rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7844

# **Abstract**

This paper presents a new demographic profile of extreme and moderate poverty, defined as those living on less than $1.90 and between $1.90 and $3.10 per day in 2013, based on household survey data from 89 developing countries. The face of poverty is primarily rural and young; 80 percent of the extreme poor and 75 percent of the moderate poor live in rural areas. Over 45 percent of the extreme poor are children younger than 15 years old, and nearly 60 percent of the extreme poor live in households with three or more children. Gender differences in poverty rates are muted, and there is scant evidence of gender inequality in poor children’s educational attainment. A sizable share of the extreme and moderate poor, 40 and 50 percent, respectively, have completed primary school. Compared with the extreme poor, the moderate poor are significantly more likely to have completed primary school and are less likely to work in agriculture. After conditioning on other individual and household characteristics, having fewer than three children, having greater educational attainment, and living in an urban area are strongly and positively associated with economic well-being. The results reinforce the central importance of households in rural areas and those containing large numbers of children in efforts to reduce extreme poverty, and are consistent with increased educational attainment and urbanization hastening poverty reduction.

This paper—prepared as a background paper to the World Bank’s _Poverty and Shared Prosperity Report 2016: Taking on Inequality_ —is a product of the Poverty and Equity Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at dnewhouse@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange
where there was no autonomous regulator, laws for the reform of the ESI Including regulatory reform were being passed. Table 2 shows the regional distribution of electricity reform laws for those states without autonomous regulation. According to the Domah data, all the countries with autonomous regulators had enacted an electricity regulatory law. By the end of our sample period only two countries in the sample (Barbados and Indonesia) did not have any electricity regulatory law in place.

These laws sometimes provided for IPPs or other elements of market reform, for commercialisation and sometimes for unbundling and competition in generation and supply<sup>23</sup> . If the laws covered regulation, they typically specified the powers and duties of the Ministry (or designated Ministry agency/department) in carrying out regulatory functions.

> 22 The Domah questionnaire used the term “autonomous” rather than “independent”, not least because it is more neutral.  We treat the two terms as synonymous.

> 23 However, the actual introduction of competition and/or privatisation took place at some later date, typically with several events at different times.  This is why we cannot within this data set obtain good indicators for privatisation and competition.

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Figure 4.5: _The tax burden becomes a problem to firm survival and growth as countries advance in their transition to a market economy_ <!-- Start of picture text --> tax revenue, % GDP<br>45<br>40 36 38<br>35 32 34<br>3025 22 22 23 23 24 24 24 25 26 26 27 27 28 28 28 30<br>19 19<br>20 17<br>15 12<br>10 10<br>10<br>5<br>0<br>Georgia Kazakhstan Kyrgyz Rep. Azerbaijan Moldova CIS Ukraine Russia Lithuania Romania Latvia ECA Turkey Bulgaria Poland SEE Belarus CEE Estonia Europe US Slovak Rep. Czech Rep. Hungary Slovenia Croatia<br><!-- End of picture text --> Source: World Bank, World Development Indicators (2002) Figure 4.6: _ECA countries lag behind as regards the efficiency of independence of the judicial system and protection of property rights_ <!-- Start of picture text --> Legal structure and security of property rights<br>10<br>9 8.2 8.7<br>87 6.5 6.6 6.7 7.0<br>6 4.6 4.8 4.9 5.1 5.2 5.4 5.6 5.7 5.8 5.8<br>5 3.8 4.0 4.1 4.2 4.6<br>4<br>3<br>2<br>1<br>0<br>Russia CIS Turkey Ukraine Albania Romania Bulgaria SEE Lithuania Croatia ECA Slovak Rep. Latvia Poland CEE Estonia Czech Rep. Hungary Slovenia Europe US<br><!-- End of picture text --> Source: Economic Freedom of the World (2003) 38
# **1. Introduction**

Bangladesh has seen robust economic growth during the last decade, with real GDP growing by more than 6 percent on average every year during 2006 – 2015. Additionally, the economy has seen a long‐term acceleration in growth since the late 1970s when the real GDP was growing by just over 4 percent annually. The acceleration in real GDP growth has also translated to an acceleration in real GDP per capita growth as the gap between real GDP growth and population has been widening over time.<sup>2</sup> Beginning from $352 in 1980, real GDP per capita (measured in 2010 US dollars) crossed $700 in 2008 and is on mark to cross $1,000 next year. The sustained economic growth during the last few decades has helped the country graduate from a low‐income country to a lower‐middle‐income country as classified by the World Bank (World Bank, 2015).

