.2)_|_(2.1)_|_(2.4)_|_(1.4)_|
|_Other fuel_<br>_industries_|4.9|4.9|5.0|5.0|5.0|5.0|5.0|5.0|5.0|5.0|
|<br>|_(0.2)_|_(0.1)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|_(0.0)_|
|_Ferrous metallur_|5.4|6.2|6.0|7.1|5.9|7.9|5.9|9.1|6.0|8.5|
|_gy_<br>|_(5.)_|_(4.5)_|_(5.2)_|_(4.9)_|_(5.1)_|_(5.9)_|_(5.2)_|_(6.5)_|_(5.)_|_(6.2)_|
|_Non-ferrous_<br>|9.0|11.3|8.8|12.9|9.0|13.7|8.7|13.8|8.7|13.2|
|_metallurgy_|_(6.9)_|_(7.1)_|_(6.8)_|_(7.4)_|_(7.)_|_(7.)_|_(6.9)_|_(6.9)_|_(6.8)_|_(7.)_|
|_Chemicals and_|7.2|8.1|7.5|8.7|7.6|8.8|7.5|8.8|7.4|8.7|
|_petrochemicals_|_(5.1)_|_(4.9)_|_(7.1)_|_(8.2)_|_(7.5)_|_(7.1)_|_(7.6)_|_(5.9)_|_(7.)_|_(5.1)_|
|Mhil|||||||||||
|ecanca<br>engineering|9.4|9.2|10.1|9.9|10.6|9.9|10.1|11.9|9.8|12.2|
|_and metal-working_|_(7.7)_|_(8.5)_|_(16.)_|_(15.7)_|_(23.6)_|_(11.)_|_(14.9)_|_(11.4)_|_(13.8)_|_(12.2)_|
|_Timber, wood, pulp_|14.1|12.7|14.2|13.6|14.5|13.0|14.5|13.1|14.3|12.9|
|_and paper_|_(5.3)_|_(6.4)_|_(6.5)_|_(7.8)_|_(7.3)_|_(8.7)_|_(7.4)_|_(8.6)_|_(6.4)_|_(8.)_|
|_Construction_|12.1|13.4|12.3|13.7|12.3|14.6|12.1|14.4|12.2|14.5|
|_materials_|_(5.8)_|_(5.2)_|_(7.)_|_(5.2)_|_(6.4)_|_(6.)_|_(5.6)_|_(5.2)_|_(5.6)_|_(5.2)_|
|_Light industry_|15.2|17.0|16.3|20.6|17.0|19.1|15.5|18.6|14.8|18.8|
|<br>|_(19.4)_|_(9.7)_|_(15.5)_|_(13.3)_|_(17.3)_|_(11.8)_|_(10.9)_|_(9.6)_|_(8.)_|_(8.3)_|
|_Food industry_|13.0|14.8|13.1|19.5|14.2|24.8|14.8|22.0|14.7|21.8|
||_(9.5)_|_(9.4)_|_(12.4)_|_(16.1)_|_(14.7)_|_(29.6)_|_(16.7)_|_(30.9)_|_(16.8)_|_(25.6)_|
|_Othr idtri_|11.2|8.9|11.5|9.4|11.6|9.4|11.7|9.4|11.6|8.8|
|_e nuses_|_(7.6)_|_(7.8)_|_(8.7)_|_(8.1)_|_(8.5)_|_(8.3)_|_(8.6)_|_(7.8)_|_(8.4)_|_(7.5)_|
|_Agriculture and_<br>|6.8|7.3|8.3|12.3|8.2|13.3|8.0|11.9|7.9|11.3|
|_forestry_|_(5.5)_|_(5.6)_|_(7.6)_|_(10.3)_|_(8.3)_|_(11.3)_|_(7.4)_|_(9.7)_|_(7.3)_|_(8.9)_|
|_Other goods-_<br>|17.3|10.0|17.6|6.6|17.8|7.1|16.3|6.4|15.7|9.3|
|_producing sectors_|_(16.5)_|_(33.3)_|_(13.2)_|_(24.)_|_(14.4)_|_(25.3)_|_(11.2)_|_(19.6)_|_(8.8)_|_(13.8)_|

