rafmacalaba/datause-encoder-data huggingface dataset:
falls; (iii) any employed member of the household loses his/her job; and (iv) remittances into the household falls.

Based on the above criteria, we divide households (see Table 1) into two groups—―affected‖ and ―not affected.‖ In 2009, 32 percent of households in Armenia, 26 percent of households in Bulgaria, 20 percent of households in Montenegro, 17 percent of households in Romania, and 21 percent of households in Turkey report that at least one component of household income fell relative to 6 to 12 months earlier (see Table 1). In general, salary reductions outnumber job losses (World Bank, 2011). For example, six percent of sampled households in Turkey report a job loss, 20 percent report a wage rate reduction, and 7 percent report a reduction in hours of work (see Table 2). As discussed earlier, some of the reported income shocks are expected regardless of whether or not a crisis occurred, while the rest of the reported income shocks are likely due to the crisis.

Each CRS also contains a detailed module on the strategies adopted by the households to cope with income shocks. Although there is variation in coping strategies adopted, there are also commonalities across countries. We focus on some of the more harmful coping strategies that are linked directly to human capital investments. In health, we identify households that reduced the following relative to 6-12 months earlier: (i) visits to doctors; (ii) medical care expenses; or (iii) purchase of regular medications. Similarly, in education, we identify households that adopted the following coping strategy relative to 6-12 months earlier: (i) withdrew children from school; (ii) moved children from an expensive to cheaper schools; or (iii) postponed training (language courses, information technology courses, etc.) in school or college.

Table 3 presents the distribution of coping strategies in human capital investments. The distribution of coping strategies demonstrates some variation between affected and unaffected households across countries. In Armenia, for example, 43 percent of households cancel visits to doctors or health care centers. The incidence is 16 percentage points higher among households that report income shocks. In Montenegro, Romania and Turkey where 16.4, 8.7 and 20.8 percent household respectively reduced their regular visits to doctors or use of health services. While in some countries the difference between households affected and not affected is substantial (7-8 7
tenure protects against low levels of tenure insecurity but that such protection cannot counter more potent threats; in fact, for severe insecurity the sign of the point estimate reverses, though only attaining marginal significance.

Factors associated with title demand can be analyzed by focusing on parcels without title and estimating where W� is either a zero-one indicator of whether the owner would like to apply for a title or, for cases where this is the case, willingness to pay (in logs). In addition to variables described above, right hand side variables include perceived tenure insecurity (S�) to explore if high levels of perceived tenure insecurity create demand for land documentation and legalization. Table 7 displays results for the regressions with a zero-one indicators of wanting to apply for title (col. 1-3) and willingness to pay for it (col. 4-6). Those suffering from insecurity are more likely to apply for a title overall (col.1) and on customary land (col. 3) but less on state land (col. 2). Comparing the point estimate (0.095) to the dependent variable mean implies that insecurity increases demand for title by some 20%. Transferability is associated with about 30 percentage points higher level of title demand. While having title in process increases the amount willing to pay on state land by some 50%, having an informal certificate is associated with an increment in title demand by 38% and a 27% higher willingness to pay on customary land. Informal and formal documents thus do not seem to be substitutes for each other but rather have a strongly complementary relationship.

# **4.2 Comparing results and methodology to those of the Prindex global poll**

Global polls such as Prindex could complement and put country-level statistics in context if concepts and procedures used are comparable. Comparing descriptive statistics from Prindex to those obtained using the LFS is, however, difficult without a description of the sampling procedure and information on non-response neither of which was available. To nevertheless capitalize on availability of these data which, for example, provide information on a larger set of transfer rights, we focus on regressions to explore links between access to either formal or informal documentation and transferability.

Results as reported in table 8 suggest that there is no association between access to a formal or informal documentation and the ability to transfer land _intra vivos_ , bequeath, rent, or sell it, or use it as collateral. While this suggests that a careful methodological discussion of Prindex data and approach could be useful, empirical examination of data for the 32 countries (in addition to Zambia) for which data have been gathered thus far is well beyond the scope of this paper. Two issues that may explain some of the surprising findings reported above and their potentially broader implications are worth mentioning. First, the Prindex focuses on property rather than land that goes together with potentially very high levels of under-reporting of nonresidential agricultural properties: in the case of Zambia, just about 4% of respondents, compared to 75% in 16
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implies a long-term welfare loss from even transient unemployment at given income. It also suggests that high unemployment benefits do not attract people out of work, but they may well discourage a return to work. Also, the unemployment effect was not found to be robust to restricting the sample to those for whom incomes were almost certainly better measured. This suggests that there could well be a bias in past estimates of the (income-compensated) welfare effect of unemployment, arising from the structure of income measurement errors.

