rafmacalaba/datause-encoder-data huggingface dataset:
percent. Although these are ambitious increases, it should be noted that many NMSs achieved comparable annual increases in the rates of tertiary education completion between 2000 and 2011.

**Table 4:  Tertiary education completion rates among 30- to 34-year-olds in 2008 and national targets for 2020 (percent)**

||Tertiary education as a<br>percentage of people between<br>the agegroup30-34|National Target||
|---|---|---|---|
|Bulgaria|23.8||36|
|Czech Rep|15.9||32|
|Estonia|34.3||40|
|Hungary|23.6||30|
|Latvia|27.7||36|
|Lithuania|45.2||40|
|Poland|31.5||45|
|Romania|16.8||26|
|Slovakia|24.1||40|
|Slovenia|27.3||40|

_Source:_ World Bank staff calculations using EU-SILC data.

# **4. Methodology**

## **_Simulating at-risk-of-poverty rates and anchored poverty_**

To simulate the 2020 at-risk-of-poverty and anchored poverty indicators, we first need to simulate the 2020 household income distribution. Household incomes (Yh) are the sum of labor incomes (YL), government and private transfers (YT), and other sources of income (YO): In EU countries, labor incomes constitute the majority of a household’s income. The transfer income (which is most often pensions) is assumed to remain constant.<sup>10</sup> However, transfers that are tied to employment status, such as unemployment benefits or social assistance benefits, are allowed to vary as needed in the simulations to reflect simulated changes in the employment status of individuals.

A household’s labor income is the sum of labor income earned by each working household member ( Yh = ∑yi i ∀ i∈h) . We assume that the labor income of individual i is determined by individual i′s characteristics conditional on the individual’s employment status: 10 In a separate policy modeling exercise, we explore the impacts on household income distribution of different policy scenarios regarding transfers, such as reducing income transfers by a certain percentage.
Similar to the energy sector, the transport industry is vulnerable to both climate-related conditions and the risks associated with climate transition – think economywide carbon pricing, climate-smart transport policies, new transportation solutions, among others. As one of the leading contributors to carbon emissions, responsible for 24% of CO2 emissions from fuel combustion on a global scale, the transport sector is expected to undergo a significant transformation (IEA, 2020). These long term and far reaching effects are often overlooked in analysis, but are crucial for shaping effective policies. Section 3.3.1 discusses examples of government response, including carbon tax and energy pricing, and their impact on employment.

# **2.3. Labor supply and time allocation**

Changes in climate may affect labor supply and time use in various ways. First, weather conditions may encourage worker absenteeism. In India, a rise in the 10-day average temperature by 1<sup>_◦_</sup> C WBGT<sup>xxi</sup> increased the probability of absenteeism among steel workers by 2% and among garment workers by 10%, but not for piece rate workers in cloth-weaving factories (Somanathan et al., 2021). Workers in Chinese manufacturing, however, are not more absent on hotter days (Cai et al., 2018).<sup>xxii</sup> Weather conditions may also encourage shirking. The quality of reported data among Demographic and Health Survey (DHS) interviewers declined significantly on hot days, with more missing responses per interview on average, while the total number of interviews conducted per interviewer remained unchanged (LoPalo, 2023).<sup>xxiii</sup> Second, workers may adjust the distribution of work time in response to weather shocks. In one example work time is reallocated from farm to off-farm work. Indian households that experienced a persistent annual decline in farm income due to drying up wells (caused by climate change) are 12 percentage points more likely to derive income from off-farm employment. These added earnings compensate for income lost, leaving total household revenue unaffected (Blakeslee et al., 2020). Neither work related migration nor employment in nearby villages, nor changes in agricultural practices, played a similarly large role in 12
Barro, R., and J. Lee. 1994. “Sources of Economic Growth.” _Carnegie-Rochester Conference Series on Public Policy_ 40 (1): 1-46.

Barro, R. 1996. “Determinants of Economic Growth: A Cross-Country Empirical Study.” NBER Working Papers 6317, National Bureau of Economic Research.

Barro, R., and R. McCleary. 2003. "Religion and Economic Growth.” NBER Working Papers 9682, National Bureau of Economic Research, Inc.

Beaman, L., R. Chattopadhyay, E. Duflo, R. Pande, and P. Topolova. 2009. “Powerful Women: Does Exposure Reduce Bias?” _Quarterly Journal of Economics_ 124 (4): 1497-540.

Bertocchi, G. 2008. "The Enfranchisement of Women and the Welfare State." Center for Economic Research (RECent) 018, University of Modena and Reggio E., Dept. of Economics.