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>‐6<br>GDP GDP per Capita<br>Source: World Development Indicators<br>Average Annual Growth Rate (%)<br>1961 ‐ 1965 1966 ‐ 1970 1971 ‐ 1975 1976 ‐ 1980 1981 ‐ 1985 1986 ‐ 1990 1991 ‐ 1995 1996 ‐ 2000 2001 ‐ 2005 2006 ‐ 2010 2011 ‐ 2015<br><!-- End of picture text --> To better understand the sustained growth achieved by the country, it is useful to explore how some of the key growth drivers have evolved during this period. Capital accumulation has been a key driver of economic growth for many countries and Bangladesh is no exception to this phenomenon. Bangladesh has realized continued rise in the investment share of GDP during the last 35 years. The investment share of GDP has almost doubled from 14.4 percent
behavior in studies from high-income countries was a statistically insignificant 0.07 sd. Studies conducted in low- and middle-income countries did not collect this outcome.<sup>2526</sup>

> 25 In high-income contexts, the largest effect sizes, both positive and negative, come from a US study reporting a statistically significant improvement in behavior of 1.06 sd to -0.77 for externalizing behavior following a teacher intervention to reduce children’s behavioral problems (Raver et al., 2009). In low- and middle-income contexts, standardized effect sizes range from a significant 0.45 sd on a social composite index following exposure to a preprimary curriculum that promoted social and emotional abilities through play in India (Dillon et al., 2017), to an insignificant -0.08 sd in prosocial scores following a preprimary school expansion in Cambodia (Bouguen et al., 2018).

> 26 Appendix Figures 7–10 graph study-specific effect sizes and visually demonstrate the overall positive but still mixed results for non-cognitive skills.

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Pasha, S.V., Jha, C.S., Diwakar, P.G., Dadhwal, V.K., 2016. Development of national database on long-term deforestation (1930-2014) in Bangladesh. Global and Planetary Change 139, 173–182. https://doi.org/10.1016/j.gloplacha.2016.02.003

- Reed, P.M., Hadjimichael, A., Malek, K., Karimi, T., Vernon, C.R., Srikrishnan, V., Gupta, R.S., Gold, D.F., Lee, B., Keller, K., Thurber, T.B., Rice, J.S., 2022. Addressing uncertainty in multisector dynamics research. https://doi.org/10.5281/zenodo.6515285

- Regan, H.M., Ben-Haim, Y., Langford, B., Wilson, W.G., Lundberg, P., Andelman, S.J., Burgman, M.A., 2005. Robust decision-making under severe uncertainty for conservation management. Ecological Applications 15, 1471–1477. https://doi.org/10.1890/03-5419

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3 Japan, Hong Kong SAR, China; Taiwan, China; Singapore; and South Korea have all gone on to become high-income economies with steady and positive economic growth that has persisted over five decades. Can aspiring countries in Southeast Asia hope to experience the same success? The track-record so far is not promising. Malaysia, Indonesia, Thailand and the Philippines also experienced rapid growth in the 1980s and early 1990s, but that growth rate was insufficient to advance these economies to high-income status. Since the 1997 East Asian crisis, their growth rates have stagnated, or even contracted for a period of time (Figure 1).

