rafmacalaba/data-use-mentions-extended huggingface dataset:
clearly defining objectives (e.g. selecting the poorest, or any poor, or the food-insecure). We find that when measured consistently in comparable contexts, the variation in the performance of methods is relatively small. This result is different from Coady et al. (2004) and Devereux et al. (2017) whose reviews include countries that differ greatly in several ways. Finally, our results highlight how targeting studies based on simulations can be misleading. First, simulations are conducted on nationally representative surveys while most interventions are implemented on largely different samples, after geographical targeting has been applied. Second, program coverage rates often differ from the simulations conducted, having dramatic implications on the performance of targeting. The actual targeting performance is also affected by implementation issues.

Overall, our findings suggest that after geographical targeting is applied, the other targeting method employed is not likely to make a major difference in reducing poverty or food insecurity in low-income settings. That being said, targeting costs are also similar across methods and represent a small share of the total amount of funds distributed. Our findings imply that when making choices on targeting methods (or whether to target or not), other parameters – not considered in our paper because of data limitations - may be more important than targeting errors and costs. For instance, the legitimacy of targeting schemes, the program spillovers that targeting may generate on non-beneficiaries, or how targeting choices affect the program impact on final outcomes deserve greater attention.

Our study contributes in several ways to the literature. As far as we know, this is the first cross-country comparison of targeting performance based on a primary data analysis from actual programs. As such, it differs from cross-country analyses based on simulations (e.g. Brown et al., 2018), which focus on PMT performance but cannot assess CBT or measure actual performance in practice. Our study also differs from cross-country reviews that are based on actual programs, but not based on primary, micro-level data analysis (Coady et al.
.org/content/dam/Worldbank/poverty/GDSP_Methodology_Sep2015%20(2).doc x.

Mitra, S. R. Katayama, and Yoshida, N. (2010). “A Short Note on International Poverty Estimation,” mimeo.

Narayan, A., Saavedra‐Chanduvi J. & Tiwari, S. (2013). “Shared Prosperity: Links to Growth, Inequality and Inequality of Opportunity”. Policy Research Working Paper 6649. The World Bank.

PovcalNet (2015). PovcalNet: an online analysis tool for global poverty monitoring, available at: http://iresearch.worldbank.org/PovcalNet/ Ravallion, M. (1992). "Poverty Comparisons ‐ A Guide to Concepts and Methods," Papers 88, World Bank ‐ Living Standards Measurement.

Ravallion, M. (1996). "Issues in Measuring and Modelling Poverty," _Economic Journal_ , Royal Economic Society, vol. 106(438): 1328‐43.

Serajuddin, U., Uematsu, H., Wieser, C., Yoshida, N., and Dabalen, A. (2015). “Data Deprivation: another deprivation to end“. Policy Research Working Paper 7252. The World Bank.

World Bank. (2008). Poverty data: A supplement to the World Development Indicators, 2008. World Bank.

World Bank. (2015a). _A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals_ . Policy Research Report. Washington, DC: World Bank. doi:10.1596/978‐1‐4648‐ 0361‐1.

World Bank Group. (2015b). _Global Monitoring Report 2014/2015: Ending Poverty and Sharing Prosperity._ Washington, DC: World Bank. doi:10.1596/978‐1‐4648‐0336‐9.

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..............................................................|2.2<br>........................................................|2.2<br>2.2<br>..........................................................<br>....................................................................|
|Yorkshire|2.1|2.1<br>1.7|
|Northem|2.3|2.4<br>16|
|Industry Average|**2.2**|. 1.9<br>**4.5**|

Source: P. _Burns_ and T.G. Weyman-Jones (1994) _TILe Performnance of the Electricity Distribution Business in England and Wales, 19711993. CRI_ Discussion paper No. 8. Cipfa: UK

# 4. Generating information while designing a concession

In addition to ensuring that it meets the main fiscal
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)|(0.016)|(0.014)|(0.014)|
|Severe|0.231|0.132|0.235|0.338|0.052|
||(0.008)|(0.012)|(0.013)|(0.014)|(0.005)|
|Observations|8,713|3,051|1,646|1,820|2,196|

_Note_ : Each column in Panel A reports the estimated total number of adults who are moderately or severely food insecure, or just severely food insecure. Each column in Panel B reports the average incidence of (i) moderate or severe food insecurity and (ii) severe food insecurity among the adult population. In both panels, food insecurity is measured using the FIES. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia, round 2 in Malawi, Nigeria, and Uganda. Standard errors on the estimates are in parenthesis.

