rafmacalaba/datause-encoder-data huggingface dataset:
||Energy expenses<br>by income<br>quintile|Consumption<br>expenditure on<br>electricity, gas, and other<br>fuels as a share of<br>income-by-income<br>quintile|EUROSTAT<br>based on<br>Harmonized<br>HBS<sup>17</sup>|Yes, but only<br>for 2005,<br>2010, 2015,<br>and 2020|
|---|---|---|---|---|
||Ability to keep<br>home adequately<br>warm|Share of the population<br>unable to keep home<br>adequatelywarm|EU-SILC|Yes|
||Arrears on utility<br>bills|Share of population with<br>arrears of utilitybills|EU-SILC|Yes|
|Consensual|Dwellings<br>comfortable cool<br>in the<br>summertime|Share of the population<br>able to keep home<br>comfortably cool|EU-SILC|Not available<br>for BGR<sup>18</sup>|
||Dwellings<br>comfortable<br>warm in<br>wintertime|Share of the population<br>able to keep home<br>comfortably warm|EU-SILC|Not available<br>for BGR|
||Presence of leak,<br>damp, rot.|Share of population<br>reporting leakage|EU-SILC|Yes|
||Biomass prices|Average household price<br>per kWh generated from<br>biomass|BSO|No|
||Coal prices|Average household price<br>per kWh generated from<br>coal|BSO|No|
|Energy|Fuel oil prices|Average household prices<br>per kWh generated from<br>fuel oil|BSO|No|
|Prices|Household<br>electricity<sup>19</sup><br>price|Electricity price for<br>household consumers<br>(consumption band 2500-<br>5000 kWh)|EUROSTAT|Yes|
||Household gas<br>price|Gas price (consumption<br>band 20-200 GJ)|EUROSTAT|Yes|
||District heating<br>price|Average household price<br>per kWh generated from<br>district heating|BSO|No|

> 17 - - <u>https://ec.europa.eu/eurostat/web/household budget surveys/methodology</u>

18 Information on ability to keep home cool is particularly important in the context of climate change and the increasing likelihood of heat waves. The World Bank rapid surveys collected in June 2023 collect information to construct this indicator in Bulgaria. This information could potentially be collected through specific ad hoc modules of the national EU-SILC, as recommended by the latest EC recommendation on energy poverty in October 2023. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202302407 19 Household electricity and gas price are considered complementary indicators, as they affect affordability but do not indicate affordability per se.

8
- MDG 1c: halve between 1990 and 2015, the proportion of people who suffer from hunger (Malnutrition prevalence, weight for age, percent of children under 5).

- MDG 2.a: ensure that by 2015, children everywhere, boys and girls alike, will be able to complete a full course of primary schooling (Primary completion rate, total, percent).

- MDG 3.a: eliminate gender disparity in primary and secondary education, preferably by 2005, and at all levels of education no later than 2015 (Ratio of female to male in primary and secondary enrollment).

- MDG 4.a: reduce by two-thirds, between 1990 and 2015, the under-five mortality rate (Mortality rate, under-five, per 1,000).

- MDG 5.a: reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio (Maternal mortality ratio, per 100,000 live births).

- MDG 7.c: halve by 2015 the proportion of people without sustainable access to safe drinking water and basic sanitation (Improved water source and sanitation facilities, percent of population without access).

In what follows, we take a close look at MDG performance in developing countries, with a particular focus on those countries facing the larger gaps in terms of MDG achievement.

**_Several low-income countries are doing well._** A look beneath the aggregate global statistics shows not just middle-income countries doing well, but many low-income countries, too (table 1).  This confirmed that progress in individual African and poor countries was indeed strong.<sup>9</sup>

**_Although the variation among lagging countries is large, the average gap is not._** Lagging countries are, on average, only 23 percent away from being on track to achieve all the MDGs (table 2). They are especially close to the targets for gender parity in primary education (average gap is 7 percent); gender parity in secondary education (16 percent gap); hunger (19 percent gap); primary education completion (20 percent gap); and, to some extent, under-five mortality (23 percent gap). But for each target there are countries where progress has been scant. For example, several countries are far from halving extreme poverty, even as the global goal will be reached.