Bertrand, M. 2010. “New Perspectives on Gender.” In _Handbook of Labor Economics_ , ed. by Orley Ashenfelter and David Card, chapter 17. Amsterdam: North Holland Press.

Bils, M., and P. Klenow. 2000. "Does Schooling Cause Growth?" _American Economic Review_ 90 (5): 1160–83.

Branisa, B., S. Klasen, and M. Ziegler. 2009. "Why we should all care about social institutions related to gender inequality." Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 15, Courant Research Centre PEG.

Caselli, F., G. Esquivel, and F. Lefort, 1996. "Reopening the Convergence Debate: A New Look at Cross-Country Growth Empirics." _Journal of Economic Growth_ 1 (3): 363-8.

Cavalcanti, T.S. Parente, and R. Zhao, 2007. "Religion in Macroeconomics: A Quantitative Analysis of Weber’s Thesis." _Economic Theory_ 32 (1): 105-23.

Cavalcanti, T., and J. Tavares. 2007. "The Output Cost of Gender Discrimination: A ModelBased Macroeconomic Estimate." CEPR Discussion Papers 6477, C.E.P.R. Discussion Papers.

Doepke, M., and M. Tertilt. 2010. “Does Female Empowerment Promote Economic Development?” BREAD Working Paper 281, Bureau for Research and Economic Analysis of Development.

19
true panel estimates reasonably well. In particular, all the point estimates based on the synthetic panels fall within the 95 percent confidence intervals (CIs) of those based on the true panels, and even fall within one standard error of the latter in one case. That is, the unconditional probability estimate for those who remain poor in both years is 10.8 percent, which is almost identical to the corresponding estimate based on the true panel of 10.7 percent. Second, all the 95 percent CIs of the synthetic panel estimates overlap with those of the true panels for at least half or more. This is due to the fact that the synthetic panel estimates are model-based estimates, thus these estimates generally should have smaller standard errors than those using the actual panels (which are usually referred to in the survey statistics literature as the design-based estimates). Finally, estimates for the conditional probabilities are somewhat less accurate than those for the unconditional probabilities since obtaining the latter entails prediction for both the numerator and the denominator of (12)-type quantities.<sup>33</sup>

# **VI. Other Topics**

We briefly review some other issues that are related to poverty measurements that are increasingly receiving attention.

# _Measurement Errors_

Measurement errors are known to be a common issue with micro survey data, and can bias poverty estimates (see, e.g., Deaton, 1997: Chesher and Schluter, 2003). Particularly with lowquality panel data sets, these errors may result in spurious movements that can be incorrectly attributed to poverty mobility. While some researchers argue that up to 50 percent of the transitory poverty may be accounted for by measurement error in income or consumption (Dercon and Krishnan, 2000; Glewwe, 2012; Lee _et al._ , 2017), others suggest the magnitudes of bias rely on

> 33 This is also consistent with the theoretical and empirical evidence provided in Dang and Lanjouw (2013).

33
- 26 -

Table 6: Mean Percentage of Instructional Time Devoted to Selected Subjects in the Primary School Curricula by Per Capita Income Level of Country

|||Develop|ing Countries||Developed|Countries|
|---|---|---|---|---|---|---|
|<br>I|Low-<br>ncome|Lower<br>Middle-<br>Income|Upper<br>Middle-<br>Income|High<br>Income|Market<br>Economies|Non-Market<br>Economies|
|(|N=25)|(N=38)|(N=22)|(N=5)|(N=17)|(N=7)|
|Comb.Lang.|36.5|33.5|35.9|35.3|32.6|37.6|
|Nat.Lang.|17.8|16.7|23.4|29.7|30.5|32.6|
|Loc.Lang.|1.3|1.1|1.1|0.0|0.1|0.0|
|<br>For.Lang.|2.0|1.0|2.2|5.7|1.6|_5.0_|
|Off.Lang.|15.5|14.3|5.6|0.0|0.2|0.0|
|Mathematics|18.2|17.3|18.3|16.1|18.9|20.3|
|Science|7.4|8.5|8.0|6.2|7.2|7.0|
|Comb.Soc.Science|7.9|10.1|8.5|4.7|9.7|6.6|
|Social Studies|3.5|6.7|4.4|4.0|5.8|0.0|
|History|1.4|1.3|_1.5_|0.3|1.1|3.5|
|<br>Geography|1.6|1.0|1.3|0.4|1.3|2.7|
|Civics|1.4|1.1|0.7|0.0|0.9|0.3|
|Comb.Moral/Rel.|5.3|5.6|4.3|19.0|4.1|0.0|
|Religion|4.0|4.3|2.8|17.1|3.5|0.0|
|<br>Moral Education|1.3|1.3|0.9|0.0|0.5|0.0|
|Aesthetic Ed.|8.9|7.7|10.7|6.8|14.1|10.5|
|Music|3.6|3.3|4.2|2.0|5.2|5.2|
|Art|5.3|4.3|6.4|4.8|8.9|5.3|
|Physical Ed.|6.8|_5.5_|6.6|7.5|9.8|9.8|
|Hygiene|1.1|1.9|1.8|2.6|0.7|0.3|
|Comb.Voc.Ed|6.0|7.4|3.1|2.8|0.8|6.5|
|Manual Training|3.0|2.9|2.1|1.8|0.3|6.0|
|<br>Agriculture|1.5|2.0|0.4|0.0|0.2|0.3|
|<br>Domestic Science|1.4|2.0|0.4|1.0|0.3|0.1|
|Vocational Ed.|0.0|_0.5_|0.0|0.0|0.0|0.0|
|Business|0.1|0.0|0.0|0.1|0.0|0.0|
|Other|2.8|2.6|1.9|0.8|1.8|1.5|
**Table 3: Cross-country variability in access to infrastructure – 2012**

