rafmacalaba/data-use-mentions-extended huggingface dataset:
_poverty is the at-risk poverty rate using the income measure reported in SILC, and therefore, corresponds to the official poverty measure. Source: Own estimation based on a harmonized, synthetic dataset consisting of HBS (2019) and EU-SILC (2020)._

**The overlap between both concepts varies significantly by the metric of energy poverty used** . This reflects that energy poverty is a complex concept that lacks a common definition and is multidimensional by nature.<sup>25</sup>

# **IV. Conclusion**

**In this paper, we explore the potential of statistical matching techniques to combine information gathered on households' living conditions and welfare with information on households' energy expenditure from separate surveys.** More concretely speaking, we apply data fusion methods to the EU-SILC 2020, with income reference year 2019, and the HBS 2019 in Bulgaria to ultimately add information on energy spending shares to the EU-SILC. Statistical matching is necessary in this case, as traditional data linkage is not possible given that households sampled into the HBS and EU-SILC do not coincide. We first determine several overlapping variables that can serve as matching variables. We next choose the set of relevant matching variables via lasso regressions. Based on these variables, we generate a harmonized and concatenated dataset containing information from both HBS and EU-SILC data. We then apply three different imputation models to impute energy spending shares for households from the EU-SILC.

**We choose the best-performing model by analyzing the distribution of imputed and observed energy spending shares.** Our results show that weighted PMM imputation models generate the closest distribution of energy spending shares within the set of weighted imputation models. We verify this result by a number of robustness checks. We choose the imputed values from the weighted PMM to generate imputed energy spending shares for households in the EU-SILC.

> 25 For the detailed discussion on energy poverty measurement, see Robayo-Abril and Rude (forthcoming b).

31
the difference between the inequality (or poverty) indicator without the transfer and with it. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

**The changes in the heating allowance are particularly noteworthy in this scenario.** This is primarily because the higher threshold for the Monthly Social Allowance (MSA) now extends its coverage, allowing a more substantial number of households to access the benefit. As a result, the heating allowance's benefits become perceptible for households in higher income deciles, reaching up to the fourth decile when considering market income plus pensions (Figure 10, panel a).

**Notably, the distribution of these benefits is relatively equitable for the first three income deciles, ensuring that households across these groups receive similar levels of assistance (Figure 10, panel b).** At the same time, the generosity of the benefits demonstrates an increase, particularly for the first three deciles (figure 10, panel c). In the first decile, this increase exceeds 10% of the household's income, significantly boosting their financial well-being. However, despite the apparent pro-poor concentration of these benefits (as indicated by the negative concentration coefficient), the relatively low incidence or generosity of the heating allowance (equating to 0.26% of market income plus pensions) contributes to the lack of any significant change in income inequality. On the other hand, when considering its impact on poverty, this scenario exhibits a relatively modest marginal effect. Although the marginal contribution

28
generally focused on one or two cities (Avner and Lall 2016; Chen et al. 2017; Campbell et al. 2019).

This paper is a first attempt to fill this gap. It brings together a number of data sets on the distribution of economic opportunities,<sup>2</sup> people and the characteristics of the public transport in 11 African cities to compute baseline public transport accessibility metrics in a consistent manner. In doing so it creates a benchmark for accessibility that allows for comparisons between cities and produces visuals and descriptive statistics for each city. In producing this baseline, the paper also sets the basis for further analysis that could investigate the impacts of transport or land use modifications.<sup>3</sup>

# **2 WHY FOCUS ON ACCESSIBILITY? A SHORT LITERATURE REVIEW**

Starting with Kain’s spatial mismatch hypothesis (Kain 1968), there has been a vast literature investigating how accessibility to employment opportunities<sup>4</sup> could affect employment, wages, productivity and well-being in urban areas. Initially focused on African-Americans’ employment outcomes in US urban areas, the research has broadened to investigate the role of space and accessibility first for all minorities and then for all individuals’ outcomes in general. While the literature is still massively geared toward developed country cities, some studies focusing on urban areas in developing countries are emerging.

From a theoretical standpoint, there are several reasons why increased accessibility could reduce unemployment or increase the quality of individuals’ employment (for a detailed review of theoretical explanations for the spatial mismatch, see Gobillon et al. (2007)). In this paper the rationale for focusing on accessibility is underpinned by the following theoretical framework: higher accessibility implies lower job search costs which i) incentivizes a more intensive job search and improves the probability of finding a job and/or ii) for a given job search intensity will improve the probability of finding a job that matches a worker’s skills and aspirations – the quality
Khandker, 1998; Zaman, 1999).<sup>3</sup> On the other hand, access to public safety nets is not a significant determinant of the household‟s assessment of whether they are able to cope or not. This result is not surprising since except for a few (e.g. Vulnerable Group Feeding and Gratuitous Relief) most of the safety nets are designed to mitigate the effects of chronic poverty and are not catered to mitigating the effects of shocks. These results need to be interpreted with caution as we do not account for the fact that those who access these services – both microfinance and safety nets – may have different unobservable characteristics relative to those households who do not (the „selection bias‟ problem).