Progress is mixed or poor on access to safe drinking water, access to sanitation, maternal mortality, and extreme poverty. Even so, the mean gaps of all lagging countries are less than 50 percent from the targets on access to safe drinking water (25 percent) and access to sanitation (27 percent), and no worse than 40 percent on maternal mortality (32 percent) and extreme poverty (39 percent).

> 9 See Leo and Barmeier 2010.

7
communities that are much harder to access, resulting in higher non-response rates through noncontact, although they do not provide evidence for this (reasonable) assertion.

Kennickel (2019) shows that non-response rates in the US Survey of Consumer Finances are much higher for those at the very top of the income distribution. The survey has two sample frames, one derived using a regular household survey frame and another for rich households, which is derived from tax filer data. For the richest stratum of the tax filer derived sample frame, described as “a group in the upper reaches of the top 1%”, the response rate in 2013 was less than 10%, compared to 66% in the sample obtained from the regular household sample frame (Kennickel, 2019: 446).

Hlasny and Verme (2018) used data from the Egyptian Income and Expenditure Survey to show that unit non-response rates are higher in Egyptian governorates with higher mean income per capita. The National Income Dynamics Study (NIDS) in South Africa obtained much higher unit non-response rates than the previously mentioned LMIC surveys- 31%. Non-response rates by race of the predominant racial group in the primary sampling unit (PSU) also varied. Predominantly white areas had nonresponse rates of 64% while predominantly black areas had a non-response rate of 24% (Leibbrandt et al. 2009). Since race is strongly positively correlated with income in South Africa, the implication is that non-response rates are increasing with income, which is a concern when measuring the top of the income distribution.

# _Item non-response_

We have noted that refusals or non-contacts may limit how well one measures the top of the income distribution if the rich have high non-response rates. But those individuals who do respond to a survey may not answer specific questions about incomes, wealth or consumption, since these are more sensitive. This issue is called item non-response. Like unit non-response, this can be missing completely at random, or it may be related to other individual characteristics or income levels themselves. If the latter, then this is a form of selection bias, like the potential bias of unit nonresponse.

Bollinger et al. (2019) used linked survey and administrative earnings data from the US to show that item non-response is not missing completely at random- it is highest at low and high earnings. Such studies require substantial resources and are thus unfortunately uncommon in LMICs. However, Flachaire et al. (2022) did obtain such linked survey and income tax data from Uruguay. The authors show that item non-response was around 10% in the surveys and that it was highest at the bottom of the income distribution, a surprising result that, taken at face value, suggests that item non-response may not be as much of a concern when measuring the top of the income distribution as one might expect.

6
OECD (2009). Creating effective teaching and learning environments: First results from TALIS.

Terrier, C. (2016). Boys lag behind: How teachers' gender biases affect student achievement. IZA Discussion Paper No. 10343. Available at SSRN: <u>https://ssrn.com/abstract=2868309</u>

- Tharp, R. G., & Gallimore, R. (1988). _Rousing minds to life: Teaching, learning, and schooling in social context._ New York, NY, US: Cambridge University Press.

- Tishman, S., Jay, E., & Perkins, D. N. (1993). Teaching thinking dispositions: From transmission to enculturation. _Theory into Practice_ , _32_ (3), 147-153.

- <mark>Tyler, J. H., Taylor, E. S., Kane, T. J., & Wooten, A. L. (2010). Using student performance data to identify effective classroom practices.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>100</mark>_ <mark>(2), 256-60.</mark>

- Vosniadou, S. (Ed.). (2009). _International handbook of research on conceptual change_ . Routledge.

Vygotsky, L. S. (1978). _Mind in society_ . Cambridge, MA: Harvard University Press.

- Wallin, D. (2003). Student leadership and democratic schools: A case study. _National Association of Secondary School Principals NASSP Bulletin_ , _87_ , 55–78.

- Walsh, J. A., & Sattes, B. D. (2011). _Thinking through quality questioning: Deepening student engagement_ . Corwin Press.