45
**Table 3a. Summary Results: Poverty reduction among the elderly with means-tested minimum pensions** = <u>Poverty line 1⁄2 of the national median per capita income</u>

|**Country**|**Current**<br>**poverty**<br>**headcount**<br>**ratio**|**Absolute**<br>**reduction**<br>**in poverty**<br>**headcount**<br>**ratio**|**Relative**<br>**reduction**<br>**in poverty**<br>**headcount**<br>**ratio**|**Relative**<br>**cost of**<br>**program,**<br>**elderly**<br>**>=60**<br>**years**|**Relative**<br>**cost of**<br>**program,**<br>**elderly**<br>**>=65**<br>**years**|
|---|---|---|---|---|---|
|**Argentina**|0.13|0.09|0.685|0.019|0.012|
|**Bolivia**|0.29|0.17|0.597|0.012|0.007|
|**Brazil**|0.06|0.01|0.198|0.001|0|
|**Chile**|0.15|0.05|0.369|0.006|0.005|
|**Colombia**|0.31|0.15|0.48|0.029|0.02|
|**Costa Rica**|0.32|0.24|0.738|0.021|0.016|
|**Domin Rep**|0.22|0.13|0.621|0.019|0.014|
|**Ecuador**|0.25|0.1|0.402|0.009|0.007|
|**El Salvador**|0.17|0.11|0.656|0.024|0.018|
|**Guatemala**|0.24|0.08|0.347|0.007|0.005|
|**Honduras**|0.31|0.16|0.508|0.017|0.013|
|**Mexico**|0.28|0.12|0.405|0.009|0.007|
|**Nicaragua**|0.17|0.02|0.133|0.005|0.003|
|**Panama**|0.23|0.15|0.643|0.018|0.013|
|**Paraguay**|0.23|0.14|0.591|0.019|0.014|
|**Peru**|0.23|0.15|0.633|0.022|0.016|
|**Uruguay**|0.08|0.05|0.709|0.017|0.011|
|**Venezuela**|0.21|0.12|0.581|0.023|0.016|

Source: Authors’ calculations using data from the Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank) OECD adjusted HH sizes are used in calculating the poverty headcount ratios.

25
2

# **1 Introduction**

At the start of this decade, around 45% of the population of developing countries was urban. By 2030 the number will have increased by around 10 percentage points (Figure 1). Africa’s urbanization is particularly rapid; it is twice as fast as that of Europe during its early urbanization (Figure 2). The spread of urban agglomerations in Africa is indeed quite dramatic (Figures 3a, 3b and 3c).

With this background, it is now accepted that urbanization is (i) a defining feature of development, (ii) proceeding at a very rapid pace and, furthermore, (iii) central to poverty reduction strategies. However, the urbanization discourse tends to take place at an aggregative level, with the overall national rate of urbanization taking center stage, both as an outcome to be explained, and as a causal variable explaining growth, inequality and poverty. This obscures key analytical features of the _composition_ of the urban landscape, and often narrows the urbanization policy debate simply to the issues relating to large cities.

The rapid emergence of large and mega cities in the developing world<sup>2</sup> has indeed been spectacular, with their numbers doubling from 22 large cities and 14 megacities in 1995, to 44 and 29 in 2015 (UN‐Habitat, 2016). Nonetheless, although these cities are in many ways the leading edge of urbanization, grasping the limelight because of their influence and economic importance, they are not the fastest growing, nor do they represent the majority of the urban population. Worldwide, the fastest growing urban centers are also those with less than one million inhabitants (UN‐Habitat, 2016). Similar observations hold in Africa. While two‐fifths of its urban population is in cities larger than 1 million, two‐fifths is also in small towns—those with population less than 250,000 (Figure 4), with urban centers less than 1 million absorbing as much as 75 percent of urban population growth (UN‐Habitat, 2014).

> 2 Defined as housing between 5 and 10 million and more than 10 million urban dwellers respectively (UN‐Habitat, 2016).
# **6. Discussion and Conclusions**

This paper analyzed the impact of return migration from Saudi Arabia and the Gulf countries on the transfer of gender norms to Kerala. After accounting for selectivity and endogeneity through IVs, the findings showed that returning migrants from Saudi Arabia tend to exhibit conservative values in all cases except employment, and particularly with regard to general attitudes and household decision making. In contrast, those returning from the Gulf did not exhibit significantly different attitudes compared to those with no migration experience. The two exceptions were general gender attitudes, where they were more conservative than non-migrants, and female employment, where they were relatively less conservative. These results are similar to previous studies of return migrants to Jordan and Egypt from other Middle Eastern countries.

With increasing migration trends globally, these findings have important policy implications. Often, the remittances to migrant sending countries are considered a blessing, while the impact of social remittances—including those on social norms and gender attitudes—are overlooked. This paper showed that even though social remittances can have beneficial impacts on migrant populations and their home communities (e.g., in terms of transfer of socially beneficial values such as those on education, fertility and education, as previous studies have pointed out), the potential harmful ones should not be overlooked.

