rafmacalaba/data-use-mentions-extended huggingface dataset:
public institutions, ensuring the right to protest safely, lowering the number of signatures required on petitions to elevate issues to authorities, and providing technical support to neighborhood associations, to mention a few. Lesson from Banerjee and Duflo’s (2011) small radical thinking to fight global poverty might also be relevant in the context of citizen participation.

The analyses conducted here can be replicated elsewhere by developing machine learning methods able to “learn” from datasets covering other regional or national settings. Future research on the linkages between political trust and citizen engagement in the Latin America and the Caribbean region and in other regions should also consider other emerging forms of political engagement like e-participation—that is, online political activity such as, for example, participating in online surveys, using government digital transparency, or adding political banners to profile pictures on Facebook, among others—in addition to voting. It should also more precisely unpack data on different forms of trust, for example in national versus local governments, or across different branches of the police, armed forces, and judiciary. More detailed information is also needed to capture the types of community problems citizens engage or the type of causes that participation in demonstrations are sought to reverse, which are not currently asked in Latinobarómetro. Future efforts would also benefit from additional investments in quantifying the costs involved in different types of political participation. Flexible models allowing for nonlinearities capturing systems 26
Policy Research Working Paper 5994

# **Abstract**

Using data from the 2010 Life in Transition Survey, this paper examines the levels of citizens’ satisfaction with public service delivery in Europe and Central Asia and identifies some factors that may help explain variation in utilization and levels of satisfaction with service delivery. It finds satisfaction with public service delivery in Europe and Central Asia to be relatively high, and, despite the adverse economic and social impact of the recent global economic crisis, to have risen since 2006 in most countries in the region. However, the level of satisfaction with public service delivery in Eastern European and Central Asian countries in 2010 remains lower than in Western European comparator countries. Although the Life in Transition Survey does not provide specific objective measures of service delivery quality and efficiency, the data provide three important clues that may help explain why satisfaction is lower in transition countries than in western comparators: (i) relatively higher utilization of public services in Eastern European and Central Asian countries, (ii) relatively higher reported prevalence of unofficial payments, and (iii) relatively underdeveloped mechanisms for grievance redress.

This paper is a product of the Poverty Reduction and Economic Management Unit, Europe and Central Asia Region. 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 author may be contacted at szaidi5@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
obtain higher estimates. Herrerias (2010) investigates whether an investment in equipment/machinery impacts the economic growth effect in China and finds that it does significantly. Banerjee, Duflo and Qian (2012) also study the effect of the transportation system on regional economic performance in China. They find that the transportation infrastructure could increase sectoral output, but it does not necessarily boost GDP growth.

Czernich et al. (2011) study the effect of broadband infrastructure on the economic growth in the panel of OECD countries and find positive and significant impacts. Czernich et al. address endogeneity concerns using an IV approach where pre-existing voice telephony and cable TV networks predict maximum broadband penetration. However, since the model derives its first stage 22
|0.1435|0.0692|0.0913|0.1157|
|**N obs.**|24889|46357|20288|21393|19578|

_Notes:_ South : ES, PT, IT, GR, CY, and IL; East : CZ, PL, SK, HU, EE, LV, BG, RO, SI, RU, and UA; North: DK, FI, NO, and SE; West -1: UK, IE, NL, and AT; West-2: DE, FR, BE, and CH.

Larger coefficients indicate larger perceived difficulties. *, **, *** indicate that respective coefficient for the given employment status is significantly different from the coefficient for formal employees at 10%, 5%, 1% level, respectively (based on robust standard errors clustered on within-country region). Countries are not weighted by population size. Design weights corrected for variation of sample size across countries in each round are applied (i.e., all respondents are assumed equally likely to be sampled; countries which did not participate in some rounds are, however, under-represented).

_Source:_ Calculation with ESS data.