- Walsh, J. A., & Sattes, B. D. (2016). _Quality questioning: Research-based practice to engage every learner_ . Corwin Press.

- Wang, M. C., Haertel, G. D., & Walberg, H. J. (1993). Toward a knowledge base for school learning. _Review of Educational Research_ , _63_ (3), 249-294.

Wang, Z., Devine, R. T., Wong, K. K., & Hughes, C. (2016). Theory of mind and executive function during middle childhood across cultures. _Journal of Experimental Child Psychology_ , _149_ , 6-22.

- Webb, N. M. (2009). The teacher's role in promoting collaborative dialogue in the classroom. _British Journal of Educational Psychology_ , _79_ (1), 1-28.

- Wharton-McDonald, R., Pressley, M., & Hampston, J. M. (1998). Outstanding literacy instruction in first grade: Teacher practices and student achievement. _Elementary School Journal, 99_ , 101–128.

Wilen, W. W., & Clegg, A. A. (1986). Effective questions and questioning: A research review, _Theory & Research in Social Education, 14_ (2), 153-161, doi: 10.1080/00933104.1986.10505518 Willingham, D. T. (2007). Critical thinking. _American Educator_ , _31_ (3), 8-19.

62
Figure 22: Proportion of Crop Produce Sold and Poverty, 2014/15 <!-- Start of picture text --> 50%<br>millet<br>40% lentils<br>roselle<br>sorghum groundnut tomato<br>30%<br>sesame other_crop<br>20%<br>10%<br>egypt_beans onion<br>wheat dates<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%<br>Proportion of Crop Sold<br>Poverty Rate<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

There are important regional differences in which crops are grown (Figure 23). Dates are the most common crop grown in the Nile region (Figure 23a). Sorghum is the most common crop grown in the Eastern region, Khartoum, and the White and Blue Nile region (Figure 23b–d). It is also the second most important crop grown in Darfur and Kordofan (Figure 23e–f). Millet is most common in Darfur and is important in Kordofan but is not grown extensively elsewhere. The most important crop in Kordofan is sesame.

Sesame is a common cash crop in the Kordofan region, where it was grown by almost half of all farmers in 2014/15 (Figure 23e). As the 2009 survey did not include sesame as a potential crop grown, it is difficult to confirm whether the current production of sesame is a recent trend or if it has been a common crop produced by Sudanese farmers. However, as shown in Figure 4b, the Food and Agriculture Organization of the United Nations (FAO) statistics show that sesame production and exports have grown significantly during this time. Substantial reduction in rural poverty (Figure 10b) in two of Kordofan’s three states (the states not affected by conflict) may be partly explained by farmers growing this cash crop.

Households engaged in crop production in Sudan’s most populous rural region, that is the Blue and White Nile (the states of Al-Gezira, White Nile, Sinnar, and Blue Nile), mostly grow sorghum (Figure 23d), but larger commercial farms in this region grow cash crops. This region is heavily irrigated. States such as Al-Gezira are known as the breadbasket of Sudan. Most households list income from wages and salaries as their main source of livelihood in these states. Information on what crops these farmers are growing is not provided in the survey. As seen in Figure 4b, Sudan has recently increased exports of sesame and sugar. This is largely due to increased production in this region. Given that individuals from these households are employed in larger, commercial farms, increasing the productivity and profitability of these farms will be vital to ensure the prosperity of the households of such employed farmers.

25
52

# REZLCES

AINSWORTH, M., and J. MUNOZ. 1986. The Cote dlIvoire Living Standards Survey: Design and Imolementation. Living Standards Measurement Study Working Paper No. 26. Washington, DC: World Bank.

## ATLAS de la COTE d'IVOIRE. 1983. Editions Jeune Afrique.

- BEHRMAN, J. 1988. 'A survey on socioeconomic development, structural adjustment and child health and mortality in developing countries." Paper presented to seminar on child survival programs: issues for the 1990s, Johns Hopkins University, 20-22 November 1988.