Household poverty status is also a significant determinant of the ability to cope. We had earlier found poor households to be less likely to have experienced one or more shocks. Conditional on facing a shock however, we find that poor households are more likely to not be able to cope. This result is consistent with the results we find with regards to the division dummy variables: households living in poorer divisions are less likely to cope with shocks. Relative to households living in Sylhet, the results show that households in Rajshahi, Khulna and Barisal are more likely to not be able to cope with shocks. The results also show that households whose major source of income was from agriculture are more likely to not be able to cope relative to households whose major source of income was from casual labor. The results also show that households are more likely to be able to cope with health shocks but not climatic and asset shocks.

# **5. Conclusion**

We can summarize our main findings as follows. First, shocks of various forms affect a large share of Bangladeshi households – our survey of 2,000 households showed that more than half the sample claimed to have faced a shock over the previous year. Surprisingly, the non-poor face a larger share of
might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. We include 131 countries in this development accounting exercise.<sup>9</sup> Table II shows our results in comparison to Schoellman (2011), Hall and C. Jones (1999) and Hendricks (2002). We find that when our human capital measure only captures quantity ( _w_ = 0), the share of human capital accounts for roughly 9-26 percent of output per worker differences.

> 8 These values are based on Psacharopoulos and Patrinos (2004) and Hanushek and Zhang (2009) respectively. 9 This includes all countries which have both Barro-Lee data as well as learning data.

12
**_Access. Road_** _density in terms of population (road-kilometers per 1,000 people)_ is twice the average for low income countries and higher than Middle East & North Africa, and East Asia Pacific. However, at 6.1 road-km per 1000 people it is only a third of the density of high income OECD countries and two-thirds of upper-middle income countries. Interestingly, Brazil has significantly higher road density in terms of population (10), while Indonesia and Malaysia score lower (1.7 and 2.8).  Road _density in terms of land (road-kilometers per 1,000 sq km)_ is low (227) when compared with many other groups and is only higher than that of SSA (155) and low income countries (181). It is much lower than in benchmark upper middle-income countries (328).  At the same time, it is important to recognize that these road density indicators for South Africa may be somewhat misleading in comparative perspective because of the specific territorial configuration of population density that is concentrated in comparatively small parts of an otherwise large national territory. To the extent that South Africa has lower than average population density and greater differences in the concentration of population and economic activity (parts of the country are very poorly inhabited with very little or no commercial activity) in different regions, the indicator may overstate the gap with comparator countries. For example, South Africa performance on density in terms of land appears similar to Brazil (203 road kilometers per 1,000 sq km)), another resource rich, coastal country with vast segments of the territory sparsely populated; similarly Indonesia and Malaysia also have very similar road density in terms of land (203 and 200 road kilometers per 1,000 sq km, respectively).<sup>9</sup> **_Rail_** _lines density in terms of population_ , by contrast, is very high when compared to SSA, Middle East & North Africa, South Asia, East Asia Pacific
# **Does Democratization Promote Competition?**

# **Evidence from Indonesia**<sup>**†**</sup>

Mary Hallward-Driemeier<sup>*****</sup> Anna Kochanova<sup>**</sup> Bob Rijkers<sup>***</sup> Originally published in the Policy Research Working Paper Series on _January 2020._ This version is updated on _February 2021._ To obtain the originally published version, please email prwp@worldbank.org.

## **JEL** : L11, O38, O47, N45, P16, P26, L60

**Keywords** : Corruption, cronyism, political connections, firm dynamics, Indonesia, manufacturing, state– business relationships, political turnover, democratization

> * Corresponding author: Bob Rijkers, The World Bank, 1818 H Street, N.W., Washington, DC 20433. Phone: +1(202) 4730426. E-mail: brijkers@worldbank.org.

† We are very grateful to Ahmed Mushfiq Mobarak and Denni Purbasari for sharing their data on political connections to Suharto with us. We would like to thank Nicolas Gomez Parra and Nicolas Santos Villagran for excellent research assistance. Research for this paper has been supported in part by the Knowledge for Change Program, the Multidonor Trust Fund for Trade and Development, and the Strategic Research Program of the World Bank. It also benefitted from funding from the World Bank’s Research Support Budget. 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.

