rafmacalaba/data-use-mentions-extended huggingface dataset:
the voucher scheme in the region. The RVC was responsible for allocating vouchers to districts based on established criteria; estimating demand for agricultural inputs, based on historical input-use data, and providing this information to AIS; reviewing information collected from each district, including data on production, cropped area, input use, prices and marketable surplus; informing districts about their voucher allocations and initiating further allocation of vouchers to Wards and Villages by the local government authorities; helping to monitor implementation of the voucher scheme; and compiling NAIVS progress reports from the districts to submit to the NVSC Secretariat.

The District Voucher Committee (DVC) was made up of the District Agriculture and Livestock Officer (DALDO) and representatives of farmer groups, agro-dealers, and civil society and NMB and established under the chairmanship of the District Commissioner. In close collaboration with the respective district NAIVS Forum, the DVC implemented the voucher scheme in its respective district. In addition to allocating vouchers to wards and villages based on established criteria, the DVC monitored implementation at the village level and prepared and submitted implementation progress reports to the RVC for transmittal to AIS at the national level.

Finally, the Village Assembly (VA), in consultation with the Village Council and Village Government, organized the election of the Village Voucher Committee (VVC) members (three 3
.021|1521|
|Egypt, Arab Rep.|0.523|0.009|9541|Senegal|0.588|0.026|838|
|Jordan|0.474|0.014|4118|Sierra Leone|n.a|n.a|n.a|
|||||South Africa|0.521|0.025|1325|
|South Asia||||Tanzania|0.559|0.023|1617|
|Afghanistan|n.a|n.a|n.a|Togo|0.446|0.029|1130|
|Bangladesh|0.673|0.009|4275|Uganda|0.648|0.023|876|
|India|0.658|0.006|13288|Zambia|0.664|0.016|1710|
|Nepal|0.638|0.016|1816|Zimbabwe|0.569|0.026|1150|
|Pakistan|0.595|0.009|6202|||||

_Notes_ : This table presents the sibling correlation estimates for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1970s birth cohort sample.

40
Figure 7. Payouts relative to premia for rainfall and livestock insurance <!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2 livestock<br>weather<br>1.5<br>1<br>0.5<br>0<br>2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Note: Figure plots the ratio of total payouts to total premia paid for livestock insurance and rainfall insurance policies sold by BASIX across all states. Source: BASIX administrative data.
Low|4.40|3.58|3.93|21.5|3.65|3.12|3.10|
|y|High|5.84|4.26|4.77|13.0|4.79|3.88|4.34|

_All variables range from 1 (bad) to 7 (good) with exception of cost of imports which is shown in percentage terms_ The synthesis results of the full set of econometric specifications is presented in table 6 below, and the full regression results appear in Table 4 of the paper. We find that globalization at both city and country level (Kearney Globalization Index) is positively correlated with all the measures of governance. Globalization at a country level, as measured by the Kearney Globalization Index is significant and positive against all the governance indicators, except cost of imports where it is significant and negative. Per capita income is also positively correlated with all dimensions of governance.

The results shown in Table 6 are relatively robust and significant. With some specifications, country level globalization seems to dominate the city globalization variable. Partly this may be the fact that country level globalization in the more fundamental (macroeconomic and trade) sense vis-à-vis the rest of the world is even more important than city-specific measures or city specific attractiveness. Yet in part this result may also reflect the fact that the country globalization variable was constructed specifically for the purpose to have a globalization indicator, while the city globalization variables we are utilizing are indirect (and partial) proxies. Thus, the measurement errors may differ, with the city variables being more (statistically) ‘noisy’.

29
Notice the impact of the services sector is insignificant over the period. In the long run, the services sector has helped growth but, due to the limited time series that is available, the dynamism of the services sector is not captured over the period 2006 to 2011 where GVA data are available.