41
# **4 Discussion of opportunities**

We summarize in Table 3 the opportunities identified, the challenge to which they relate and whether they may help improve inter-personal comparisons (Objective 1) or crossregions comparisons (Objective 2).<sup>74</sup>

**Table 3:** Improvement opportunities for multidimensional poverty measures.

<!-- Start of picture text --> Challenges Opportunities Objectives<br>Waste less 3.2 Do not needlessly dichotomize variables<br>available data (trichotomize categorical variables) 1 & 2<br>(PX identification for consumption) 1 & 2<br>Cover more dimensions through hybrid index<br>3.3 Synthetic integration of mortality-longevity only 2<br>3.4 Assume missing joint-distribution M vs OD<br>3.4.1 Assume overlap between HM and HOD only 2<br>3.4.2 Impute consumption into OD survey 1 & 2<br><!-- End of picture text -->

|Challenges|Opportunities|Objectives|
|---|---|---|
|Waste less<br>available data|3.2 Do not needlessly dichotomize variables<br>(trichotomize categorical variables)<br>(PX identifcation for consumption)<br>Cover more dimensions through hybrid index<br>3.3 Synthetic integration of mortality-longevity<br>3.4 Assume missing joint-distribution M vs OD<br>3.4.1 Assume overlap between HM and HOD<br>3.4.2 Impute consumption into OD survey<br>3.4.3 Total consumption|1 & 2<br>1 & 2<br>only 2<br>only 2<br>1 & 2<br>1 & 2|
|Better “flter”<br>available data|3.5 Use most fned-grained unit of analysis<br>(take advantage of individual-level data)<br
the government, because state-owned land is perceived as a “free” resource, at least in the short run. The issue one may face here is that so much land is publicly held and allocated that it is not clear what the “market” price should be.

- <u>Direct subsidies through housing finance instruments. Frequently, instruments of housing</u> finance such as Housing Contract Savings Schemes (HCSS) serve as vehicles for housing subsidies, or have been subsidized to ensure their success.<sup>33</sup> This paper is not directly concerned with housing finance; however, the subsidies or premiums given to households should be included in the total amount of housing subsidies. The availability of figures in that case will vary by the type of instrument, but can be expected to be high, since those subsidies are typically budgeted.

- <u>Indirect subsidies through finance instruments. This includes the value of State</u> commitments on instruments such as mortgage insurance schemes, liquidity facilities, hedging instruments for mortgage lenders, etc. Pricing those products (which in the case of guarantee schemes amounts to estimating the contingent liabilities faced by the State) is possible in theory, and is effectively done under private schemes. Historically, however, this type of instrument is initially introduced by the State, and subsequently taken over by the private sector. At the beginning of the system, calculation may not be available due to lack of adequate historical data. An example of how data on mortgages can be used to estimate the cost of a subsidy program in the form of low-LTV mortgages at subsidized rates is given in Deng et al. (1996).

# <u>4) Limits of the Method</u>

The usual limits applying to economic analysis of particular programs, independently of the rest of the economy, also affect the analysis of housing subsidies. Interventions or distortions, operating both inside and outside the housing market, can influence the efficiency of subsidy programs
Figure 1 motivating this paper clearly shows. Developing countries with the notable exception of Vietnam are clustered at the bottom left hand side. And there is a positive slope with high scoring countries tending to be on the higher income side. In this section, we see that the effort made by the Vietnamese government to invest in education plays an important part in explaining the achievement gap. Even though Vietnam may be poor with regard to per capita income, it is not as poor with regard to the quality of educational resources and the quality of physical infrastructure. This can be seen in Figure 4 comparing the average PISA 2012 mathematics scores with a school infrastructure quality index. Compared to Figure 1, where Vietnam is the country with the lowest GDP per capita in the PISA 2012 data set and thus placed in the left hand side of the figure, Vietnam moves more towards the middle in Figure 4. Using SCMATBUI (quality of school infrastructure), Vietnam jumps ahead 11 places, with a similar story (not shown) regarding SCMATEDU (quality of educational materials). A key reason is the investments by the Vietnamese government in schools in smaller towns and rural areas as classified by PISA, given that the dispersion of school infrastructure is lower in Vietnam compared to other countries.

