rafmacalaba/datause-encoder-data huggingface dataset:
of free household labor to chop and transport fuelwood, and ownership of a wood plot, may make fuelwood an even cheaper alternative heating fuel. Female headed households and households headed by a less educated individual are more likely to use fuelwood, even when household income is controlled for.

**_<u>Table 8. Multinomial Logit Regression Results</u>_**<sup>**_1_**</sup>

|Log likelihood = -494.84|Pseudo R<sup>2</sup>= 0.377|
|---|---|
|<br>LR chi<sup>2</sup>(36) =599.03||
|Prob>chi<sup>2</sup> =0.000|Number of observations=840|

||_Fuelwoo_<br>|_Fuel Ch_<br>_d only_<br>|_oice_<sup>_2_</sup><br>_Fuelwood an_<br>|_d electricity_<br>|
|---|---|---|---|---|
|_Variables_|_Coefficients_|_Std. Error_|_Coefficients_|_Std. Error_|
|Relative price (Pwood/Pelectricity)|-1.671***|0.434|-0.514|0.505|
|Per capita expenditures|-0.007***|0.002|-0.001|0.002|
|Household size|0.206**|0.103|0.389***|0.120|
|No. children under 5|-0.008|0.221|-0.093|0.274|
|Household head education|-0.117***|0.031|-0.067*|0.036|
|Female household head|0.616**|0.310|0.386|0.389|
|Household head unemployed|0.087|0.333|-0.832|0.445|
|Household head retired|0.252|0.283|0.174|0.318|
|Family receives material|||||
|assistance|0.001|0.346|0.454|0.441|
|Roma|1.130|0.735|-0.495|1.311|
|Live in a house|2.465***|0.283|2.136***|0.330|
|Own house|0.186|0.319|0.164|0.425|
|Age of house<br>|-0.002|0.004|0.005|0.003|
|Living space (m<sup>2</sup>)|0.000|0.004|0.005|0.004|
|Household owns wood plot|1.237**|0.542|0.096|0.656|
|North region|3.431***|0.425|1.842***|0.466|
|Central region|1.338***|0.340|0.494|0.358|
|Constant|2.506|1.192|-2.651|1.408|

1The analysis focuses on choice of heating fuel, not fuel for all energy consumption. Heating constitutes the most significant share of winter energy expenditures. 2Electricity only is the base outcome for comparison.

**_How many households does the model predict will switch to fuel wood?_** Our main interest here is to predict the number of household that may switch to fuelwood as a result of electricity price increases. The multinomial regression coefficients do not directly provide such information, but we can use the model to calculate the change in predicted probability of each outcome. Table 9 shows the predicted changes in the proportion of households using electricity only, fuelwood only, or using both electricity and fuelwood., assuming an increase in electricity prices to 7c€/kWh. To assess the predictive accuracy of the model we first compare the actual proportion of household using each fuel observed in the data with the multinomial logit model’s prediction. As the first two columns of Table 9 show, the estimated model predicts the proportion of households using each alternative remarkably well.

19
21 Underreporting is a potentially serious but frequently neglected problem in the crime literature. There is often the danger that the observed relationships between crime and other variables reflect correlations with crime misreporting. It is generally difficult to deal with this problem due to the absence of independent data on crime reporting.'<sup>4</sup> In South Africa, however, the nationally representative Victims of Crime Survey (VCS) was conducted in 1998, in which households were asked whether crimes they experienced were reported to the police. Summary statistics for selected crimes are presented in Table 8. The definitions of the crime categories in the VCS differ from those in the SAPS data, and hence the figures in this table are meant to be only suggestive of possible underreporting. According to the VCS data, underreporting is a particularly serious problem for robbery and is least serious for theft of vehicles.

Measurement error in the dependent variable is a problem in regression analysis to the extent that it is correlated with some of the regressors in the model. In the case of crime misreporting, if, for example, only wealthy people reported crime or the police only filed official reports for complaints by the rich, then one would find a correlation between wealth and _reported_ crime regardless of the true relationship between wealth and crimes committed.