These panel data models impose considerable structure on the way in which latent heterogeneity is deemed to matter to subjective welfare, namely that it takes the form of a individual specific time-invariant and additive component of the error term. I turn next to another source of data that offers a more flexible and robust way of addressing concerns about latent heterogeneity in scales.

# **_Using vignettes to address heterogeneity in scales_**

The essential idea of vignettes in this context is to include in the survey (or a sub-sample) reasonably detailed descriptions of the lives of certain stylized individuals and ask the respondent the same subjective question about those vignettes that was posed about the respondent herself.

Vignettes to have been used in a number of recent studies of subjective data on ( _inter alia_ ) health status, political efficacy and job satisfaction. King et al. (2004) and King and Wand (2007) designed vignettes to establish common points on the heterogeneous reference scales regarding political efficacy in China and Mexico. Kristensen and Johansson (2008) used vignettes in anchoring subjective scales for job satisfaction. Bago d’Uva et al. (2008) used them for correcting self-assessed health data for reporting bias. Vignettes have also been used in testing the competence of doctors (Das et al., 2008). Kapteyn et al. (2008) used vignettes to compare life satisfaction between respondents in the U.S. and the Netherlands.

Beegle, Himelein and Ravallion (BHR) (2012) used vignettes to provide various tests for bias due to latent heterogeneity in individual scales of subjective welfare. Their tests entailed adding vignettes to a national household survey for Tajikistan in 2007. Respondents placed these vignettes on the same six-step ladder used to report their own subjective economic welfare. BHR performed three sets of tests for bias. In their first test, they found some significant covariates for 25
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paragraphs.

Customary land holders are willing to sell their unit of land plot if the oered price _p_ lies above their reservation utility _q_ ( _a − u_ ) . The set of customary enforcement levels is therefore given by _Q_<sup>_∗_</sup> ( _p_ ) = [ _<u>q, p/</u>_ ( _a − u_ )] if _p/_ ( _a − u_ ) _<_ _<u>q</u>_ and <u>[</u> _<u>q, q</u>_ <u>]</u> otherwise. Here, it is convenient to denote _<u>p</u>_ = _<u>q</u>_ ( _a − u_ ) _>_ 0 and _<u>p</u>_ = _<u>q</u>_ ( _a − u_ ) _>_ 0 ( 0 _< p <_ _<u>p</u>_ ~~)~~ the land prices enticing supply by none or by all customary landowners. The broker's expected prot can then be written as The equilibrium is found where brokers make zero expected prot, that is _V_<sup>�</sup> ( _p_ ) = 0 . It is easy to see that the function _V_<sup>�</sup> ( _p_ ) is equal to zero at _p_ = 0 and is negative and decreasing for _p ∈_ (0 _, p_ <u>]</u> . It is also decreasing for _p >_ min _{p,_ _<u>p</u>_ _~~}~~ /_ ( _a − u_ ) . In this paper we simplify the discussion of equilibria by assuming that _V_<sup>�</sup> ( _p_ ) crosses the zero axis at most once from below for _p ∈_ [ _p_ _<u>p</u>_ <u>]</u> . A sucient condition is that This is equivalent to saying that _p_<sup>_o_</sup> ( _x, q_ ) _g_ ( _q_ ) is increasing in _q._ For instance, when _θ_ ( _q_ ) is small enough, one just needs to have _π_ ( _q_ ) _g_ ( _q_ ) a monotone increasing function of _q_ . Under this assumption, the expected prot linearly falls from and below zero on [0 _, p_ <u>]</u> , increases on <u>(</u> _<u>p, p</u>_ <u>]</u> _,_ reaches a maximum at _p_ = _<u>p</u>_ and, again, linearly falls on <u>(</u> _<u>p, ∞</u>_ ) . This is shown in Figure 2. A rst solution for _V_ ( _p_ ) = 0 is the trivial equilibrium solution _p_ = 0 for which no customary landowner supplies land and the land market is inactive (i.e. _Q_<sup>_∗_</sup> (0) = _∅_ ). This is the unique solution if _V_<sup>�</sup> <u>(</u> _<u>p</u>_ <u>)</u> _<_ 0 . Otherwise if _V_<sup>�</sup> <u>(</u> _<u>p</u>_ <u>)</u> _>_ 0 , the function _V_<sup>�</sup> ( _p_ ) has three roots _p ∈{_ 0 _, p_<sup>_∗∗_</sup> _, p_<sup>_∗_</sup> _}_ ( 0 _< p_<sup>_∗∗_</sup> _<_ _<u>p < p</u>_<sup>_∗_</sup> ) and is positive on the interval [ _p_<sup>_∗∗_</sup> _, p_<sup>_∗_</sup> ] . However, only the highest price _p_<sup>_∗_</sup> is robust to overbidding by brokers. Indeed, if all brokers set a price _p < p_<sup>_∗_</sup> _− ε_ with small enough _ε >_ 0 , some brokers can reap the land market by setting the price _p_<sup>_∗_</sup> _− ε_ and make a positive prot _V_<sup>�</sup> ( _p_<sup>_∗_</sup> _− ε_ ) . Hence, under asymmetric information, the land market yields the equilibrium price _p_<sup>_∗_</sup> for all transactions with all types of landowners. To complete the argument, we now reintroduce the reference to the CBD distance _x_ .