The results indicate that there is considerable heterogeneity in social remittances across migrant destinations. This finding suggests that proactive steps are needed to mitigate the detrimental impacts of these social remittances in migrant sending countries. Measures should be adopted to mitigate the impact of the more extreme attitudes transferred from other countries, with a particular focus on migrants returning from destinations that are associated with the adoption of such extreme attitudes. These measures may include penalties for GBV and other extreme measures, as well non-discriminatory regulations regarding employment in migrant sending countries. The issues regarding GBV, extreme attitudes toward women, as well as discriminatory employment practices need to be addressed systematically through education and communication programs to prevent the transfer of such norms through return migration. Several migrant 28
government<sup>22</sup> . This is in particular the case of the financing variables, K and F , and of the institutional quality variables θ, for example corruption. On the other hand, the effects of shocks or of the existence of a regulator, appear to be unchanged.

Tables 7 to 9 present the results from random probit estimations, using as dependent variable the dummy indicating, for each project i in country n, whether there is a renegotiation initiated by the government at time t, where t varies between 1989 and 2000. Additionally, Table 10 presents robustness checks using as a dependent variable the dummy variable indicating whether there is a renegotiation initiated by the government or by both parties (Tables 7 to 10 are in the Appendix). The model, which is a linearized version of the theoretical equation giving the probability of renegotiation, is the following: where 1[.] is the indicator function taking value 1 whenever the statement in brackets is true, and 0 otherwise; yint is the binary variable indicating whether concession i, in country n, at time t, was renegotiated or not at the initiative of the government; xi is a vector of time invariant characteristics of the concession contracts; zint is the number of years, since the award of concession i, in country n; Ent is a vector of environmental characteristics (macroeconomic indicators, elections and institutional indices); eint is the error term; and α1, α2, and α3 are the vectors of parameters corresponding to xi, zint and Ent respectively.

Table 7 shows the basic specifications. Table 8 presents instrumental variable estimations, where we make use of a two-stage procedure to address the potential endogeneity of contract clauses<sup>23</sup> . Table 9 introduces some additional variables and interactions related to institutional and political aspects.

We instrument the variables found to be endogenous applying the simple Rivers-Vuong (1988) test. The instruments are sectors, corruption, bureaucracy quality, rule of law, and existence of regulatory body, which are exogenous in the sense that they are not determined by the risk of potential

> 22The absolute magnitude of the effects, however, may vary.

> 23Although such an estimation is in principle biased when the endogenous variable is binary (see Wooldridge, 2002), support for it can be found in Angrist (1991) who states that the bias is negligible. The second stage standard errors are computed using a bootstrapping procedure. See a more detailed discussion in GLS.

28
<!-- Start of picture text --> Figure 1b. Poverty headcount ratio<br>All individuals<br>Elderly from EHH1 & EHH2<br>Argentina Bolivia Brazil ChileColombiaCosta RicaDominican RepublicEcuadorEl SalvadorGuatemalaHonduras MexicoNicaraguaPanamaParaguay PeruUruguayVenezuela, RB<br>0.216 0.214<br>0.199<br>.2<br>0.169<br>0.154<br>0.149<br>.15 0.142 0.140<br>0.131<br>0.122 0.122<br>0.097 0.101 0.103 0.100 0.100<br>.1 0.083 0.090<br>0.069 0.074 0.070 0.073 0.065 0.064 0.067<br>0.057<br>.05 0.042<br>0.029 0.031<br>0.014 0.013 0.015<br>0.009<br>0.004 0.004 0.001<br>0<br><!-- End of picture text -->

# Poverty line = $2.5 a day

Household incomes were equivalized using the OECD Equivalency Scale Adjusted household size [= 1+(# of adults-1)*0.5 + (# of children*0.3)] An adult was a household member aged 18 years or more, & a child was a household member aged less than 18 years at the time of the survey Elderly are household members aged 60 years or more Households with >=1 elderly member are divided into 2 subsets: EHH1 (elderly living alone by themselves) & EHH2 (consisting of both elderly and non-elderly members) 28
An often-cited problem with multilevel models is the sample-size requirements at the higher levels of the hierarchy. Data sparseness or a limited number of observations per group can lead to inaccuracy of parameter estimates. Although there is no official rule for the number of observations per group (metropolitan areas in our case), rules of thumb in the literature recommend a minimum of 20 to 50 observations per group within each component (Raudenbush & Bryk, 2002; Kreft & De Leeuw, 1998; Maas & Hox, 2004). Several simulation studies have shown that multilevel modeling may be used even with as little as 10 observations per group (Bell et al., 2014; McNeish & Stapleton, 2016). In this study, we therefore include all metropolitan areas with at least 10 establishments surveyed.

