rafmacalaba/datause-encoder-data huggingface dataset:
individuals who have not moved.<sup>22</sup> The most recent round has 50583 observations and 13507 households. The analytical work below uses detailed information collected from 7283 direct respondents aged 45 and over for whom information on pension eligibility was enumerated.

**_Korea_** . The Korean Longitudinal Study of Aging (KLoSA) is a nationally representative survey conducted by the Korea Labor Institute to investigate the overall conditions of people aged 45 or older in South Korea (Excluding Jeju island). KLoSA belongs to the same family of aging surveys as CHARLS, HRS and ELSA. The first baseline survey was conducted at 2006, and it was followed by a job history survey in 2007. While KLoSA is a longitudinal survey, only the baseline survey is publically available. In common with CHARLS and ELSA, but differing from the HRS, the first baseline survey doesn’t cover the institutionalized elderly. Overall, 6171 households and 10254 individuals participated in the baseline survey, and there are 10030 observations with complete information.

# **A Model of the Employment of Older Adults**

Below, we examine determinants of the labor supply (employment) of older workers, recognizing that important correlates, such as access to pensions, may be systematically related to both the ability to retire and unobservable characteristics (e.g., ability). While these models should be viewed as providing descriptive evidence, we choose measures of health status, and proxies for wealth and family characteristics with the aim of minimizing endogeneity biases.

As a framework for understanding the “retirement” and labor supply decisions of the elderly, assume that individuals (or households) maximize utility subject to a family budget constraint, which is a function of wealth, income, available time, health status and non-labor income of household members. From the constrained utility maximization decision, we conceptualize a general model of labor supply:

> 22Even though older residents of both this survey, as well as the CHNS in China, do not move or split off with the same frequency as household members who were under 25 during the initial wave, one might nonetheless be concerned about this potential source of bias.

14
**Table 1: Sample Means, PISA 2003 and 2006, Indonesia**

||||**Pis**|**a 2003**|||**Pi**|**sa 2006**||
|---|---|---|---|---|---|---|---|---|---|
|Variable||Mean|S.D.|Missing|N|Mean|S.D.|Missing|N|
|_Institutions_||||||||||
|School determine|s pedagogy|0.99|0.09|0%|10,761|0.95|0.22|0%|10,647|
|Adequate supply|of teachers|0.46|0.50|1%|10,691|0.71|0.45|1%|10,493|
|_Schools_||||||||||
|Public||0.54|0.50|1%|10,704|0.60|0.49|1%|10,493|
|Students repeatin|g (%)|0.01|0.04|6%|10,133|0.01|0.03|7%|9,867|
|Rural||0.32|0.47|1%|10,669|0.26|0.44|2%|10,414|
|_Students_||||||||||
|Grade|_8_|0.15|0.36|0%|10,761|0.12|0.33|0%|10,647|
||_9_|0.49|0.50|0%|10,761|0.40|0.49|0%|10,647|
||_10_|0.35|0.48|0%|10,761|0.44|0.50|0%|10,647|
||_11_|0.02|0.13|0%|10,761|0.04|0.21|0%|10,647|
|Age||15.71|0.27|0%|10,761|15.78|0.29|0%|10,647|
|Female||0.50|0.50|0%|10,756|0.49|0.50|0%|10,647|
|_Family_||||||||||
||No education|0.15|0.35|2%|10,545|0.14|0.34|1%|10,503|
|Mother s|chooling:_Primary_|0.35|0.48|2%|10,545|0.35|0.48|1%|10,503|
||_Lower secondary_|0.15|0.36|2%|10,545|0.19|0.39|1%|10,503|
||_Upper secondary_|0.17|0.37|2%|10,545|0.22|0.41|1%|10,503|
||_University_|0.18|0.38|2%|10,545|0.11|0.31|1%|10,503|
|Books at home|_11-100_|0.58|0.49|10%|9,639|0.69|0.46|4%|10,241|
||_101-500_|0.08|0.27|10%|9,639|0.10|0.30|4%|10,241|
|Home computer (|1 or more)|0.16|0.37|0%|10,743|0.15|0.35|4%|10,245|
|Home language s|ame as test|0.32|0.47|4%|10,364|0.34|0.47|1%|10,517|
|PISA score||360|74.9|100%|10,761|391|75.3|100%|10,647|

7
# **Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment**

Hai-Anh Dang, Talip Kilic, Vladimir Hlasny, Kseniya Abanokova and Calogero Carletto<sup>*</sup>

**Keywords:** consumption, poverty, survey-to-survey imputation, household surveys, Tanzania. **JEL Codes** : C15, I32, O15.