|Panel A: Sha|re of varia|bilityex|plained by|country <br>|dummies onl<br>|y (R2)<br>|<sup>Washin</sup>||Landline|Cell|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||pcGDP|Water|Toilet|Sewers|Electricity|Refrigerator|<sup>g</sup><br>machine|TV|<br>phone|<br>phone|Computer|Internet|<br>Car|Motorcycl|
|||0.669|0.395|0.657|0.334|0.542|0.662|0.643|0.157|0.399|0.246|0.184|0.099|0.852|
|Panel B: Cou<br>|ntry premi|um relat|ive toHo|nduras- 2|012||||||||||
|Argentina|-|0.103|0.277|0.238|||||||||||
|Bolivia|5 793|-0.0879|0.0405|0.122|0.0121|-0.176|-0.536|0.00895|-0.105|-0.0152|0.0540|-0.109|-0.0679|<br>0.0536|
|Brazil|14 970|0.0467|0.108|0.104|0.0890|0.234|-0.191|0.134|-0.0198|-0.00150|0.154|0.142|||
|Chile|20 266|0.0370|0.267|0.320|0.0853|0.173|0.0695|-0.390|-0.0719|-0.0597|0.149|0.0727|0.0144||
|Colombia|11 840|0.0386|0.304|0.370|0.0817|0.101|-0.141|0.105|0.00660|0.0396|0.113|0.0728|-0.106|0.161|
|Costa Rica|13 589|0.0894|0.321|-0.318|0.0848|0.192||0.113|0.103|0.00574|0.0854|0.148|0.0405|0.0494|
|Rep.Dom|10 322|-0.191|0.0151|-0.251|0.0670|0.00877|0.0142|0.0150|-0.139|-0.144|-0.0858||-0.0559|<br>0.260|
|Ecuador|7 718|0.00373|0.293|0.151|0.0637|0.0582|-0.348|0.0357|0.0386|-0.0792|0.0386|-0.0169|-0.0257|<br>0.0117|
|Mexico|16 136|0.0189|0.0291|0.0833|0.0834|0.0967|-0.0577|0.0773|0.0131|-0.165|0.0123|-0.0183|0.0227|-0.0102|
|Paraguay|10 851|-0.0410|0.135|-0.340|0.0813|0.132||0.0798|-0.141|-0.00581|-0.00253|-0.0009|0.0456|0.410|
|Peru|7 505|-0.119|0.0877|0.140|-0.0157|-0.280|-0.509|-0.0372|-0.138|-0.110|-0.00785|-0.0716|-0.156|0.0536|
|El Salvador|11 376|-0.409|0.211|0.0652|0.0390|-0.0509|-0.522|0.0194|-0.0730|0.00916|-0.0502|-0.0353|-0.0488|<br>-0.0391|
|Uruguay|18 439|0.0875|0.236|0.0327|0.0870|0.223|-0.0450|0.121||-0.128|0.330|0.119|0.00391|<br>0.289|

Note: Honduras is taken as reference country for having the lowest pc GDP in 2012 ($4433). Argentinean pc GDP is unavailable for any year close to 2012.