27
_ESTIMATING FOOD PRICE INFLATION_ _13_ Particularly within the matrix _X_ there can be highly correlated predictors, while the matrix _G_ may contain multiple identical indicators, for instance if there is only one market within an administrative unit. The following hard-coded variable selection rules are used. First, linear combinations are removed to avoid dummy problems. Highly collinear variables with _> ._ 95 correlation are removed by iteratively recalculating the correlation matrix and removing the variable with the highest overall correlation. Finally, variables with near-zero variance are removed.<sup>17</sup> All predictors are centered and scaled.

Note that equation 4 is estimated using an elastic net model that uses _L_ 1 and _L_ 2 penalties to shrink the predictor space, see again (Friedman et al., 2010; Hastie et al., 2021). The cubist regression specification of equation 5 is essentially an observation specific counterpart of the linear model. As such, only the regional dummies are removed. The cubist model can partition the data and fit local regressions. Since the coordinates are supplied, the model can learn spatial fixed effects using adaptive neighborhood sizes as well as spatial interaction effects by partitioning by coordinates rather than relying on explicit spatial dummies, see again (Quinlan, 1992; Kuhn et al., 2012; Witten et al., 2016).

# _D. Validation of imputation accuracy_

There are no countries in the data where the food price data is complete and the true inflation of a basket of food items is fully known. This makes validation against true data difficult.<sup>18</sup> Cross-validation techniques are used to adjust the flexibility and assess the predictive accuracy of the models in each iteration. The elastic net model optimizes over the standard _L_ 1 and _L_ 2 penalties and the mixing parameter.<sup>19</sup> The cubist model tunes over the neighborhood size used for smoothing, and the boosting iterations, using grid of all _Neighborhood × Committees_ = (2 _,_ 4 _,_
of income inequality driven by unequal opportunities. Given that the IEO-R data for Africa in our sample is based on coarser partitions than in most other cases, one really should not read too much into this correlation. Nevertheless, it equally cannot be taken for granted that the IEO-R and the part of the HOI which seeks to capture inequality of opportunity are measuring the same things.

# **5. Concluding remarks**

Inequality of opportunity is a complex concept that can be measured in a number of different ways. A number of measures have recently been proposed, both under the ex-ante and the ex-post approaches, or indeed seeking a compromise between them. But most of these approaches have been applied to a single country or a very small group of countries, making cross-country comparisons impossible. Two exceptions are ex-ante measures of inequality of economic opportunity (IEO), and the Human Opportunity Index (HOI). Our review of this empirical literature yielded (roughly) comparable measures of the IEO for forty-one countries, and of the HOI for thirty-nine. Most countries in the first set are in Europe and Latin America, but there are examples from North America, Asia, Africa and the Middle-East. The second set covers countries in Africa and Latin America exclusively, and the overlap between the two samples is ten countries.

The evidence reviewed suggests that an important portion of income inequality observed in the world today cannot be attributed to differences in individual efforts or responsibility. On the contrary, it can be directly ascribed to exogenous factors such as family background, gender, race, place of birth, etc. There was considerable cross-country variation in the (lower-bound) relative measure of inequality of economic opportunity: Brazil’s share (0.32) is sixteen times as large as Norway’s. Although there certainly is noise in these measures, and various comparability caveats, there appears to be some signal as well.

16
data sets were created using all information sources available at CPC, GTS daily reports and CPC special collections (Chen et al., 2008). Extensive pre-processing and cleaning of gauge data includes comparison with contemporaneous data from satellite and other sources. Precipitation data are gridded at 0 _._ 5<sup>_◦_</sup> resolution using an Optimal Interpolation (OI) technique, which accounts for orographic effects. The global temperature data set is gridded at the same resolution using the Shepard Algorithm. A series of papers use CPC data, including Tennant and Gilmore (2020) and Williams and Travis (2019).