> * The World Bank, 1818 H Street, N.W., Washington, DC 20433. Phone: +1(202) 473-9120. E-mail: mhallward@worldbank.org.

**
is predicted by the analysis above not to affect FAI into _h_ , so that _βMP_ = 0; but we have noted too that there may be circumstances in which there might be some (likely positive) effect. For the interaction term, the nonlinearity in (4) suggests that _βM_ ∆ _>_ 0: as noted earlier, a tax differential ∆ _ph_ that is more favorable to investment in _h_ dampens the adverse impact of a higher host marginal effective rate _Mh_ . Sufficiency of the _I_ - _METR_ in characterizing real investment decisions, as implied by Proposition 1, corresponds to the restrictions _β_ ∆ = _−βMH_ = _βM_ ∆ = _−β_ ∆2 and _βMP_ = 0.

> 32For this purpose, the bilateral stock of FAI is imputed using the perpetual inventory method, using an assumed depreciation rate of 0.195. The results this gives for the US are very close to the direct reports in the BEA data, giving some confidence that the estimates are reasonably accurate.

> 33In the presence of complementarities, corresponding to negative weights _δhjp_ , such an increase might arise from a reduction in statutory rates; in the empirics that follow, however, the weights will all be non-negative.

17
Table A5: Consistency in Responses on Sensitive Information with Additional Surveys _(Including education level as control variable)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Cons|istencyw|ith Baselin|e Survey|Cons|istencyw|ith Endlin|e Survey|
||Stress|high|Physical<br>is ef|punishment<br>fective|Stress|high|Physical<br>is ef|punishment<br>fective|
|Phone survey|0.030|-0.071|-0.064|-0.114|0.073|-0.087|-0.075|-0.017|
||(0.079)|(0.142)|(0.074)|(0.127)|(0.090)|(0.157)|(0.089)|(0.158)|
|Mean of Dep.Var (WhatsApp)|0.574|0.574|0.735|0.735|0.566|0.566|0.642|0.642|
|R-squared|0.142|0.147|0.171|0.177|0.173|0.188|0.156|0.178|
|Observations|207|207|207|207|169|169|169|169|
|Individual Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Interviewer FE|No|Yes|No|Yes|No|Yes|No|Yes|
|Municipality FE|Yes
**Figure 3.** Electricity Emission Intensity

<!-- Start of picture text --> Water Distribution<br>Posta l services<br>Agriculture<br>Air transport<br>Manu f .-Pla stics<br>Manuf.-Chemicals<br>Retail<br>Manu f .-Apparel<br>Manu f .-Machinery<br>Construction<br>Water transport<br>Storage/wa rehouse<br>Manu f .-Me tal products<br>Land t ransport<br>Wholesale<br>Manuf.-Cement<br>Manu f .- Editing and recording<br>Manu f .-O thers<br>Vehic l e/ f u el sales<br>M anuf.-Textiles<br>0 .2 .4 .6 .8 1<br><!-- End of picture text --> Note: The bar plot shows the average emissions intensity from electricity consumption by economic sector. Emissions intensity is computed as total emissions by electricity consumption divided by value added. Data are from DGII and SIEN and are relative to 2018. Dotted blue lines refer to standard deviations within sectors.

**Figure 4.** Fuels Emission Intensity

<!-- Start of picture text --> Air transport<br>Land t ransport<br>Manu f.-Cement<br>Water tran sport<br>Stora ge/warehouse<br>Agriculture<br>Posta l services<br>Ma nuf.-Textiles<br>M anuf.-Chemicals<br>Manuf.-Metal products<br>Vehicle/fue l sales<br>C onstruction<br>Manuf.-Plastics<br>Manuf.-Machinery<br>Wholesale<br>Manuf . -Apparel<br>Manuf.-Others<br>Manuf.-Editing and recording<br>Water Distribution<br>Retail<
eve of the outbreak, Sierra Leone ranked near the global bottom of a gender inequality index.<sup>2</sup> Relative to the Sub-Saharan Africa average, it has high rates of adolescent fertility (Panel B) and the highest rate of maternal mortality in any country for which data exists (Panel C). This is partly driven by the extremely low levels of public health care provision (Panel D): pre-crisis there were 0 _._ 2 doctors and 3 nurses per 10 _,_ 000 people (the corresponding figures for most OECD countries are 30+ doctors and 100+ nurses), in a country with an estimated 1 _._ 4 million women of child-bearing age and 1 _._ 1 million under-five children. According to the WHO, teen pregnancy is one of the leading causes of death for mothers in Sierra Leone and illegal abortions are common. It is also a setting where there is a high prevalence of sexual exploitation and violence towards young women. For example, over half of women in Sierra Leone (56%) report having suffered some form of gender based violence during their lifetime [Amnesty International 2015].<sup>3</sup> It was in the context of pre-crisis Sierra Leone that our data collection exercise was originally planned. This was for a randomized control trial evaluation of an intervention to empower young women along economic and social dimensions, building on our earlier work evaluating the same intervention in Uganda [Bandiera _et al._ 2018].<sup>4</sup> The program, known as the Empowerment and Livelihood for Adolescents (ELA) intervention, is delivered by the NGO BRAC. It provides clubs in which young women can meet, and offers a package of skills training. This training includes life skills, such as awareness of health and reproductive issues; financial literacy; and vocational skills. Older girls can also access microfinance to start micro-enterprises.