To examine the extent to which misreporting effects the results presented in Tables 2-7, we repeat the analysis using adjusted crime statistics. We conduct the analysis exclusively for residential burglary, which, unlike other crimes, both appears frequently in the victims survey and has nearly identical definitions in the VCS and SAPS 14 Glaeser and Sacerdote (1999) adjust reported crime rates in the Uniform Crime Reports using independent data on reporting from the National Crime Victimization Survey in the U.S.
Policy Research Working Paper 10986

# **Abstract**

The design of the power market determines how the sector can address complex economic, social, environmental, and engineering challenges to deliver sustainable and reliable electricity at least cost to consumers. This is no simple task, as supply and demand must be balanced every second, multiple resource and network constraints must be satisfied, and the market must send the correct price signals to motivate efficient generation and investment in the sector over time. There is significant heterogeneity in the electricity market designs adopted by countries globally. No “one-type-fits-all” blueprint exists, and for any design to function well, the country, political, and economic context matters. At this juncture, the experimentation continues. This paper takes stock of the global patterns and trends in the adoption of different power market design options, utilizing a unique new Global Power Market Structures Database (Akcura 2024) covering 230 economies for the period 1989 to 2024. The paths these countries have taken in structuring their power markets provide valuable lessons on the multiple models that can support the development of the power sector in different country contexts. The paper draws on global experience to shed light on promising design options for the future.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eakcura@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- 64 -

# Tabie 4.16a: 1982 EXPEND1IURES BY FUNCTION AND LEVEL OF GOVERNMENT AS PERCENT OF GDP

|In|dustrialized<br>Countries|Developing<br>Countries|LAC (a)<br>Countries|Argentina|Brazil|Chile|Colombla|Panama|
|---|---|---|---|---|---|---|---|---|
|||GE|NERAL<br>GOVE|RMIENT|||||
|EXPENDITURES<br>|44.6<br>|32.6<br>|32.1<br>|31.1<br>|39.1<br>|34.7<br>|20.8<br>|40.1<br>|
|General Services (a)|N.A.|N.A.|N.A.|2.4|6.7|5.9|3.9|11.4|
|Economic Services|N.A.|N.A.|N.A.|N.A.|6.3|3.1|5.3|5.1|
|Social Serv'ces|24.5|11.3|13.2|11.5|14.3|24.2|10.7|14.2|
|- Education|5.1|3.6|3.4|2.9|2.9|6.2|5.4|4.2|
|- Health|5.3|1.8|1.3|1.0|2.4|2.4|1.2|5.0<br>|
|-Soc. Sec.&Welfare]|||I|I|I|I|I|I|
|<br>-Housing&Comrunityl|14.2|5.8|1 8.4|17.4|] 9.0|1 15.6|1 4.0|l 5.1|
|Amenities|||1|1||I|I||
|SocialExpenditures|||||||||
|asSof Total Expend.|(55.0)|(34.6)|(4,.0)|(37.0)|(36.5)|(69.7)|(51.4)|(35.4)|
|||CE|NiRAL GOVE|RMfENT|||||
|EXPENDITURES|34.1|27.2|27.2|22.7|27.7|31.2|15.1|39.4|
|General Services (a)|.7|.8|.6|1.6|2.5|3.7|<br>2.8|10.9|
|Economic Services|3.1|5.8|5.2|3.6|4.6|_3.1_|4.3|5.1|
|Social Services|18.4|7.7|9.7|7.6|10.0|22.8|7.1|14.0|
|- Education|1.4|2.3|2.3|1.3|1.0|5.0|3.1|4.1|
|- Health|3.8|1.0|1.2|.2|1.6|2.3|.6|5.0|
|-Soc. Sec.&Weltdre<br>|12.4<br>|3.9<br>|5.8<br>|5.8<br>|7.4<br>|14.3<br>|3.0<br>|3.1<br>|
|-Housing&Community<br>Amenities|.8|.5|.4|.2|.1|1.1|.4|1.7|
|Social ExpendItures|||||||||
|as%of Total Expend.|(54.0)|(28.1)|(35.5)|(33.4)|(36.1)|(72.9)|(47.4)|(35.4)|
|||SUBN|ATIONAL<br>GOV|ERNMENT|||||
|EXPEND1lURES|10.5|5.4|4,8|8.4|11.4|3.5|5.7|.7|
|General Services (a)|N.A.|N.A.|N.A.|.8|4.2|2.2|1.1|.4|
|Economic Services|N.A.|N.A.|N.A.|N.A.|1.8|N.A.|.9||
|Soclal Services|6.1|3.6|3.!|3.9|4.3|1.2|3.5|.2|
|-Education|3.6|1.3|1.1|1.6|1.9|1.2|2.4||
|<br>-Health|1.5|.8|.2|.9|.8|.1|.6||
|-Soc. Sec.&Welfarel|I|I|1|1.4|1.0|.-|.2||
|-Housing&Communityl|I1.0|1 1.5|1 2.2|.-|.6|.-|.3|.2|
|Amenitles<br>I|1|l|||||||
|Social ExpendItures|||||||||
|asS of Total Expend.|(58.3)|(67.2)|(71.8)|(47.0)|(37.5)|(36.0)|(62.0)||