This paper focuses on Malaysia and Thailand because they appear to be on the ‘cusp’ of sustained growth as the two most prosperous Southeast Asian economies (Figure 1). The latest data shows that Malaysia’s per capita income was $4987 while Thailand’s was $2604 in constant 2000 US dollars in 2010. They have surpassed the income per capital of countries like Indonesia and the Philippines with which they were once comparable. But they still lag high-income economies like Japan and South Korea and do not seem to be catching up with them.

<!-- Start of picture text --> Figure 1:<br>45000 GNI per capita, constant 2000 US$<br>40000<br>35000<br>30000 Japan<br>25000 United States<br>Korea, Rep.<br>20000<br>Malaysia<br>15000<br>Thailand<br>10000 Philippines<br>5000 Indonesia<br>0<br>Year<br><!-- End of picture text --> Source: World Development Indicators The secret to sustained growth is elusive (Easterly 2001a).<sup>2</sup> Kharas and Kohli (2011) argue that they key for middle-income countries to escape this income status is to rely on growth of a capital- and skill-intensive manufacturing sector and growth of a
The instrument contains a core of 37 questions that aimed to trace all the dimensions of litigation starting with overall perceptions of the Benefit Plan and priority setting strategies employed by the government, exploring features of the judicial process, and ending with consequences of litigation. The instrument design was inspired by recent methodological recommendations related to the study of the political economy of healthcare (Gloppen 2009: 26). We also used additional modules of group specific questions for judges, doctors, insurers, pharmaceutical companies and patient groups. The sections that present the main findings include the concrete formulation of survey questions designed to measure perceptions of efficiency and equity.

Our quantitative dataset includes 36 observations, with a response rate of around 75%, and distributed across groups as follows:

**Table 1: Stakeholder groups and samples**

|_Stakeholder group_|_Interest group coding_|_Observations_|
|---|---|---|
|Executive (two ministries and one agency)|Government (unified agency)|7|
|Legislature|Government (unified agency)|6|

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Consistent with the figures discussed above, a recent qualitative study conducted across 18 Indonesian villages found widespread evidence of nepotism in village bureaucracies. These qualitative accounts suggest that nepotism may be central to understanding bureaucratic practice and performance: “As a result of considerable, albeit narrowing, discretionary powers of the village head, we found that the village bureaucracy is often made up of friends and, particularly, family members of the village head. In 8 of our 18 villages at least some ...village officials were related to the village head. Not surprisingly, the villages where officials were family members of the village head are also the villages with more unresponsive and factionalized village governments” (World Bank, 2023, p.17).

# **3 Empirical Framework**

This section describes the survey and administrative data we use, develops our empirical strategy, and validates the key assumptions underlying the regression discontinuity design.

## **3.1 Data**

We describe here the numerous sources of primary and secondary data on village governance, elections, and bureaucracies that underpin our empirical design.

**Survey of Village Officials and Citizens.** We conducted a large-scale survey of village officials and citizens in Indonesia between March and August 2022. The survey covered 852 villages across 23 districts in 17 provinces spanning the archipelago. Our sampling strategy focused on districts with good internet coverage and aimed to achieve broad national representativeness within this set. The primary targets were active village officials, including elected village heads, non-elected members of the village government, hamlet heads, and BPD chairpersons and representatives. In addition, we surveyed 8 to 12 adult citizens in each village. The survey aimed to inform the design of a future bureaucrat training intervention, to deepen our understanding of village governance, and to provide new insights into perceptions of village governance and development among officials and citizens. We collected perceptions of service access and quality, as well as priorities for future development spending, separately from citizens and officials. This allows us to
# **1 Introduction**

in countries is than in most rich countries and has re- Inequality developing higher mained high over the past 30 years (Alvaredo and Gasparini, 2015). To what extent can tax systems redistribute income in these countries? In this paper, we combine microdata and theory to shed new light on the redistributive potential and optimal design of consumption taxes, the main source of government revenue in developing countries. We find that consumption taxes are redistributive, lowering inequality by as much as income taxes. These effects are primarily due to different informal (untaxed) consumption patterns along the household income distribution. In addition, we show that, once informal consumption is accounted for, commonly used redistributive policies such as reduced rates on necessity goods have a limited impact on inequality. Our results stand in contrast to the existing literature, which does not take into account the existence of informal consumption, and concludes that consumption taxes have a limited or negative redistributive effect in developing countries (see for example Lustig, 2018).