- BOULIER, B., and V. PAQUEO. 1988. 'On the theory and measurement of the determinants of mortality." Demograaby 25(2):249-63.

- COHEN, M. A. 1984. "Urban Policy and Development Strategy."<sup>In I.W.</sup> Zarthman and C. Delgado (eds), The Politlcal Economy of Ivory Coast. A SAIS study on Africa. Praeger.

- CORNIA, G. A., et al., eds. 1987. Adjust=ent with a Human Face. Vol. 1, Protecting the Vulnerable and PromotLng Gr2wth. Oxford: Clarendon Press.

- CROPPER, M. L. 1977. "Health, investment in health, and occupational choice." JouMrnal_of Political Econom 85:1273-94.

- den TUINDER, B. A. 1978. Ivogy Coast: The Challenge of Success. A World Bank Country Economic Report. Baltimore: Johns Hopkins Universlty Press.

- FUCHS, V., ed. 1980. Economic Aspects of Health. Chicago: University of Chicago Press.

- GALLOWAY, P. 1988. "Basic patterns in annual variations in fertillty, nuptlality, mortality and prices in pre-industrial Europe." opulation StudLes 42(2).

- GROOTAERT, C. 1987. Mosure et Analyze des Niveyaux de vle dans les Pays en Development: Ouestionnaire Annote. Llving Standards Measurement Study Working Paper No. 24. Washington, DC: World Bank.

- GROOTAERT, C., and R. KANBUR. 1990. Policy-oriented analysis of poverty and the social dimensions of structural adjustment: A methodologv and progosed a2plication to Cote d'Ivoire. 1985-88. SDA Working Paper No 1. Washington, D.C. : World Bank.

- GROSSMAN, M. 1972. The Demand for Health: A Theoretical and E=pirical I.Ugatig n. New York: National Bureau of Economic Research.
- Diluiso, F., Annicchiarico, B., Kalkuhl, M., Minx, J. C. (2021): Climate actions and macrofinancial stability: The role of central banks, _Journal of Environmental Economics and Management_ , 110, p. 102548.

- DISK (2022): Asgari Ucret Gercegi 2022 Raporu, Tech. rep., Devrimci Isci Sendikalari Konfederasyonu Arastirma Merkezi.

- Dissou, Y., Karnizova, L. (2016): Emissions cap or emissions tax? A multi-sector business cycle analysis, _Journal of Environmental Economics and Management_ , 79, pp. 169–188.

- Dorband, I. I., Jakob, M., Kalkuhl, M., Steckel, J. C. (2019): Poverty and distributional effects of carbon pricing in low- and middle-income countries - A global comparative analysis, _World Development_ , 115, pp. 246–257.

- Fischer, C., Springborn, M. (2011): Emissions targets and the real business cycle: Intensity targets versus caps or taxes, _Journal of Environmental Economics and Management_ , 62(3), pp. 352–366.

- Gerali, A., Neri, S., Sessa, L., Signoretti, F. M. (2010): Credit and Banking in a DSGE Model of the Euro Area, _Journal of Money, Credit and Banking_ , 42(s1), pp. 107–141.

- Gir ́aldez, J., Fontana, S. (2021): Sustainability-linked bonds: the next frontier in sovereign financing, _Capital Markets Law Journal_ , 17(1), pp. 8–19.

- Guerrieri, L., Erceg, C., Bodenstein, M. (2008): Oil Shocks and External Adjustment, Tech. rep., Society for Economic Dynamics.

- G ̈urkaynak, R. S., Kisacikoglu, B., Lee, S. S. (2022): Exchange Rate and Inflation under Weak Monetary Policy: Turkey Verifies Theory, CEPR Discussion Papers 17248, C.E.P.R. Discussion Papers.

- Hallegatte, S., Mcisaac, F. J., Dudu, H., Jooste, C., Knudsen, C., Beck, H. A. (2023): The Macroeconomic Implications of a Transition to Zero Net Emissions: A Modeling Framework, Tech. rep., The World Bank.

- Havranek, T., Irsova, Z., Laslopova, L., Zeynalova, O. (2020): The elasticity of substitution between skilled and unskilled labor: A meta-analysis, unpublished.