Source: Staff elaborat!on of data from IMF, Government Finance Statistics Yearbook, Volume X, 1986 Expenditure totals Include Lending Minus Repayment and Transfer to Other Levels of Government. Expenditures data for specific functions exclude net Lending flows.

(a) LAC reglonal averages kIghted against GDP on a wider sample of countries. (b) Includes General Administration and Public Order but excludes Defense expenditures.
good indicator of the stance of monetary policy. The overnight rate, however, was instead chosen to proxy for changes in the stance of monetary policy (a monetary policy indicator) because sufficient time series of the RP rate were not available and the recent movements in the call rate have been very much similar to those in the RP rate."<sup>2</sup> Of particular relevance for our analysis is to exploit the December 1997 policy episode which appears to adequately capture apparently severe contractionary shift in monetary policy. This exogenous episode of tighter policy would provide more insightful information about whether the bank lending channel operating mainly due to the presence of credit market imperfections in the form of borrowing restrictions has played an important role in propagating the real effect of the monetary policy shock not captured by the traditional interest rate channel. Both consumer price index _(CPI)_ and the industrial production index (y ) were selected to control for macro conditions. One class of primary assets (bank loans and security holdings) of each bank group was considered in the VAR analysis. Data used were logarithms of the nominal level data of all variables considered except the call rate.

For estimation purpose, the following standard finite vector autoregressive (VAR) representation is considered: where A,is a square coefficient matrix; utdenotes the vector of mean-zero, serially uncorrelated variables, and its contemporaneous variance matrix, _Q,_ is assumed to be positive definite symmetric; a is a 4xl column vector of parameters; the vector Xit include three observable random time series variables under consideration, i.e., the index of industrial production (yj,, consumer price index _(Cpi,,),_ lending volume _(L,,)_ (or securities holdings (S,,)) and one of the call rate and the December 1997 policy episode as a monetary policy indicator ( _M, )._ We estimated the vector autoregressive representation (7) in the first difference to reflect the preliminary unit root test results indicating the time series variables being considered are integrated of order one. A lag length of three was selected and the BoxPierce Q-statistics at that length were consistent with the evidence that the error term of each reduced-form equation is white noise within at least 10 percent level. Furthermore, it seems reasonable to focus on the VAR analysis containing only four variables of our particular interest in light of the practical consideration such as degree of freedom as much as the potential cost of misspecification involved in selecting further lag length and variables.'<sup>3</sup> So this rather parsimonious VAR specification seems quite adequate. A

> 12 Note that the contemporaneous correlation of these two series( RP rate and call rate) during the period of January 1996 through May 1998 turns out to be 0.99.

> 13 Estimating the coefficient on 4 lags of four variables in the VAR specification by adding another variable with this short sample would pose a problem of exhausting all degree of freedom.