A major constraint in studying informality is that, by definition, informal sector purchases are hard to observe and to link to consumers’ incomes. We innovate by using the places of purchase reported by households in expenditure surveys to proxy for the share of consumption purchased from the informal sector. Thus, our starting point is the creation of a new micro database that combines expenditure surveys from 31 lowand middle-income countries and contains information on the place of purchase for each transaction (such as street stall or supermarket). To assign each place of purchase to the formal or informal sector, we build on micro evidence from retail censuses and on the literature showing that large modern retailers are much more likely to remit taxes than smaller traditional retailers (Kleven et al., 2016; Lagakos, 2016).

Our makes three contributions. The is to new facts paper first produce stylized on consumption patterns across the income distribution and over development. We document the existence of a downward-sloping Informality Engel Curve (IEC
For example, if at time _t_ half of the population in the cohort formed by individuals between 25 and 30 years of age have post-secondary education, then after 10 years (at _t_ +10), half of the population between 35 and 40 will have post-secondary education. Furthermore, for younger cohorts we imposed the assumption that

> 20 The aggregate returns to capital and labor in the CGE model, however, are not aligned with the household data. See Rutherford & Tarr (2008) for a discussion of the impact of reconciliation of the factor returns in the household data with the input-output table.

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how higher IDP inflows affect the size informal sector. Particularly, we examine whether larger IDP inflows are indeed affecting the size of the informal sector by classifying all workers age 15 and 62 into two groups according to whether their primary job is part of a sectors that tends to have high levels of informality (such as retail sales and construction) or low levels of informality (such as financial services, machinery production, production of chemical or pharmaceutical products, and highly technical jobs).<sup>21</sup> Sector classification codes are only available in the labor force surveys beginning in 2002. Our sample,

> 20The only exception is firm exit for which we find significant effects for both groups of firms. 21 We use the same classification employed when we divide the manufacturing survey into firms more and less exposed to informal competition for consistency.

22
*|3.117***|2.598***|2.689***|
||(0.197)|(0.330)|(0.249)|(0.226)|(0.053)|
|_N_|3,851|1,560|814|5,618|10,903|
|_R_<sup>_2_</sup>-statistic|0.356|0.484|0.264|0.345|0.290|

_Source:_ Authors’ estimates based on data from the 2014 Integrated Business Establishment Survey.

_Note:_ This table presents estimates for equation 3. Dependent variable = log employment in manufacturing. Other industry = those industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = those services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. All regressions control for the informality status of a firm, the type of ownership, the type of legal organization, and region. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

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**Figure 9. Russia’s Intensive Margin of Trade, 2007 and 2011**

**A. B.**

<!-- Start of picture text --> 2007 2011<br>.1 .1<br>.05 .05<br>0 0<br>DEU USA UKR GBR FRA TUR FIN KAZ CHE NLD DEU USA UKR FRA GBR CHE TUR NLD KAZ JPN<br>Share in total export Share in total export<br><!-- End of picture text --> Source: World Bank Trade in Services Database Note: Intensive margin is the indicator that assesses whether exports are growing because a country is exporting more of the same goods and services to the same market rather than exporting new goods and services or reaching new markets. DEU = Germany, USA = United States of America, UKR = Ukraine, GBR = Great Britain, FRA = France, TUR = Turkey, FIN = Finland, KAZ = Kazakhstan, CHE = Switzerland, NLD = Netherlands, JPN = Japan.