The average poverty elasticity of GVA growth across NUTS2 regions is only -0.19 and -0.11 from 2008 to 2010 during the crisis. The elasticity improved after the crisis from 2010 to 2011 at -0.68 (See Table 26). GVA excludes subsidies and taxes and this is one possible reason for why GVA does not exhibit poverty reducing impacts in the expected sectors: services and industry. Government subsidies are much higher in services and industry, therefore GVA is not a full measure of the size of the industrial and services sectors. Changes in the amount of government subsidy into reach region can bias the results and also generate sporadic trends.

Changes in employment have a much larger explanatory power on poverty reduction than growth in GVA. As Azevedo and Atamanov (2014) find, employment and job creation are the main drivers of poverty reduction. When examining regressions of an identical structure but using employment variables, there is a much stronger story on the importance of the services sector as a channel of poverty reduction.

Employment data are available for more years than the value-added series and can also be more informative of welfare changes since employment and wages result in direct poverty reduction effects. The services sector is the only sector that maintained positive employment growth every year from 2006 to 2013 (Figure 11). It is impressive that during the crisis period, employment did not decrease in aggregate. Only employment in industry declined from 2008 to 2009 but this was offset by growth in services and agricultural employment. Agriculture was beneficial in some regions as people fell back into agriculture during the financial crisis, which is why there is strong growth in agriculture from 2009-2010 and also from 2010-2011. However, as the country began its
2004 _b_ ), who define several standard quality indicators (based on electricity losses, percentage of paved roads, and telephone faults, given that they use stocks of electricity, roads, and

> 12In their estimation of demand functions for infrastructure services based on panel data, Fay and Yepes (2003, p. 8) found long-term elasticities of 0.375 for electricity, 0.5 for telephone mainlines, and 0.14 for paved roads. These estimates differ quite significantly from our values of unity. However, the Fay-Yepes estimates refer to low- and middle-income countries in general, so there is no indication that they are adequate for MENA countries in particular—or, for that matter, the three countries in our sample. Moreover, in their regressions, there is no price (or user cost) variable, so their estimated income elasticities may be biased.

17
analyze the results of the Monte Carlo simulations. First, there is the potential issue of misspecification of the econometric model, Equation (1). The econometric specification of mean income growth could be affected by omitted factors: global and country-specific components. Additionally, some of the factors affecting mean income growth might not respond to a linear form.

Second, the probability distribution assumptions may affect the variables used to predict the future behavior of the country mean income growth. Global growth and commodity prices could entail more complicated distributions with longer tails. In the same vein, the idiosyncratic error terms could have a long-tailed distribution or skewed parameterization instead of a normal distribution arrangement. Thus, our projections of mean income growth may not capture tail behaviors. In the same vein, given a lack of observations for the Gini coefficient across countries over the study period 1980–2014, the bounded random walk stochastic process assumption that models the logarithm of the Gini coefficient could entail a very restrictive specification.

Third, country-level statistics are based on household data that incorporate multiple sources of errors. For instance, collected household observations could include sampling and non-sampling measurement errors. Although the number of household surveys has expanded in countries around the world, the frequency and quality of the collected information vary greatly, which may cause problems related to the consistency and comparability of the data between and within countries (World Bank 2014, Chapter 5). Fourth, demographic trends that account for changes in the total population might have some degree of uncertainty, especially in terms of the materialization of a significant factor such as war, massive migration, or an epidemic (United Nations 2017).

# **VI. RESULTS**

This section presents our simulated outcomes of poverty and changes in the income distribution for the period 2015–2030. All our results are analyzed at the global level and by the country typologies introduced in Table 1. Subsections A and B discuss poverty headcount and relative income inequality estimates, respectively. In Subsection C, we analyze the speeds of
The surveys administered a household questionnaire, an individual questionnaire, an agriculture questionnaire, and a community questionnaire. The HP dataset, with its focus on agricultural production and productivity, includes variables predominantly from the agriculture, household, and individual questionnaires (see ‘Survey instruments’ below). The survey questionnaires were administered in faceto-face interviews and recorded as Computer Assisted Personal Interviews (CAPI) using the Survey Solutions platform in Ethiopia (waves 4 and 5), Malawi (waves 3 and 4), Mali (wave 2), Nigeria (waves 3 and 4), Tanzania (waves 3, 4 and 5) and Uganda (waves 7 and 8). In Uganda, CAPI software CWEST was used in wave 2, and SurveyBe in waves 3 to 5. In all other rounds of the surveys, Paper Assisted Personal Interviews (PAPI) or Computer Assisted Field Interviews (CAFE) were used.