Fieldwork for our baseline survey was completed a week prior to the first
# Labor: Overwhelming Demographic Challenges

**The degree of social protection European workers enjoy in the form of employment protection and social benefits is significantly higher than in any other developed region.** The generosity of the social protection system allows Europeans to maintain a balance between work and leisure. While this pillar of the European growth model has obvious benefits for workers, it discourages labor participation and is a burden on the public finances. Europeans do indeed work less and retire earlier than Americans. Over the last four decades the effective retirement age in Europe has systematically declined in Europe even though life expectancy has risen significantly. Relatively low labor mobility adds to the problems Europe faces in achieving efficient allocation of labor within and between countries.

**EU11 social protection indicators do not seem to be systematically different from those in the rest of Europe.** Though EU11 employment protection laws tend to be less stringent than elsewhere in the EU, its market protection indices have been converging with those of the EU15 in recent decades, both because Western European economies have adopted more liberal labor market policies and because EU11 countries have increased protection.

**Figure 15. Employment Protection Index**

**Figure 16. Hiring and Firing Practices Index**

<!-- Start of picture text --> OECD employment protection index Hiring and firing practices index, 2010<br>4.00 7.0<br>3.50 6.0<br>3.00<br>5.0<br>2.50<br>4.0<br>2.00<br>3.0<br>1.50<br>2.0<br>1.00<br>0.50 1.0<br>0.00 0.0<br>Source: OECD Indicators of Employment Protection Source: World Bank Doing Business<br>Note: Scale from 0 (least stringent) to 6 (most restrictive)<br>
back-of-the-envelope approach as described below.

We first source geo-located food product price data (Development Data Group, 2021) via the World Bank Micro-data Library API. Our API query returns local currency (nominal) prices for all African countries with production gains for each crop and each market between 2007 and 2015 ( _πcm_<sup>_nom_).Thisallowsustoincreasethenumberofmarket-level</sup> prices available within each country and smooth over sudden price shocks. We apply the corresponding national monthly consumer price index (CPI) and PPP conversion factors from the World Bank’s Poverty and Inequality Platform (PIP) (Aron et al., 2023) to convert to real monthly average prices in 2010 USD for each crop in each market.

We then perform a spatial merge of the grids with production gains to the nearest market from the food producer price database. Consequently, the prices that apply to specific crop production gains in each grid are those from the nearest markets in the food 13
annual poverty and welfare statistics at the division level for urban and rural areas. A potential challenge is that the optimal sample for objective (i) is not necessarily identical to that needed to achieve objectives (ii) or (iii). Therefore, the design of the sample requires careful thinking on how to achieve the multiple objectives, which are not necessarily mutually consistent, within a single sample. This paper describes how the sample of the HIES 2016/17 was designed to achieve the aforementioned three objectives.

This paper also reviews the formulas that are often used by NSOs and development practitioners to calculate the sample size and the optimal allocation of the sample across strata. The HIES 2016/17 follows a stratified clustered sampling. Clustered sampling is used when the sample is selected in multiple stages. That is, in a first stage, clusters of households or individuals are selected randomly and then households or individuals are selected from the clusters in a second stage. This is less efficient than simple random sampling (selecting household or individuals in one stage directly) in terms of minimizing sampling errors, but it is more cost-effective as selected households or individuals are physically located relatively close to each other in the same cluster. This paper shows that the standard formulas used by practitioners for defining the sample size and the allocation of the sample across strata assume stratified random sampling rather than stratified clustered sampling. As a result, the sample size and the allocation of the sample can be too optimistic in terms of minimizing the sampling errors.

This paper is organized as follows. Section 2 reviews key literature on optimal sample allocation for stratified random samples and stratified clustered samples. Section 3 describes objectives of the survey, data used to design the HIES 2016/17 sample, sampling frame, stratification and domains of study, selection of target variables and accuracy statistics for sample performance evaluation, determination of the sample size, and allocation of Primary Sampling Units (PSUs) across strata. Section 4 summarizes evaluation of the proposed sampling design. Section 5 analyzes