28
# **Appendix 1: Summary statistics (weighted)**

|**Variable**|**mean**|**sd**|
|---|---|---|
|Log expenditure per capita (Tajik somoni)|5.0|0.54|
|_Household Demographics_|||
|Female-headed household|0.19||
|Age of household head<br>|50.6<br>|13.74<br>|
|Household size|6.3|2.80|
|Number of children (<15)|2.2|1.69|
|Number of older adults (65+)|0.3|0.57|
|Number of migrants|0.3|0.68|
|_Ethnicity_|||
|Tajik|0.79||
|Uzbek|0.18||
|Russian<br>|0.01<br>||
|Other|0.02||
|_Education of household head_|||
|No education|0.04||
|Primary (grades 1-4)|0.07||
|Basic (grades 1-8)|0.12||
|Secondary general (grades 9-10)|0.34||
|Secondary special|0.13||
|Secondary technical|0.11||
|Higher education|0.19||
|Graduate school/_aspirantura_|0.002||
|_Employment characteristics of Household, Head’s_|_occupation_||
|Number of employed|1.76|1.33|
|Not employed|0.37||
|Agriculture, fishing and forestry|0.18||
|Manufacture and mining|0.04||
|Services (electricity, gas, hot water, etc.)|0.02||
|Construction|0.07||
|Public administration, education, health|0.13||
|Sales and services|0.16||
|Other|0.03||
|_Agriculture_|||
|No land holdings|0.37||
|<br>Small holding (1-10 acres)|0.29||
|Medium holding (11-20 acres)|0.15||
|Large holding (21+ acres)|0.20||
|Anylivestock, poultry, beehives,fishetc.|0.55||

28
All data collection is done by GISSE a research institute in Bamako. The anonymized unit record data of the baseline and the monthly surveys can be downloaded from www.gisse.org. The response rate for the phone interviews has been very high (Table 1): after 6 rounds of monthly interviews the original sample is almost entirely intact. The low level of attrition demonstrates that mobile phone samples can be maintained over prolonged periods without being unduly affected by (non-random) respondent drop-out.

# **3. Characteristics of the Displaced and Returnee Population**

According to the 2009 population census, the two most sizeable ethnic groups in northern Mali are the Songhai (45%) and Kel Tamasheq (32%) --see Table 2. The crisis brought about an ethnic divide, which is reflected in the composition of the three sub-samples. The majority of IDPs and returnees are Songhai (75% and 71% respectively), while the majority of refugees are Kel Tamasheq. Results suggest that the decision of where to flee was determined by ethnicity: Kel Tamasheq and Arabs left the country; Songhai fled towards Bamako.

**Table 2: Ethnic composition of IDPs, refugees, returnees in the North**

|**Ethnicity**|**IDPs in**<br>**Bamako**<br>**(%)**|**Refugees**<br>**Niger**<br>**(%)**|**Refugees**<br>**Mauritania**<br>**(%)**|**Returnees**<br>**(%)**|**Total I+R+R**<br>**(%)**|**Ethnic**<br>**composition of**<br>**the North (%)**|
|---|---|---|---|---|---|---|
|Songhai|75|21|-|71|43|45|
|Kel Tamasheq|12|56|69|12|38|32|
|Arab|3|-|28|4|11|3|
|Peulh|4|21|-|6|4|7|
|Other|6|11|3|7|4|12|
|**Total (%)**|**100**|**100**|**100**|**100**|**100**|**100**|
|**Total (n)**|**100**|**81**|**100**|**220**|**501**|**1,268,009**|

Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census.

The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94% of returnees were displaced within Mali. Only 6% returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5% of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2% lived in Gao town and 1% in Kidal town. The remaining 92% lived in 27 different towns and villages in northern Mali, locations not covered by the survey.

7
Pradhan, Menno & Martin Ravallion. 2000. “Measuring Poverty Using Qualitative Perceptions of Consumption Adequacy.” Review of Economics and Statistics 82(3):462—71.

Ravallion, Martin & Jyotsna Jalan. 1996. “Growth Divergence Due to Spatial Externalities.” Economic Letters 53(2):227—32.

Ravallion, Martin & Jyotsna Jalan. 1999. “China’s Lagging Areas.” Americal Economic Review 89(2):301—05.

Ravallion, Martin & Quintin Wodon. 1999. “Poor Areas or Only Poor People?” Journal of Regional Science 39(4):689—711.

Rivers, D. & Q.H. Vuong. 1988. “Limited Information Estimators and Exogeneity Tests for Simultaneous Probit Models.” Journal of Econometrics 39:347—66.