Outside of the survey questionnaires, additional variables were recorded and are provided as part of the publicly available datasets. First, land areas of agricultural plots and/or parcels were in many cases measured using handheld GPS devices, since respondent-reported land areas were shown to be unreliable (Carletto et al., 2013). Furthermore, households, agricultural plots, and communities were georeferenced via GPS during the survey operations, which enables the integration of survey data with geo-spatial data.

Sampling, tracking, and survey weights The surveys supported by the LSMS-ISA are designed to be nationally representative of the country’s population of households. To achieve this, a stratified two-stage probability sampling approach is employed, with census Enumeration Areas (EAs) selected as primary sampling units with probability proportional to size. Survey strata consist of urban/rural levels and administrative areas. In each selected EA, all households are listed and then randomly selected from the complete list. Population and housing censuses are used as sampling frames. Once households have been sampled and interviewed, sampling weights are constructed and provided to data users to allow for the calculation of nationally
motivation is compensated by the cost increasing effect of women requiring higher reservation wages to work in a rural area.) Attracting more students from rural areas, would also reduce the required budget increase slightly, as reported in column 4 of Table 8.

# **6. Conclusion**

This paper analyzes the willingness to work in a rural area of final year nursing and medical students in Ethiopia. We use contingent valuation data, obtained from "payment cards" questions. We find that there are two main determinants of the willingness to work in a rural area: the income of the parents’ household and the students’ willingness to help the poor. The first result is in line with results from labor economics which show that income from other household members affects labor decisions (Mroz, 1987). The second result indicates that a health worker’s intrinsic motivation has a strong influence on her preference for a rural posting. We also find that medical students are less likely than nursing students to prefer to work in a rural area. The results are very robust: they are very similar when using OLS estimation or when using maximum likelihood estimation that uses the full information of the "payment card". The latter method also allows a direct estimate of the standard deviation, which appears to be constant across different specifications, a further sign of the robustness of the results.

Given the high significance of the variable, we attempt to further unravel who is motivated to help the poor. Behind it is the question whether intrinsic motivation is predetermined or whether it can be shaped - i.e. whether motivated health workers are a result of socialization or selection. If intrinsic motivation is an internalization of norms, as argued by Deci (1975) a central question for public policy is when internalization takes place: before they enter the profession or after. Our data are by no means rich enough to address this question in a conclusive way, but give some indication. The key result is that women are significantly more likely
reported to having done so for work reasons or explicitly to help the family. Table 1 shows that from the time of the first interview in 1993, fourteen years later more than one household out of four had at least one member that had migrated and not come back while, among individuals, migrants represented about 9%.

**Table 1.** Migration in the IFLS.

|Wave|Individuals Migrants %<br>Households|Migran~~t~~%<br>Households|
|---|---|---|

> 11 For a more detailed description of the dataset see Thomas et al. (2012).

11
sumption behavior across income groups. They derive a demand model based on an integrable quadratic logarithmic expenditure share system and show that this model fits UK data better than the Working-Leser Engel types of models, particulalry for selected commodities. Blundell, Chen and Kristensen (2007) later showed that behavior changes across different types of goods with some goods approaching a linear or quadratic shape while others having different forms. More recently, Lewbel and Pendakur (2009) proposed the Exact Affine Stone Index (EASI) implicit Marshallian demand system. In the words of the authors: _“In contrast to the AID system, the EASI demand system also allows for flexible interactions between prices and expenditures, permits almost any functional form for Engel curves, and allows error terms in the model to correspond to unobserved preference heterogeneity random utility parameters.” (p.29)_ . Recent empirical works that attempted to estimate demand systems directly from data in developing countries include Attanasio, Di Maro, Lechene, and Phillips (2013) and Osei-Asare and Eghan (2013).