15
**Figure A1** Histogram of habitation populations (PMGSY OMMS)

<!-- Start of picture text --> Histogram of Habitation Population<br>PMGSY Administrative Data<br>0 500 1000 1500 2000<br>Population<br>10,000<br>8000<br>Frequency 6000<br>4000<br>2000<br><!-- End of picture text --> Notes: The figure shows the histogram of village population as reported in the PMGSY Online Monitoring and Management System. The vertical lines show the program eligibility thresholds at 500 and 1,000. Due to evidence of manipulation, the running variable is population from the 2001 Population Census.

57
time across all vaccines, given the time the contract was signed, and the income level of the country signing the contract.

Table 2: Regressions of delivery data on purchase contract date and fixed effects

|VARIABLES|(1)<br>Month of first<br>delivery|(2)<br>Month of first<br>delivery|
|---|---|---|
|Month contract is signed|0.432***<br>(0.0759)|-0.115<br>(0.137)|
|Month signed X Contract signed in 2021||0.823***<br>(0.168)|
|Low-income or lower-middle-income (=1)|1.447***<br>(0.461)|1.450***<br>(0.462)|
|Upper-middle income (=1)|1.159***<br>(0.351)|1.449***<br>(0.349)|
|Contract signed in 2021 (=1)||-10.49***<br>(2.110)|
|Constant|10.55***<br>(0.706)|15.68***<br>(1.191)|
|R-squared|0.431|0.515|
|Numberofobservations|367|367|
|Vaccine candidatefixed effects|Yes|Yes|

Source: Authors’ calculations based on the IMF-WHO Vaccine Supply Tracker and the UNICEF Vaccine Market Dashboard Notes: Months are measured in months since January 1, 2020. Data include contracts for which there were partial or full deliveries, 77 percent of contracts by HICs, 83 percent by UMICs, 84 percent by LMICs, and 50 percent by LICs. The fixed effects for a highincome country and the AstraZeneca/Oxford vaccine are omitted from the regression. Standard errors in parentheses are robust to heteroskedasticity.

*** p<0.01, ** p<0.05, * p<0.1

The coefficient on the month the contract is signed, in Column (1) of Table 2, is positive and statistically significant, its value indicating that for each month later a contract is signed, the first delivery arrives 0.432 month later (standard error = 0.0759). Table 1 showed that low-income countries ordered on average 5 months later than high-income countries, so this implies their first delivery arrived 0.432 x 5 = 2.16 months later on average, because they ordered later.

9
attachments and stimulation are significant aspects of brain development in the early years and play-based learning helps children develop their fine and gross motor skills, develop language and socialization skills, and become creative problem-solvers. Play not only enhances children’s learning readiness but also can more generally help them adjust to school settings (Zigler, Singer and Bishop-Josef 2004). Child development research has also shown that children’s intellectual development is best supported when children receive increasingly complex, differentiated learning experiences (Bronfenbrenner 1994, Engel, Claessens and Finch 2013).

However, playgroups in Indonesia were not designed to provide multiple years of unique, developmentally appropriate learning. They also meet less frequently than other types of services. As a result, children who subsequently enroll in kindergarten are more likely to avoid redundancy in their learning experiences by having exposure to different, more academically focused curricula. We caution against interpreting these results as support for solely academically-focused early education given that child development research shows that children who are exposed to playbased learning in the early years are significantly more likely to have positive socio-emotional development than children who are only exposed to academic preschools (Elkind 2008). Instead, we interpret the results as evidence that early childhood education must support children’s learning at various stages of development and in the context of Indonesia, this is most strongly supported when children enroll in a play-based early education setting (playgroup) followed by a more structured and increasingly academic-based environment (kindergarten) prior to primary school. Given that current policy debates are centered around making a single year of preprimary education compulsory, these findings are timely.