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.06|(2.83)|1,411|
|$ shared with a Jordanian citizen from different ethnicity|-0.20|(0.43)|[1.00]|3.70|(2.84)|1,406|
|$ shared with a Jordanian soldier/police offcer|-0.06|(0.49)|[1.00]|4.32|(3.23)|1,403|
|$ shared with an employee of the Jordanian government|-0.11|(0.46)|[1.00]|3.37|(2.98)|1,406|
|$ shared with an employee of an international NGO|0.37|(0.40)|[1.00]|3.00|(2.83)|1,402|

**_Notes_** :

- The table shows the regression results on pre-specified behavioral outcomes using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Risk Taking Preferences is defined as in the Jordan version of the Global Preferences Survey. The quantitative measure consists of a series of five binary choices. Choices were between a fixed lottery, in which the individual could win a certain amount or zero, and varying sure payments. Choice of the lottery resulted in an increase of the sure amount being offered in the next question, and vice versa. The total score is normalized by the control group’s mean and standard deviation such that the final score has a mean of zero and standard deviation of one in the control group. Higher values indicate a tendency to take more risks. Impatience Preferences is defined as in the Jordan version of the Global Preferences Survey. It includes a series of five interdependent hypothetical binary choices between immediate and delayed financial rewards. The total score is normalized by the control group’s mean and
2020). The central government launched a US$275 billion plan to improve air quality throughout the country, and the Beijing municipal government promised an additional US$160 billion toward that goal (Vaitla et al. 2015b).

# **Confidentiality**

When data are not confidential, subjects may be identifiable. This violates core principles of privacy and increases the likelihood that harm can be done to the identified subjects. Deidentifying individuals is not always enough to maintain confidentiality. In the 1990s, for example, the Governor of Massachusetts approved making de-identified medical records of state employees available for researchers. Though the data had key identifiers such as name and addresses removed, by triangulating the information available with other public information, a researcher was able to identify the medical records of the governor. Other individuals could likewise be identified (Heffetz and Ligett 2014).

Beyond re-identification, data breaches also pose a threat to confidentiality because they raise doubts as to whether personal information is safe. Aadhar, the unique 12-digit identity number of India mentioned before, has suffered from several data leaks. In one instance, more than 200 government websites accidentally made personal data including demographic data, names, phone numbers, religion, bank account numbers and more, available publicly on the internet.<sup>5</sup> Though the data were quickly removed, such leaks often have permanent implications on confidence in data systems.

# **Appropriateness**

For data to generate value, they need to be generated and used to serve a legitimate policy purpose. This means the data must contain information relevant to the policy problem at hand. Critically, this also means that the amount of information collected and used should not be excessive but appropriate for the particular needs. As such, appropriateness is key to preventing data misuse, such as surveillance or exclusion.

Governments’ tracking and contact tracing of infected and at-risk individuals through smartphone apps or phone location data during the COVID-19 pandemic shed light on the relevance of appropriateness. Some of these tracking
dren are household heads because if the children are living with parents, we may falsely attribute the permanent income of children to parents. Then we merge the household expenditure of the fourth wave (2007) with the household expenditure of the fifth wave (2014) using the household tracking ID. We adjust inflation taking 1993 as the base year.

**Role model data:** The role model in our study are individuals who were born in the same district as the child and belong to the top 50 percent of education distribution of fathers’ reference age group. To calculate the role model education, we first needed to obtain fathers’ age. We check if the father has co-resided with children in any of waves. If we can find the father, we obtain the age from the household roster and convert that value to what his age would be in 2014. For the non-resident father, we obtain the age from the non-resident module. The IFLS asks age at death and year of death, if the father is not alive. Using this information, we calculate what would have been fathers’ age in 2014. We then obtain the birth district information of the children. Separately, next we use the roster data of each wave to obtain years of education and age of all individuals who are older than 22 at the time of the survey. We choose older than 22 because by age 22 the individuals would have completed their education. Then we convert the age of these individuals to the value it would be in 2014. Using the birth district information, we obtain the birth district of all the individuals in the roster. For any given age, we calculate the average of years of education of the top 50 percent achievers within the _±_ 3 ( _±_ 5 for robustness check) age group of a district. We then merge the district ID information and age information with the birth district ID and father age information of the data above.

**Peer Data:** We select