* The senior authorship is shared between Dang and Kilic. Dang (hdang@worldbank.org; corresponding author) is a senior economist in the Living Standards and Measurement Study (LSMS) Unit at the World Bank Development Data Group in Washington, DC and is also affiliated with GLO, IZA, Indiana University, and London School of Economics and Political Science; Kilic (tkilic@worldbank.org; corresponding author) is the senior program manager for the LSMS Unit at the World Bank Development Data Group in Washington, DC; Hlasny (vhlasny@gmail.com) is an economic affairs officer at the UN ESCWA in Beirut, Lebanon; Abanokova (kabanokova@worldbank.org) is an economist in the LSMS Unit at the World Bank Development Data Group in Washington, DC; and Carletto (gcarletto@worldbank.org) is the senior manager for the LSMS and the Strategy and Operations Units at the World Bank Development Data Group in Washington, DC. We would like to thank Benoit Dercef, Andrew Dillon, Anne Swindale, and participants at the 2023 European Survey Research Association (ESRA) conference, the IPA/ GRPL conference (Northwestern) and various seminars and workshops at Australian National University, University of Oxford, and the World Bank for helpful discussion and feedback on the earlier drafts. We are grateful for the funding from the United States Agency for International Development (USAID).
<!-- Start of picture text --> Vote-netted revenue<br>( excludin g EBPs )<br>Total (equal to EBP amount,<br>if an y)<br>Total New Fundin g<br>Existing Funding<br>[Provide a breakdown by organization, vote and input factor, as applicable. Use the same format as the<br>“New Fundin g ” section. ]<br>Total Existin g Fundin g<br>Transfers<br>Sending Organization [Provide breakdown by vote and input factor. Use same format as the “New<br>Fundin g ” section. ]<br>Subtotal<br>Receiving Organization [Provide breakdown by vote and input factor. Use same format as the “New<br>Fundin g ” section. ]<br>Subtotal<br>Total Transfers (must equal<br>zero )<br>Grand Total<br>Source: Treasury Board of Canada Secretariat 2014<br><!-- End of picture text --> Exhibit 5. Checklist of possible costs

|**Salary and related costs**|Salaries, wages, shift premiums, overtime allowances, acting pay, reclassifications,<br>bilingual bonuses, relocation, leave, employee benefit plan (pension and insurance)<br>payments|
|---|---|
|**Accommodation costs**|Accommodation charges forgovernment owned or leasedproperties|
|**Other operating**|Travel, conferences, freight, postage, training, contracted personal and professional<br>services, communications, information and telecommunications systems, computing<br>hardware and software, furniture, office supplies, internal professional services such as<br>legal counsel,auditors,corporate services such as libraryand mail room|
|**Capital**|Acquisition of land, construction of buildings, engineering structures and works, car fleets,<br>ships and planes, and major alterations, repairs or renovations to extend the life of these<br>assets|
|**GST**|Whilst not charged to a department’s operating budget, the GST (and state sales tax<br>harmonization payments) must be estimated and shown separately in proposals seeking new<br>funding|
|**Transfer payments**|Estimates of money, goods, services or assets that are transferred from an appropriation to<br>individuals, organizations or other levels of government, including conditional transfer<br>payments such asgrants|
|Source: TreasuryBoard of|Canada Secretariat 2007,63-4.|

In the specific case of capital asset projects, the Treasury Board Submission template also requires that project costing over the forward years account for the total cost of projects, that is, the estimated operating expenses of acquiring and maintaining an asset. While the appropriations are based on a ‘near-cash’ basis, the Canadian budget process requires capital proposals to include accrual information where the resources are incremental (i.e. not included in the department’s reference levels) and the cash and accrual profiles are materially different.