It is important to discuss several limitations to our study. First, although our analysis controlled for a rich set of the household background and individual children’s characteristics that should mitigate why students went through different early education pathways, our estimates may be biased if unobserved individual and family characteristics are responsible for the differences in early learning outcomes between children following different early education pathways. For example, parents particularly motivated by education might send their children to the full sequence of preprimary education at the correct timing, which would lead to positive bias in our estimates. In analyses not shown in our results tables, we found that when we controlled for child, family and ECED characteristics, the magnitude of the coefficients on early childhood education pathways became smaller. This seems to indicate that unobserved child, family, and ECED characteristics 22
- Tanzania National Bureau of Statistics (TNBS) (2017). Tanzania National Panel Survey 20142015, Wave 4. Public Use Dataset. Ref: TZA ~~2~~ 014 ~~N~~ PS-R4 ~~v~~ 03 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/2862` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2021). Tanzania National Panel Survey 2019-2020 - Extended Panel with Sex Disaggregated Data. Public Use Dataset. Ref: TZA ~~2~~ 019 NPS-SDD ~~v~~ 06 ~~M~~ . Downloaded from `https://microdata.worldbank.org/index. php/catalog/3885` on 9 February 2024.

- Tanzania National Bureau of Statistics (TNBS) (2023). Tanzania National Panel Survey 20202021, Wave 5. Public Use Dataset. Ref: TZA ~~2~~ 020 ~~N~~ PS-R5 ~~v~~ 02 ~~M~~ . Downloaded from `https: //microdata.worldbank.org/index.php/catalog/5639` on 9 February 2024.

- Taraz, V. (2018). Can farmers adapt to higher temperatures? evidence from India. _World Development 112_ , 205–19.

- Tarnavsky, E., D. Grimes, R. Maidment, E. Black, R. P. Allan, M. Stringer, R. Chadwick, and F. Kayitakire (2014). Extension of the TAMSAT satellite-based rainfall monitoring over Africa and from 1983 to present. _Journal of Applied Meteorology and Climatology 53_ (12), 2805–22.

- Tesfaye, W., G. Blalock, and N. Tirivayi (2021). Climate-smart innovations and rural poverty in Ethiopia: Exploring impacts and pathways. _American Journal of Agricultural Economics 103_ (3), 878–99.

- Tzachor, A., C. Richards, M. Gudoshava, P. Nying’uro, H. Misiani, J. Ongoma, Y. Yair, Y. Mulugetta, and A. Gaye (2023). How to reduce Africa’s undue exposure to climate risk. _Nature 620_ , 488–91.

- Uganda Bureau of Statistics (UBOS) (2012). Uganda National Panel Survey 2005-2009. Public Use Dataset. Ref: UGA ~~2~~ 005-2009 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1001` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2014a). Uganda National Panel Survey 2010-2011. Public Use Dataset. Ref: UGA ~~2~~ 010 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2166` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2014b). Uganda National Panel Survey 2011-2012, Wave III. Public Use Dataset. Ref: UGA ~~2~~ 011 ~~U~~ NPS ~~v~~ 01 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/2059` on 6 September 2019.

- Uganda Bureau of Statistics (UBOS) (2016). Uganda National Panel Survey 2013-2014. Public Use Dataset. Ref: UGA ~~2~~ 013 ~~U~~ NPS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2663` on 9 February 2024.

- Uganda Bureau of Statistics (UBOS) (2019). Uganda National Panel Survey 2015-2016. Public Use Dataset. Ref: UGA ~~2~~ 015 ~~U~~ NPS ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/3460` on 9 February 2024.

- Uganda Bureau of Statistics (UBOS) (2021). Uganda National Panel Survey 2019-2020. Public Use Dataset. Ref: UGA ~~2~~ 019 ~~U~~ NPS ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/3902` on 9 February 2024.