Roback, Jennifer. 1982. “Wages, Rents, and the Quality of Life.” Journal of Political Economy 90(6):1257—78.

Rosen, Sherwin. 1979. Wages-based Indexes of Urban Quality of Life. In Current Issues in Urban Economics. Baltimore: Peter Mieszkowski and Mahlon Straszheim (eds.), Johns Hopkins University Press.

- Roy, Andrew D. 1951. “Some Thoughts on the Distribution of Earnings.” Oxford Economic Papers 3:135—46.

Seddon, David, Jagannath Adhikari & Ganesh Gurung. 1999. “Foreign Labour Migration and the Remittance Economy of Nepal.” (mimeographed report to DfID).

Smith, R. & Richard Blundell. 1986. “An Exogeneity Test for a Simultaneous Equation Tobit Model with an Application to Labor Supply.” Econometrica 54:679—685.

37
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles related to the fact that, contrary to the relative pension level, it captures the effect of changes in benefit indexation with the reform.

As done before, pension wealth under the reform scenarios is compared with the noreform pension wealth benchmark in Figure 48. Notice that the pension wealth in the reform scenarios falls in relative terms by more than the relative pension level as with the reform post-retirement pension indexation changes from wage to prices.

**Figure 48: Pension Wealth as a Portion of No-reform Pension Wealth**

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**Source:** WB calculations

We now compare the results for the two reform options.

When the two reform options are compared, low income workers will be better off under the hybrid scheme than under the pure DC option, regardless of the assumed interest rate earned on individual accounts. With no minimum pension guarantee in the pure DC reform option, the expected benefit for this group may drop below 20% of the average wage. Even under the optimistic scenario of 5% real rate of return on investments they can get only about 30% of the average wage which is lower than what most countries target as the as a minimum pension. On the contrary, the hybrid scheme option may significantly reduce investment risk for low income earners, providing at the same time an acceptable benefit level of 43-44% of the average wage.

Under the more optimistic scenario (5% real interest rate), the expected benefits for the average income group in the pure DC system will be very similar to those in the hybrid system: 60% of the average wage for men and 55% for women in the pure DC option versus 57% for men and 55% for women in the hybrid option. But, in the likely event of lower interest rates (3% real), this group will do much better under the hybrid scheme – 36%/32% versus 54%/52% respectively. Though in the hybrid scheme average wage

- 104 -
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so-called _restos a pagar_ . In most states we are unable to assert whether payments in a given year were committed and/or verified in the previous year; furthermore, annulments of verifications and cancellations of payments can be common, and are not always precisely measured in our dataset. We believe both of these factors might drive the systematic overestimation of aggregate amounts in our dataset compared to SICONFI. Finally, we also highlight that results in both our dataset and SICONFI are self-reported by municipalities, meaning there could be systematic discrepancies not due to errors in one source, but due to inconsistent reporting by subnational agents.

While we document that in some states we systematically overestimate total amounts verified and paid, these deviations Dmt are only weakly correlated with municipal characteristics. We perform a predictive OLS exercise regressing Dmt on a list of observables including each municipality’s log population and log GDP per capita. In Table 4 we report results from regressions with only year fixed effects in odd columns and with year and state fixed effects in even columns. In odd columns we find that deviations are negatively correlated with ln(Population) and positively correlated with ln(GDP). For both deviations in commitment and verification amounts, these correlations become much smaller in magnitude and mostly not statistically different from zero once we include state fixed-effects. For deviations in payment amounts, we still observer marginally significant correlations (at 10% significance level) for both population and GDP – in column (6), a one log point increase in GDP correlates with 0.5 p.p. smaller deviation in payments, or about 12% of the mean. A one log point increase in GDP per capita roughly corresponds to going from p25 to p75 of the distribution, so this is a small magnitude.

In sum these results suggest that our data’s aggregate deviations from SICONFI totals are partially correlated with municipalities’ characteristics, although only weakly. Researchers using our data may want to control for such characteristics depending on the research question at hand.

# **4 Applications**

## **4.1 Local firm contracting in public procurement**

Using our newly created dataset on local public procurement in Brazil, we investigate how much geographical variation exists in the location of government suppliers. According to García-Santana and Santamaría (2023), government purchases are highly locally concentrated worldwide. This phenomenon may occur due to supply factors such as regional economic specialization and lack of a diverse local production sector, or frictions such as transportation costs, geography or information asymmetries. It may also be explained by demand factors such as buy-local policies. These policies are ubiquitous across countries, but there is limited evidence regarding whether governments unofficially favor local suppliers even if a formal 11