With respect to demand systems, our contribution is to compare the behavior of different welfare measures using alternative demand systems including simple Cobb-Douglas (CD), Linear Expenditure System (LES), the Almost Ideal Demand System (AIDS), the Quadratic Almost Ideal Demand System (QUAIDS) and the Exact Affine Stone Index (EASI). The paper finds that the difference in welfare measurement is minimal as compared to changes in other parameters such as the price change or the budget share.

Results of this paper can be relevant for a wide set of issues empirical economists are confronted with. Changes in prices occur for a variety of reasons. They may be induced by global shocks as it was the case for the global rise in commodity prices during the first decade of the 2000s or the 2008 global financial crisis, or they may be due to domestic shocks such as those induced by variations in local climatic conditions. Price changes may also occur as a result of economic policies such as changes
*

<!-- Start of picture text --> 20000 2.5<br>18000 Dirty Patents Clean Patents Ratio: Clean vs Dirty<br>16000 2<br>14000<br>12000 1.5<br>10000<br>8000 1<br>6000<br>4000 0.5<br>2000<br>0 0<br>1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br><!-- End of picture text --> Source: Aghion, Dechezlepretre, Hemous, Martin and Van Reenen (2012), calculations based on the PATSTAT database.

4
that intergenerational mobility could be capturing the nonlinear relationship between inequality and growth.<sup>15</sup> The lack of robust results on the effect of inequality of growth using panel data may reflect not only issues of correct specification but also an insufficient way of dealing with endogeneity, particularly the use of weak instruments (Kraay 2015).

In sum, the mixed evidence of the impact of inequality on growth arises primarily based on whether the study used a cross-country approach (which includes between-country inequality) or a panel data approach (which includes only within-country variation over time). Given that some of the key mechanisms linking inequality to growth—such as institutional quality, credit constraints, and redistribution policies—do not change much over time, the influences of those channels are greater in the cross-country than the time series dimension. Given that channels such as political economy and credit constraints generate a negative impact of inequality on

> 15 The relationship between intergenerational mobility and growth is complex and may depend on inheritance laws and uncertainty of property rights. Cerra et al. (2021, Chapter 18) examines these issues in more detail.

27
- Cattaneo, M. (2010). “Efficient semiparametric estimation of multi-valued treatment effect under ignorability” _Journal of Econometrics_ , 155: 138-154.

- Cuevas, J. and Silveyra, M. (2018). “Reporte de implementación del Programa PODER”, Documento interno, Banco Mundial.

- De Brauw, A; and Hoddinott, J. (2011). “Must Conditional Cash Transfer Programs Be Conditioned to Be Effective? The Impact of Conditioning Transfers on School Enrollment in Mexico”, _Journal of Development Economics_ 96(2): 359-370.

- de Hoyos, R; Attanasio, O.; and Meghir, C. (2019) “Can scholarships increase high school graduation rates? Evidence from a randomized control trial in Mexico”, _World Bank Policy Research Working Paper_ No. 8826. World Bank, Washington, DC

- de Hoyos, R.; Estrada, R.; and Vargas, M. (2018), “Predicting Individual Wellbeing Through Test Scores : Evidence from a National Assessment in Mexico”, _World Bank Policy Research Working Paper_ No. 8459. No. 8826. World Bank, Washington, DC

- de Hoyos, R.; Rogers, H.; and Székely, M. (2016). “Out of School and Out of Work : Risk and Opportunities for Latin America’s Ninis”, World Bank, Washington, DC

- de Janvry, A.; Dustan, A.; and Sadoulet, E.; (2017). "Flourish or Fail?: The Risky Reward of Elite High School Admission in Mexico City," _Journal of Human Resources_ , University of Wisconsin Press, 52(3): 756-799.