27
Figure 12: Vehicle use per country <!-- Start of picture text --> 30000<br>USA<br>ECU<br>20000<br>AZE IRL<br>CRI ISR DNK<br>LVA<br>FRADEUNZLBELAUTSWE<br>CMR THA TURHRV EST GBRNLDAUS NORCHE<br>FIN<br>10000 SVN<br>POLHUN<br>ROU<br>LTU<br>MLT<br>TUN PER MEX ESP<br>MMR<br>MDA<br>0 NIC BLR KAZ<br>6 8 10 12<br>Log of gdp per capita<br>Average number of annual km per passenger car<br><!-- End of picture text --> Source: IRF data for passenger car ownership and car-km per country. The average number of annual km per passenger car is calculated by dividing the total number of car-km per the number of passenger cars. The graph shows averages over the period 2015-2017 to limit the extent of reporting mistakes.

# **4 Income as a main determinant of transport consumption**

As documented in the previous section, there are large differences across countries and within countries across households with different incomes. Calculating elasticities of transport demand based on cross-country averages would therefore miss the large differences in transport demand and use of carbon-intensive goods that exist within countries. This section uses household survey data to estimate transport elasticities. Harmonized survey data across countries with sufficient transport-related questions are rare. We therefore focus on 18 countries for which we use the rich collection of data from LSMS surveys. Households decide whether to consume transport goods and services, and by how much, to increase their mobility and their economic and social opportunities. We look at the role of income as a main determinant for each of the margin identified in the previous section.

## **4.1 Household-level estimation**

In order to quantify the determinants of transport expenditures at the micro-level, we estimate the relationship between the level of transport expenditures and households’ characteristics us21
where _Zd,t_ + _Ze,t_ is total core output sold domestically and exported to the Rest of the World. Further, 1 + 1 _/εPZ,tμZ >_ 1 is the elasticity of substitution. Noting that the Lagrangian multiplier of the constraint is equal to the aggregate price index, _PZ,t_ , one can show the FOC to read Note that the law of one price requires that the domestically produced core good has the same price domestically and abroad.

**Differentiated core goods firms.** Differentiated core goods firms purchase labor hours from L-firms and energy from E-firms. Using these inputs as well as private and public capital, a Z-firm _j_ produces a differentiated variety of the intermediate core good _Zt_<sup>_j_and</sup> sells it to the core goods aggregators. Differentiated Z-firms operate in monopolistic markets and, hence, have price setting power. We assume that the capital stock is firm-specific and cannot be traded on a spot market. They face various frictions such as price adjustment costs, capital utilization costs, and investment adjustment costs. They pay corporate income taxes as well as payroll taxes. Labor productivity growth is stochastic and follows a deterministic long-run trend,

Taking as given total demand for core goods _Zd,t_ + _Ze,t_ , the final goods price level _PY,t_ , the core price level _PZ,t_ , the real price for energy _pE,t_ , and the real wage rate of a homogeneous labor hour _pL,t_ as well as the law of motion of capital, adjustment costs, the production functions, the demand function for intermediate goods, and the requirement to maintain a debt-capital ratio _λ_ , the firm _j_ chooses _{BZ,t_<sup>_j, P_</sup> _Z,t_<sup>_j, Lj_</sup> _Z,t_<sup>_, I_</sup> _PZ,t_<sup>_j, uj_</sup> _Z,t_<sup>_, K_</sup> _PZ,t_<sup>_j, E_</sup> _Z,t_<sup>_j}_</sup> _t_<sup>_∞_</sup> =0<sup>to maximize</sup> its real beginning-of-period value, _DZ,t_<sup>_j_+</sup> _PPSZ_<sup>_j_</sup> _Y,t,t_<sup>_S_</sup> _Z,t_<sup>_j_</sup> _−_ 1<sup>.</sup> Evaluating at period _t_ = 0, the optimization problem reads where 43
**Figure 16: Unemployment Rates in World Regions**

<!-- Start of picture text --> 14% Developing Europe<br>& Central Asia<br>12%<br>European Union<br>10%<br>Latin America &<br>8%<br>Caribbean<br>6%<br>Middle East & North<br>4% Africa<br>North America<br>2%<br>0%<br>Industrialized East<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Asia<br><!-- End of picture text --> Sources: International Monetary Fund, Middle East and Central Asia Regional Economic Outlook April 2011 and World Economic Outlook Database April 2011; International Labour Organization, Key Indicators of the Labour Market database.

**Figure 17: Unemployment Rate in Crisis Economies**

<!-- Start of picture text --> 25%<br>Spain<br>20%<br>Portugal<br>15%<br>Ireland<br>10%<br>Greece<br>5%<br>United<br>0% States<br>Q1 2000 Q1 2002 Q1 2004 Q1 2006 Q1 2008 Q1 2010<br><!-- End of picture text --> Source: International Monetary Fund, International Financial Statistics.