- Herbst, E., Schorfheide, F. (2015): _Bayesian Estimation of DSGE Models_ , The Econometric and Tinbergen Institutes Lectures, Princeton University Press.

- Hodrick, R., Prescott, E. C. (1980): Post-war U.S. business cycles: an empirical investigation, Discussion Paper 451, Carnegie-Mellon University.

- IEA (2021): Turkey 2021, Tech. rep., International Energy Agency.

- IMF (2022): _World Economic Outlook. Countering the Cost-of-Living Crisis._ , International Monetary Fund, Washington, DC.

31
# CONTENTS

# Page

|1.|Intr|oduction|2|
|---|---|---|---|
|2.<br>-|Revi<br>adul|ew of existing information<br>on cause-specific<br>t mortality by cause|3|
||2.1|Introduction|3|
||2.2|Sou:ces of data|3|
||2.3|Methods of classification|6|
||2.4|Summary of available data|7|
|3.|Surv<br>adul|ey design for the measurement<br>of cause-specific<br>t mortality|11|
||3.1|Introduction|11|
||3.2|Single-round<br>surveys|12|
|||3.2.1 Indirect demographic<br>methods and cause-.||
|||specific mortality<br>-|12|
|||3.2.2 Selection of respondents|13|
|||3.2.3 Recall period|14|
|||3.2.4 Sampling methods|15|
|||3.2.5 Sources of error|16|
||3.3|Multi-round surveys and continous follow-up|17|
|4.|Coll|ection,<br>classification<br>and presentation<br>of||
||data|concerning cause of death|18|
||4.1|Introduction|18|
||4.2|Techniques used in previous studies|18|
||4.3|Choice of interviewer|20|
||4.4|Determination<br>of cause of death|20|
||4.5|Classification<br>of cause of death|23|
||4.6|Validation of techniques|24|
||4.7|Presentation<br>of data|25|
|5.|Reco|mmendations<br>for further research|26|
|Ack|nowled|gements<br>,|28|
|Ref|erence|s|29|
export share leads to an increase in wages, controlling for individual and firm characteristics. A 10-percentage-points increase in the export share increases wages by 0.06 percentage points. Furthermore, if the employee has been trained, the impact on wages is 0.12. Additionally, the finding that trained and educated workers employed in exporting firms obtains a significant wage-premium provides evidence of a skill-biased technological change, suggesting that a more open economic environment benefit workers. This is due to, for example, that they are more likely to be trained and, thus, to obtain higher wages. Employees working in larger firms receive less than workers in smaller firms do when the export share increases; the effect on wages is 0.11 percentage points.

**Table 11 Openness**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itj|Coef.|Std. Err.|<br>t|P>jtI|
|exportsh|0.021|0.006|3.414|0.001|0.006|0.003|1.943|0.052|
|forownsp|0.540|0.095|5.670|0.000|0.070|0.046|1.501|0.134|
|numexpat|-0.115|0.070|-1.656|0.098|0.064|0.034|1.875|0.061|
|trainexp|0.009|0.006|1.591|0.112|0.006|0.003|2.079|0.038|
|textiexp|-0.015|0.004|-4.139|0.000|-0.006|0.002|-3.226|0.001|
|forowexp|-0.019|0.004|-4.943|0.000|0.000|0.002|0.063|0.950|
|emplexp|-0.003|0.001|-2.672|0.008|-0.001|0.001|-1.986|0.047|
|nexptrao|-0.115|0.094|-1.218|0.223|-0.168|0.046|-3.664|0.000|
|nexpmeta|0.152|0.039|3.874|0.000|0.078|0.019|4.061|0.000|
|folimeta|0.328|0.058|5.674|0.000|0.110|0.028|3.897|0.000|
|nexptext|0.100|0.092|1.092|0.275|0.074|0.045|1.642|0.101|
|nexpwood|-0.496|0.286|-1.733|0.083|0.405|0.139|2.909|0.004|

Furthermore, the size of export share impacts productivity positively and it is statistically significant. Hence, firms with higher export shares are more productive due to, e.g., resources are used more efficiently. The impact on productivity is 0.21 percentage points of a 10-percentage-point increase in the export share of production. In addition, if employees are being trained the impact increase to 0.3 percentage points.