# **2.2 Household Survey Data**

The World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMSISA) is a household survey program that provides financial and technical assistance to national statistical offices in Sub-Saharan Africa for the design and implementation of national, multitopic longitudinal household surveys with a focus on agriculture. As detailed below, the analysis leverages data from several rounds of panel household surveys conducted over the last decade by the respective national statistical office in Ethiopia, Malawi, Niger, Nigeria, Uganda and Tanzania, with support from the LSMS-ISA initiative.<sup>6</sup> Table 2 provides a summary of the countries, years, and observations used in the analysis. Appendix B provides greater details on each country’s sampling frame and data collection process.

In Ethiopia, we use the data from the 2011/12, 2013/14 and 2015/16 rounds of the Ethiopia Socioeconomic Survey (ESS), which has been conducted by the Central Statistical Agency of Ethiopia (CSA, 2014; CSA, 2015; CSA, 2017). The Wave 1 data is representative at the regional level for the most populous regions in the country while Wave 2 and 3 expanded to include 1,500 households in urban areas. After data cleaning to remove urban and non-agricultural rural households, we are left with 7,272 household observations across three survey
, 1997–2006.” _Journal of Comparative Family Studies_ , 40(4), pp. 545–560.

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- the South _. Feminist Economics,_ 14(3), pp. 107-152.

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23
in the labor market, as in Becker (1993). In the context of South Asia, this paper answers two questions: (i) how can the marriage penalty be separated from the child penalty in the absence of individual-level panel data, and (ii) what drives the marriage penalty in South Asia. Does it represent optimal labor supply or a misallocation of talent?

To answer these questions, we use multiple rounds of the nationally representative – Demographic and Health Surveys (DHS) from four South Asian countries Bangladesh, India, Maldives, and Nepal. Separately estimating child and marriage penalties presents several challenges. First, since the timing of these major life events is endogenous, naive comparisons in the labor market outcomes between married and unmarried women or parents and the childless are contaminated by omitted variables. Recent work on child penalties has used event-study methods to solve this identification problem (Kleven et al., 2021, 2019, 2024).

However, even if a design exploits sharp changes in labor supply around marriage or childbirth for identification, these two events are correlated in time for a given individual. As such, estimates of the marriage penalty are obscured by the presence of children, and estimates of the child penalty are likewise capturing at least in part a marriage penalty. One solution is to use individual-level panel data, where a woman’s employment status is observed before and after both marriage and childbirth. Here, event-study techniques readily apply, augmenting standard specifications with timing indicators for both events simultaneously, with marriage and childbirth coefficients separately identified by variation across women in the timing gap between these two events (Kleven et al., 2023).

However, access to such rich panel data is rare, particularly in developing country settings. We propose a method that allows for the separation of the marriage penalty from the child penalty in repeated cross-sectional data. Following Kleven et al. (2023), we generate pseudo-panels by matching women surveyed in the year of their marriage with younger unmarried women and older married
# **Conclusion**

Azerbaijan is a highly educated country, at least in terms of years of schooling and levels of education completed. There is still a need to improve the quality of education in terms of student learning outcomes. In 2015, the average level of schooling was 12.8 years. Very few 15-64-yearolds have no schooling. In fact, 69 percent of the labor force has a secondary school education. Only 10 percent have only primary, while 20 percent have tertiary education. The enrollment rate for tertiary is low. Sizable differences in earnings only appear at the tertiary level. Women have more schooling than men, at 13.2 versus 12.6 years of schooling.

We use the 2015 Azerbaijan Monitoring Survey for Social Welfare to estimate the returns to education. This is the first estimate of returns to schooling in Azerbaijan since 1995. This results in a private rate of return of 6 percent. This is low compared to the global average of 8-10 percent, and it is low for the Europe and Central Asia region. The returns to schooling for men are 6 percent and 8 percent for women, even controlling for selection; this is in line with global findings. For higher education, the rate of return is 9 percent; 9 percent for males and 11 percent for females. Only returns at the tertiary level are significant given the fact that almost everyone in the labor force has secondary education. Returns to schooling are highest in the manufacturing sector, at 8 percent; followed by services at 5 percent; and agriculture and other at 3 percent. Overall, the returns to education in Azerbaijan are low, and have not changed much in the last 20 years.

Using the full discounting method, the private return to tertiary education is estimated at 9 percent and the social rate of return is 8 percent. There are two types of students attending higher education institutions in Azerbaijan, those who receive state scholarships and those who pay tuition, even for 18