_26._ The global slowdown in growth heightened vulnerabilities that had already been in place before the crisis. Notably, countries that had their own housing booms, like Ireland and Iceland, or had high fiscal deficits before the crisis, like Greece and Portugal, now teetered on the brink of a sovereign debt crisis and required support from the European Central Bank (ECB) and International Monetary Fund. At present, the fiscal crisis in peripheral Euro countries has already turned into a sovereign debt crisis in the Euro zone, with potentially significant consequences for the world economy as a whole.

20
Furthermore, the work permit is taxable and the tax rate levied by the government would be a key policy instrument for the targeting. By taxing work permits, the government affects the size of the market, hence determining the set of beneficiaries. When the revenues from such taxation are transferred as lumpsum cash benefits, the government is further able to exploit market forces to determine both the breadth and depth of this market-based social protection scheme.

We provide a theoretical model of a market for work permits. We henceforth use the terminologies “rent in” (resp. “rent out”) and “buy” (resp. “sell”) interchangeably, since our model does not have a time dimension. Yet, the scheme of interest in this paper is a market of _temporary_ tradeable work permits. In our model, demand for work permits is driven by risk-neutral workers in the origin country who compare their current earnings with their expected earnings, were they to move (migrate) to the destination country. However, the migrants face two types of costs: (i) expenses associated with the actual costs of the journey and (ii) a non-monetary psychic cost. Workers in the origin country observe the equilibrium price of a work permit and decide whether to demand one accordingly. Symmetrically, workers in destination countries decide whether or not to sell their work permits given their opportunity cost of time, as we assume that all workers have some disutility from work. In equilibrium, the price of a work permit clears the market and the lowest earners in the destination countries are the sellers, while the buyers are those with the highest returns to migration. We next allow the government to levy a tax on work permits. The tax generates tax revenues but the market shrinks as permit prices drop.

To calibrate our model, we assume a fictitious market in the US for Mexican workers. We first estimate what earnings prospective migrants from Mexico would earn in the US by estimating Mincer-type regressions on US survey data and inferring migrant earnings using the characteristics (age, education, etc.) of a Mexican worker. We further deflate these earnings by applying a wage tax schedule and convert post-tax earnings to their real values by deflating with the prevailing relative price indices (Argente et al., 2023). Moreover, we 2
# **3.1 Impacts on the electricity sector**

The impacts of the wind power subsidy and mandate on total electricity generation in Brazil are presented in Figure 6. In the case of the subsidy, the policy basically increases the amount of electricity available at any price (an outward shift of the electricity supply curve). This causes the aggregate price of electricity to drop, thereby increasing electricity demand.<sup>5</sup> To meet the increased demand more electricity would be generated. As can be seen from Figure 6, the subsidy to cause 10% penetration of wind power in Brazilian electricity system would lead to a 1% increase in total electricity generation.

In the case of the mandate, the increased electricity cost, thereby increased electricity price due to expanded wind power is directly transferred to households. The increase in electricity price would decrease its demand. Figure 6 indicates that the expansion of wind power to meet 10% of the total electricity generation, would decrease total electricity demand by 1.5%.

**Figure 6:** Impacts of wind power subsidy and mandate on total electricity generation

<!-- Start of picture text -->
1.50<br>1.00<br>0.50<br>0.00<br>‐0.50<br>‐1.00<br>Subsidy<br>‐1.50<br>Mandate<br>‐2.00<br>Year<br>Change from BAU [%]<br>2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br><!-- End of picture text -->

Pushing wind power into the electricity generation mix through policy instruments means replacing electricity that would have been generated from other technologies, such as

> 5 There would be real income drop due to increased VAT to finance the subsidy, however this drop is distributed across the savings, and consumption of various goods and services. It could reduce the demand for electricity as well, but the effect of net drop in relative price of electricity is more than offsetting the demand loss due to increased VAT.