28
Husbands in households with multiple wives make between 5 to 6 decisions directly on average, which is similar to husbands who live with their parents. However, when not a decision maker, they are more likely to delegate or influence the decision indirectly than husbands in any other household.

**Table 4.** Average share of activities in which married women and men are directly engaged in the decision and share of those not directly involved but with effective power, by household type

||Average<br>activities i<br>partic|share of decisi<br>n which wives<br>ipate in the de|on-making<br>and husband<br>cision|Averag<br>activities in<br>not particip<br>|e share of decis<br>which wives a<br>ate directly in t<br>|ion-making<br>nd husbands do<br>he decision but<br>|
|---|---|---|---|---|---|---|
|**Married in couple**||||h<br>|ave effective p<br>|ower<br>|
|**households**|Wives|Husbands|Diff|Wives|Husbands|Diff|
|All decisions|0.82<br>(0.19)|0.82<br>(0.13)|-0.00<br>(0.03)|0.09<br>(0.24)|0.28<br>(0.41)|0.19***<br>(0.06)|
|Water decisions|0.82|0.58|-0.24***|0.11|0.28|0.17***|
||(0.24)|(0.30)|(0.04)|(0.29)|(0.41)|(0.08)|
|Expenditure decisions|0.69|0.93|0.24***|0.15|0.04|-0.12|
||(0.40)|(0.20)|(0.05)|(0.35)|(0.19)|(0.11)|
|Personal individual decisions|0.85|0.99|0.14***|0.09|0.00|-0.09|
||(0.23)|(0.06)|(0.03)|(0.28)|(0.00)|(0.18)|
|Observations|83|71|154|53|59|112|
|**Married in extended**<br>**households who live with**|||||||
|**wives' in-laws**|Wives|Husbands|Diff|Wives|Husbands|Diff|
|All decisions|0.66|0.71|0.05*|0.14|0.28|0.14***|
||(0.27)|(0.21)|(0.03)|(0.26)|(0.36)|(0.03)|
|Water decisions|0.67<br>(0.36)|0.45<br>(0.33)|-0.21***<br>(0.03)|0.16<br>(0.31)|0.31<br>(0.40)|0.15***<br>(0.04)|
|Expenditure decisions|0.40|0.81|0.41***|0.13|0.28|0.15**|
||(0.41)|(0.32)|(0.04)|(0.29)|(0.42)|(0.05)|
|Personal individual decisions|0.76|0.91|0.15***|0.10|0.17|0.08|
||(0.31)|(0.20)|(0.03)|(0.24)|(0.38)|(0.06)|
|Observations|235|172|407|191|149|340|
|**Married in households with**<br>**cowives**|Wives|Husbands|Diff|Wives|Husbands|Diff|
|All decisions|0.71<br>(0.28)|0.71<br>(0.21)|0.00<br>(0.05)|0.08<br>(0.20)|0.34<br>(0.41)|0.27***<br>(0.06)|
|Water decisions|0.75|0.46|-0.29***|0.05|0.33|0.27***|
||(0.35)|(0.29)|(0.06)|(0.18)|(0.43)|(0.08)|
|Expenditure decisions|<br>0.48|<br>0.72|<br>0.24***|<br>0.07|<br>0.35|<br>0.28***|
||(0.44)|(0.40)|(0.08)|(0.20)|(0.42)|(0.08)|
|Personal individual decisions|0.77|0.92|0.15***|0.04|0.15|0.11|
||(0.32)|(0.17)|(0.05)|(0.18)|(0.38)|(0.09)|
|Observations|77|47|124|59|43|102|

_Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Water decisions include water source for drinking, water expenditures, and water allocation. Expenditure decisions include water expenditures and large household purchases. Personal individual decisions include health care, visit family, and use of own income. All estimations are computed using survey weights to address sampling._ 13