16
|_Table3: Household characteristics of households i_|_n HBS (20_<br>(1)|_19) and EU-_<br>(2)|_SILC (2020)_<br>(3)|_– potential ma_<br>(4)|_tching vari_<br>(5)|_ables_<br>(6)|
|---|---|---|---|---|---|---|
||HBS|||EU-SILC|||
|VARIABLES|N|Mean|sd|N|mean|sd|
|Household with children (<15)|2,952|0.166|0.372|7,313|0.230|0.421|
|Household with pensioner|2,952|0.557|0.497|7,313|0.482|0.500|
|Households with elderly (>64)|2,952|0.532|0.499|7,313|0.414|0.493|
|Female-headed household|2,952|0.441|0.496|7,313|0.495|0.500|
|Household with unemployed|2,952|0.131|0.338|7,313|0.124|0.329|
|Urban|2,952|0.733|0.442|7,313|0.687|0.464|
|Income cat. (1-20)|2,952|10.50|5.767|7,313|8.669|5.833|
|No of female|2,952|1.179|0.745|7,313|1.043|0.656|
|No of self-employed|2,952|0.0717|0.285|7,313|0.101|0.352|
|No in the primary sector|2,952|0.0513|0.276|7,313|0.0706|0.284|
|No of part-time employed|2,952|1.587|1.808|7,313|0.0521|0.247|
|No with primary educ.|2,952|0.339|0.818|7,313|0.101|0.424|
|No with secondary educ.|2,952|1.372|1.031|7,313|1.398|1.111|
|No with tertiary educ.|2,952|0.454|0.713|7,313|0.876|1.115|
|No attending educ.|2,952|0.281|0.634|7,313|0.159|0.410|
|No of foreign citizens|2,952|0.00365|0.0745|7,313|0.0113|0.149|
|No of children|2,952|0.240|0.595|7,313|0.345|0.734|
|No of pensioners|2,952|0.733|0.744|7,313|0.612|0.707|
|No of unemployed|2,952|0.167|0.482|7,313|0.151|0.447|
|No. of household members|2,952|2.165|1.216|7,313|2.375|1.463|
|Adult equivalent|2,952|1.832|0.961|7,313|1.624|0.639|
|Northern and Eastern Bulgaria|2,952|0.499|0.500|7,313|0.502|0.500|
|South-West and South-Central Bulgaria|2,952|0.501|0.500|7,313|0.498|0.500|

_Notes: The indicators in this table are at the household level. We weigh each observation by household survey weights._ _Source: Own estimates based on HBS (2019) and EU-SILC (2020)._ 22
32% of the population of SSA. Third, the evidence we present is timely and supersedes potentially outdated information on issues such as vaccine hesitancy from before the start of vaccination for the general population in SSA.<sup>14–16</sup> Fourth, our analysis is comprehensive and provides microlevel evidence on all five dimensions of vaccine hesitancy, _confidence, complacency, convenience, communication,_ and _context,_ lastmile delivery barriers, and potential strategies to reach those who remain unvaccinated.<sup>17</sup> These insights come at a crucial moment for vaccination campaigns which have struggled to pick up pace.

Based on our findings, we elaborate policy options to turn vaccines into vaccinations in Sub-Saharan Africa.

# **Methods**

## **Data**

We use data from High Frequency Phone Surveys (HFPS) in five countries in East and West Africa – Burkina Faso, Kenya, Malawi, Nigeria, and Tanzania. The surveys were conducted by countries’ national statistical agencies in collaboration with the World Bank. Since 2020, the HFPS have collected crosscountry comparable longitudinal data on a wide range of topics, including the impacts of COVID-19 on households and individuals.

Some rounds of the HFPS include a module on COVID-19 vaccination. The data we use in this study was collected between November 2021 and May 2022: Burkina Faso (April-May 2022), Kenya (November 2021-March 2022), Malawi (February 2022), Nigeria (December 2021 – January 2022), and Tanzania (December 2021).

## **Sampling and sample representativeness**

The HFPS are re-contact surveys with national scope whose samples are drawn from nationally representative samples of households interviewed in recent in-person surveys. In Kenya, an additional sample of households was drawn via random digit dialing. Phone survey samples may not be fully representative of the general population, because of limited mobile phone coverage, non-response, and attrition, especially in lower-income contexts. To mitigate these concerns, sampling weights were recalibrated using propensity score and post-stratification methods, which were shown to mitigate sample selection biases in the HFPS.<sup>18–20</sup> In each household, one main respondent over the age of 15 was interviewed, who was selected to be knowledgeable of the affairs of the household and its members to provide reliable responses, though this selection overrepresents certain population groups.<sup>20</sup> Table A1 in the supplementary files summarizes the resulting sample sizes for each country included in our study.