- De Witte, K.; Cabus, S.; Thyssen, G.; Groot, W.; and van den Brink, H.M. (2013). “A Critical Review of the Literature on School Dropout”, _Educational Research Review_ 10: 13-28.

- Deininger, K. (
four language regions with a total population of about 8.5 million individuals.<sup>4</sup> Moreover, Switzerland is one of the three countries in the world with the largest share of foreign-born population: about 30 percent of the total population is foreign-born,<sup>5</sup> and about 38 percent of its permanent residents aged 15 or older have a migrant background.<sup>6</sup> The multicultural context in Switzerland goes along with a long humanitarian tradition. Table 1 shows the top 10 European countries in terms of number of refugees hosted. It is possible to observe that Switzerland, in spite of being a relatively small country (in terms of population), ranks number four in terms of share of hosted refugees. Only Sweden, Austria, and Germany host a higher share of refugees in the most recent year, 2019. This prominent role in hosting refugees is not a recent phenomenon. Figure 2 shows that over the past 60 years Switzerland has always been among the top four receiving countries, and before 1983 Switzerland ranked first in Europe in terms of share of hosted refugees. Switzerland has been part of an international process of convergence in sheltering refugees, resulting in new models of integration (Probst et al. 2019).

## **2.2 History of the Forced Displacement Context**

The Swiss Confederation in its current form adopted a federal constitution in 1848. Individual cantons have long had the tradition of hosting refugees, and even today play a key role. Piguet (2019) documents the history of asylum on the Swiss territory as going back to the Roman age when Scandinavians fled famine. During the Middle Ages cities in Switzerland followed the trend of most European cities hosting

> 3. World Bank Population Data on Population and Refugees: https://data.worldbank.org/. [Data accessed on April 17, 2021].

> 4. World Bank Population Data: https://data.worldbank.org
Ramsey, S., Stuart, G., Strong, D., and Brown, R. (2002). Evaluation of a behavioral measure of risk taking: The balloon analogue risk task (bart). _Journal of experimental psychology. Applied_ , 8:75–84.

- Levine, P. B. and Zimmerman, D. J. (2010). _Targeting investments in children: Fighting poverty when resources are limited_ . University of Chicago Press.

- Linley, P. A., Maltby, J., Wood, A. M., Joseph, S., Harrington, S., Peterson, C., Park, N., and Seligman, M. E. (2007). Character strengths in the united kingdom: The via inventory of strengths. _Personality and individual differences_ , 43(2):341–351.

- Lira, B., O’Brien, J. M., Peña, P. A., Galla, B. M., D’Mello, S., Yeager, D. S., Defnet, A., Kautz, T., Munkacsy, K., and Duckworth, A. L. (2022). Large studies reveal how reference bias limits policy applications of self-report measures. _Scientific Reports_ , 12(1):19189.

42
# **Survey Questions**

Ability proxy from the World Value Surveys: educational attainment What is the highest education level that you have attained?

1. No formal education or incomplete primary school

2. Complete primary school 3. Incomplete secondary school: technical/vocational type 4. Complete secondary school: technical/vocational type 5. Incomplete secondary: university-preparatory type 6. Complete secondary: university-preparatory type 7. Some university-level education, without degree 8. University-level education, with degree Morality proxy from the World Value Surveys: important aspects of jobs Now I would like to ask you something about the things which would seem to you, personally, most important if you were looking for a job. Here are some of the things many people take into account in relation to their work. Regardless of whether you’re actually looking for a job, which one would you, personally, place first if you were looking for a job?

1. A good income so that you do not have any worries about money

2. A safe job with no risk of closing down or unemployment 3. Working with people you like 4. Doing an important job which gives you a feeling of accomplishment 43