10
In a similar manner, the timing of pesticide application is as important as the quantity of pesticide used. Thus within the context of IPM, the pest population is carefully monitored in each part of the field. It is believed that only above a critical level is it necessary to control the pest population from becoming too destructive. This is better known as the Economic Threshold Level or ETL. An ETL is defined to be the pest density, as say, a certain number of larvae per plant, above which the marginal return from preventing crop damage is greater than the marginal cost of pesticides used. Yield damage experiments have been able to identify this threshold and again it is important to incorporate this information in formulating an optimal policy of cotton cultivation. Feder and Regev (Feder and Regev, 1975) have pointed out another aspect of the timing decision. They argue that migrant pests can reinfest the cotton farms several times during each growing season. This is because it is the total population of pests in a region that determines the pest infestation on individual farms, once the effects of a pesticide application has worn off. Thus, for pesticide application to be socially optimal requires that all farmers in a region apply pesticides at the same time. Clearly, this has important implications in the design of policy.

16
Solidario is that BDH is explicitly means-tested. Starting in 2001, the government invested into developing a family means test, called the Selben Index. Only families in the poorest two quintiles of the Selben index are eligible to receive BDH's transfer of $15 per month. Another difference between the Bono Solidario and the BDH is that the launch of the BDH was accompanied with a social marketing campaign that encouraged households to invest in the human capital of their children. However, unlike other transfer programs in Latin America, BDH transfers have never been made explicitly conditional on specific investments in child human capital (for example, school enrollment).

The rollout of BDH explicitly contained a randomized component in 4 of Ecuador's 24 provinces. Within provinces selected for the evaluation, parishes (counties) were randomly drawn. Within these parishes, BDH eligible households were randomly sorted into BDH recipient households (lottery winners) and non-recipients (lottery losers). Households formerly receiving Bono Solidario transfers were excluded from the evaluation. Lottery losers were taken off the roster of households that could be activated to receive BDH transfers. An important feature of this experiment is that, unlike the PROGRESA evaluation, randomization is at the household level, rather than the community level. That is, within a community, we observe both lottery winners and lottery losers.

The randomization appears to have been successful in attaining balanced treatment and control populations. Table 1 summarizes background characteristics of children and their families in treatment and control populations. These data are from the baseline survey data collected between June and August 2003 before households were assigned to treatment and control populations. Most of the background characteristics in table 1 appear similar. The control population looks a little more likely to be female and urban than does the treatment population, but these differences are not significant at 5 percent. We also consider our results on samples restricted to just rural and just female below.

There appears to be considerable leakage of BDH into the control population. By design of the experiment, the control population was not supposed to receive the BDH. In reality 38 percent receive it. The treatment group is 73 percent more likely to receive BDH the control population, but the considerable leakage means that our empirical work later will need to be an intent-to-treat type analysis.

4
# _2b. Data for Regional Profiles and Correlation Analysis_

We draw heavily on the same data the World Bank uses in estimating global poverty both to enhance comparability of our analysis with the measures used by many international development agencies (e.g., United Nations, USAID, UK FCDO) and also to maximize country coverage and ensure that our primary findings are valid for the world. The main source of data is the Global Monitoring Database (GMD), an internal World Bank archive of harmonized micro-level income and consumption survey data from 154 countries. (For details, see World Bank, 2020a, Appendix 1A.) In addition, we supplement the GMD survey data with household survey data from the Luxembourg Income Study (LIS) for 8 countries.<sup>7</sup> These are the same data ingested into the Poverty and Inequality Platform (PIP), an interactive online computational tool for the Bank’s poverty and inequality estimates.

In total, our global analysis is based on nationally representative income and consumption survey data from 162 countries. These data are a subset of the 168 countries used for the March 2021 update to the World Bank’s global poverty estimates as described in Arayavechkit et al. (2021). These are the same data that are used to monitor SDG 1.1. We are unable to include six countries primarily because the data for these countries are reported in aggregate form (grouped data) and not available at the unit-record level.<sup>8</sup> The global poverty estimate we report in this paper, based on the per-capita allocation of household resources from the 162 countries in our analysis, is higher than the reported poverty estimate of 9.2 percent in 2017 based on the 168 countries (Arayavechkit et al., 2021). The primary reason the global estimate reported in this paper

> 7 These eight countries are Australia; Canada; Germany; Israel; Japan; the Republic of Korea; Taiwan, China; and the United States.

> 8 The six countries for which we cannot re-estimate individual-level wellbeing based on adjusting resources by the square root of household size are Algeria, China, Trinidad and Tobago, St. Lucia, the United Arab Emirates, and the República Bolivariana de Venezuela.

8