## **Survey instrument and variables**

A harmonized survey module on COVID-19 vaccination was implemented in all five study countries. To study vaccine hesitancy and uptake in a systematic and comprehensive manner, we distinguish three groups in the population: (i) those already vaccinated, (ii) those who are willing to get vaccinated but are yet unvaccinated, and (iii) those who are hesitant to get vaccinated. We define hesitancy in our data as those 3
Costs of attendance play a role in hindering uptake. Private preschools are funded by charging fees. As an alternative, parents enroll their children earlier than the recommended age of 7 in the free-of-charge public primary schools (Jung and Hasan 2014). Appendix Table 10 tabulates the average annual cost of attending private and public preschools in 2000. Means and standard deviations are significantly higher in private compared to public preschools, which might signify both higher use and higher variability of resources among private preschools.

Recognizing the importance of ECED, the Government of Indonesia adopted it into the national education system in 2003. The medium-term plan between 2004 and 2009 envisioned an increase in ECED enrollment. The Ministry of Education and Culture offer a block-grant subsidy to encourage the private provision of ECED services: funds are directly channeled to the providers from the central government. Public expansions are harder to achieve. Following decentralization reform in 2001, districts are responsible for financing, implementing, and supervising ECED services, while the central government is responsible for developing quality assurance mechanisms (Chang et al. 2006).

Other ECED services, such as daycares and nurseries (PAUD<sup>3</sup> ), may also affect mothers’ work participation. We focus on preschools due to limited data availability. Figures 1 and 2 illustrate the geographical variation of public and private preschool access across the country. Figure 3 shows the upward trend in both public and private preschool access over the years, notably since the passing of the National Education System Act (NSEA) in 2003. In the next section, we elaborate how we exploit spatial and temporal variations in public and private preschool access to estimate the elasticity of maternal employment to preschool access.

# **3. Data**

The Indonesia Family Life Survey (IFLS) is a longitudinal household survey, first conducted in 1993, with subsequent tracking of the original and split households in 1997, 2000, 2007, and 2014. It was first fielded in 13 (of 27<sup>4</sup> ) provinces back in 1993, which represented 83 percent of the national population (Frankenberg et al. 1995). It has notably high re-contact rates, with 87.8 percent of households surveyed in 1993 being successfully tracked or confirmed dead in 2014 (Strauss, Witoelar and Sikoki 2016).

> 3 PAUD stands for _Pendidikan Anak Usia Dini_ . It is a non-formal ECED service catering to children aged 2 to 6.

> 4 At the time, Timor-Leste, now an independent country, was one of the Indonesian provinces.

6
using 22 years of data, the income and the assets of the poor who were affected by natural disasters were significantly lower over the longer term than those of the poor who were not affected by them (Pleninger 2022). In Ethiopia and Honduras, natural disasters pushed poor households into poverty traps in which recovery from natural disasters took longer than it did among other households (Carter et al. 2007).

At the aggregate, Dell, Jones, and Olken (2012) find that higher temperatures reduce economic growth in poor countries <mark>. The channels include lower</mark> agricultu <mark>ral and industrial output, and more political instability. Additionally, they find that the adverse effects of temperature are concentrated in poor countries. These results raise the concern of a reduced ability to grow among poor countries, which can have medium- and long-term consequences. Indeed, evidence from typhoons suggests that climate shocks reduce per-capita income, and this effect persists up to 20 years</mark> (Hsiang and Jina 2014 <mark>). These persistent income losses following climate shocks pose the risk of poverty traps.</mark>

# _3.4.2 Mortality and human capital losses_

Climate shocks generally increase mortality and reduce the human capital of survivors (Carleton and Hsiang 2016; Baez 2010). Indeed, about 80 percent of studies that focus on human capital find that the poor are more adversely affected than the non-poor (Table 5). <mark>Deschenes (2009) estimates an immediate increase in mortality rates in the United States following extreme weather events.</mark> Poorer countries suffer more deaths from climate shocks, while higher economic development and countries with higher quality institutions suffer less death (Kahn 2005). <mark>Among those who survive, climate shocks can still negatively affect education and health. Abiona (2022) analyzes the impact of droughts on birth outcomes in rural Sierra Leone, and finds the adverse effects concentrated among poorer households, with some evidence for maternal nutrition and gestation as pathways. Hoddinott (2001) examines the impact of a drought in Zimbabwe and finds that exposed children are on average shorter than peers who were unexposed to the drought.</mark> There is a large and growing literature on the effects of climate shocks on human capital outcomes, including studies on the persistence of early-life shocks (Currie 2016; Hanna and Oliva 2016). While most of these studies are beyond the scope of our analysis, the interaction between mortality 11