29
-9-

- The private sector's surplus of savings over investment, NPS-Spr Ipr. is shown as a function of the real rate of interest. A higher real<sup>interest rate</sup> will slow down private sector investment and increase private<sup>savings, thus</sup> increasing NPS. Empirical evidence on these effects is<sup>presented in Section</sup> II.C. This is represented by the upward sloping line "NPS"<sup>in Figure 2. The</sup> external deficit that is compatible with given real interest rates<sup>(FCA for</sup> reasible current account) is then represented by the sum of NPS and the fiscal surplus (FS; this equals minus the deficit). The horizontal line TCA is the target value for the current account. The real interest rate at which the current account target TCA equals the feasible current account FCA is the real rate at which fiscal policy and current account targets are in line.

An increase in fiscal deficits represents a decline in the fiscal surplus and hence a downward shift in the feasible current accoutit line FCA. To still meet the same current account target, a higher interest rate is needed to call forth the required extra surpltts of private savings over private investment (r shifts from rA to rB). A cut in fiscal deficits will thus allow lower real interest rates for given current account targets,<sup>and</sup> hence higher private investment.

W%ether real interest rates are indeed endogenously determined out of the interplay between external balance constraints, fiscal deficits and private (net) savings behavior, or whether consistency is achieved by administered setting of interest rates, or, for that matter, by arbitrage between foreign and domestic interest rates, is an issue we do not need to address here. What matters is that (ecoaometrically verified) relations Paper/ac :07-28-88: lc ChhibbotDisk/chhibber-Turkey
31

# **Appendix 1: Use of LPG and Solid Fuels for Cooking by Country**

Table A1.1: LPG and solid fuels as primary cooking fuel in developing countries

|**Region**|**Country**|**Source**|**Year**|**% all**<br>**house-**<br>**holds**<br>**using LPG**|**% urban**<br>**house-**<br>**holds**<br>**using LPG**|**% rural**<br>**house-**<br>**holds**<br>**using LPG**|**% using**<br>**solid fuels**|
|---|---|---|---|---|---|---|---|
|**Asia**|Afghanistan|WHO|2007|10|50|3|86|
||Bangladesh|WHO|2007|0.8|3|0.2|91|
||Bhutan|MICS related|2007|23|45|13|42|
||Cambodia|HH surveys|2003–04|5.1|23|2.1|93|
||China|WHO|2006|28|40|22|49|
||India|HH surveys|2004–05|22|60|8.6|70|
||Indonesia+|WHO|2007|11|20|3.8|43|
||Lao PDR|MICS3|2006|0.8|2.5|0.1|98|
||Malaysia|WHO|2003|96|96|95|0.8|
||Mongolia|MICS3|2005|23|0.6|0|77|
||Nepal|WHO|2006|10|40|3.9|83|
||Philippines|WHO|2003|48|62|27|45|
||Solomon Islands|WHO|2007|7.7|41|2.5|92|
||Sri Lanka|This study|2006–07|17|51|12|80|
||Thailand|MICS3|2005–06|59|81|51|37|
||Tonga|WHO|2006|54|84|45|41|
||Vanuatu|MICS3|2007|12|44|2.6|85|
||Vietnam|MICS3|2006|31|70|18|66|
|**ECA**|Albania|MICS3|2005|33|50|19|56|
||Armenia|WHO|2005|37|40|31|4|
||Azerbaijan+|WHO|2006|69|84|48|10|
||Belarus|MICS3|2005|26|9.2|57|3.4|
||Bosnia and<br>Herzegovina|MICS3|2006|5.5|9.4|3.2|49|
||Croatia|WHO|2003|75|78|68|12|
||Estonia|WHO|2003|38|43|24|16|
||Georgia|MICS3|2005|12|18|5.4|54|
||Kazakhstan|MICS3|2006|40|37|47|19|
||Kyrgyz Republic|MICS3|2006|9|9.3|8.7|37|
||Latvia|WHO|2003|82|88|67|9.8|
||Macedonia, FYR|MICS3|2005|2.5|3.8|0.5|36|
||Montenegro|MICS3|2005|2.0|2.1|1.8|32|
||Russian Federation|WHO|2005|26|9.2|59|3.4|
||Serbia|MICS3|2005|8.6|9.2|7.6|34|
||Slovak Republic|WHO|2003|80|84|74|2.9|
||Tajikistan|MICS3|2005|4.5|11|1.6|35|
||<br>Turkmenistan+|DHS|2000|96|—|—|0.3|
||Ukraine|MICS3|2005|8.1|3.4|19|9.1|
||Uzbekistan|MICS3|2006|2.2|3|2.6|16|
|**LAC**|Antigua Barbuda|WHO|2007|96|—|—|2|