In addition, the standard multilevel model assumes that the firm-level explanatory variables are uncorrelated with the metropolitan area-level error terms. However, both theoretically and empirically, such an assumption is difficult to meet due to the sorting of firms and workers across space and not correcting for this would lead to inconsistent parameter estimates. As indicated by Snijders and Bosker (2011), the correlation between the lower-level predictor variables and higherlevel error terms can be removed by including higher-level means of the firm-level predictor variables in the regression model, a procedure known as the Mundlak (1978) correction using the _mundlak_ command in Stata (Perales, 2013).<sup>11</sup> Hence, our regressions models are augmented with this correction, yielding the following equation: i 0 + ∑ + ∑ + ∑ 0i (3) qq qq rr in which MM = ββ are the metropolitan area averages of the firm-level variables ii=1 ββqq0ααqqi ii=1 γγqq0αα<sup>�</sup> qqii ii=1 φφrr0ZZrrii + oo0ii + SS , which absorb all possible correlations between the firm-level variables and group-level random effects qqii qqi . Several αα<sup>�</sup> αα studies have shown that the Mundlak correction provides the estimates and standard errors 0ii oo equivalent to the fixed effects model (Huang, 2018; Oshchepkov and Shirokanova, 2020), but still offers the possibility (unlike the FE model) to include metropolitan area variables.

An alternative would be to estimate the models using OLS with clustered standard errors. Although these estimations are included in the sensitivity analysis of this paper in Table 1, we refrain from using this estimation strategy in our baseline models because the key difference between the methods concerns the estimation of the standard errors, where multilevel modeling typically allows for a lower number of clusters than OLS with clustered standard errors for robust variances of coefficients. In addition, OLS with clustered standard errors is more vulnerable to

> 11 As noted by Kim and Frees (2007) and Huang (2018), this is also a main reason why economists have shied away from using multilevel modeling. See, for instance, Burger et al. (2011) for an application of the Mundlak correction to the study of agglomeration economies.

14
# 6.2 Case Example TWO

The second case example is a potential approach for economic evaluation of an intervention to utilize deep learning neural networks to interpret chest radiography (CXR) to screen and triage patients with pulmonary tuberculosis (TB). The example draws on an existing assessment of the diagnostic accuracy of three systems “CAD4TB”, “qXR” and “Lunit” for a hypothetical decision for scale up of the interventions in the Cameroon country context, funded by domestic health resources. The existing retrospective assessment investigates the diagnostic accuracy of the systems using CXR images collected from Nepal and Cameroon as part of different studies. Included populations were adults (aged 15 years or older) with symptoms suggestive of TB (cough more than 2 weeks, fever, night sweats, weight loss). While the existing evaluation provides useful insights into the accuracy of the different systems compared to existing methods of radiologist interpretation of CXR, an economic evaluation would increase the understanding and representation of the different components of value for the interventions. In this example, it is assumed the government seeks information to guide allocatively efficient use of the limited health budget.

**Table10  Framework Example 2 – AI-enabled diagnostic:**

|**FRAMEWORK**<br>**COMPONENT**|**SPECIFICATION**|**CONSIDERATIONS**|
|---|---|---|
|**STEP 1: IDENTIFY T**|**HE DECISION MAKER AND**<br>|**CONTEXT**<br>|
|**STEP 2. DETERMIN**|Country government,<br>Cameroon<br>**E THE INTERVENTION TYP**<br>|Existing evidence is from Nepal and<br>Cameroon, analysis would isolate<br>Cameroon data only and/or consider the<br>transferability of data from Nepal to<br>Cameroon setting<br>**E**<br>|
||Deep Learning system to<br>analyze digital chest<br>radiographs<br>for TB-related<br>abnormalities|Type: Health Service User targeted. (WHO<br>Class 2.10: “laboratory and diagnostics<br>imaging management”) with predictive<br>analytics component.|
|**STEP 3. ESTABLISH**<br>|**THE LEVEL OF COMPLEXI**<br>|**TY**<br>|
|**Intervention**|High complexity|Depending on operation and interface the<br>intervention may represent low complexity<br>to the user|
|**Outcomes**|High complexity|For immediate diagnostic accuracy there is<br>low complexity. Improved TB management<br>and increased health is high complexity|
|**Causal pathway**|High complexity|**Table continued...**|