55
Once a village or homestead was attacked, the LRA’s “strategy was to abduct first and sort out later” (Blattman and Annan 2010, 8–9). This is supported by Blattman and Annan’s (2010) quantitative analysis of youth abductions using a pre-abduction representative data for one of the most affected districts in Northern Uganda. They find no statistically significant differences in the mean of pre-war characteristics that predict abductions in other conflicts, such as pre-war wealth or parental characteristics.<sup>20</sup> These findings suggest that variation of individual and household characteristics within village did not affect the likelihood of being abducted, which in turn is likely highly correlated with attacks.<sup>21</sup>

# _Relationship Between Statistical and Perceived Risk_

Two different types of risk can be estimated with the available data: statistical (objective) and perceived (subjective). The former refers to the observed likelihood of a particular shock occurring while the latter to the a priori belief whether a specific shock will occur. While correlated, individuals make their decisions based on upon their perceptions as opposed to the “true” likelihood of shocks.

In general, these two types of risk may differ for a variety of reasons including incomplete information or behavioural biases such as the proximity or vividness of events. Lowenstein et al. (2001) highlight how “emotional reactions to risk situations often diverge from cognitive

> 20. The only exceptions were the year of birth and the size of the household. The former reflects the preference of the LRA for youths between the ages of 10 and 24 as forcible recruits; younger children were less useful while older youth were perceived as being difficult to indoctrinate. The significance of the size of household is driven by the sub-sample of household with 25 or more members. This is not only a rare occurrence in their data but also overall in Northern Uganda. The 2002 Uganda National Census shows that less 1 percent of households in Northern Uganda, rural or urban, have 15
.2|17.2|17.2|17.2|17.2|
|Senegal|23.8|22.8|26.2|26.0|15.8|15.8|
|<br>South Africa|11.4|7.7|5.3|4.7|2.7|2.7|
|Tanzania|8.6|8.0|7.6|7.5|5.0|5.4|
|Uganda|22.0|21.9|21.8|21.8|16.8|17.0|
|Zambia|5.1|4.4|3.9|3.8|2.3|2.5|
|Congo, Rep.|11.7|10.7|10.1|9.9|11.2|11.2|
|<br>Mali|23.2|23.2|23.2|23.2|||
|Botswana|7.7|7.2|6.9|6.8|3.3|4.0|

_Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database _Notes:_ Average commercial consumption level is highlighted in bold — Not available.

<!-- Start of picture text --> Figure 8. Increasing, decreasing, and neutral tariff structures<br>20<br>15<br>10<br>5<br>0<br>domestic commercial industrial<br>type of effective tariff structure<br>increasing neutral decreasing<br>number of countries<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database 19
- Eichengreen, Barry (2001). “Capital Account Liberalization: What do Cross-Country Studies Tell Us?” In: _World Bank Economic Review_ 15(3), pp. 341–365.

- Feenstra, Robert and Gordon Hanson (2001). “Global Production Sharing and Rising Inequality: A Survey of Trade and Wages”. In: _NBER Working Paper Series_ ( 8372).

- Fisunoglu, Ali et al. (2011). _Relative Political Capacity Dataset_ . Version 2.4 (August 2020). doi: 10.7910/DVN/NRR7MB.

- Frankel, Jeffrey and David Romer (1999). “Does Trade Cause Growth?” In: _American Economic Review_ 89(13), pp. 379–399.

- Goldberg, Pinelopi and Nina Pavcnik (2007). “Distributional Effects of Globalization in Developing Countries.” In: _Journal of Economic Literature_ 45(1), pp. 39–82.

- Goldberg, Pinelopi and Nina Pavcnik (2016). “The Effects of Trade Policy”. In: _NBER Working Paper Series_ ( 21957).

- Gollin, Douglas (2002). “Getting Income Shares Right”. In: _Journal of Political Economy_ 110(2). doi: 10.1086/338747.

- Gross, Wendelin, Cristina Hayden, and Christian Butz (2012). “About the impact of rising oil price on logistics networks and transportation greenhouse gas emission”. In: _Logistics Research_ 4, pp. 147–56.

- Guerriero, Marta (2019). “The Labor Share of Income around the World: Evidence from a Panel Dataset.” url: https://www.adb.org/publications/labor-share-income-around.

- world-evidence-panel-dataset

- Gygli, Savina et al. (2019). “The KOF GLobalization Index – revisited”. In: _The Review of International Organizations_ 14, pp. 543–574.

- Harrison, Ann (2005). _Has Globalization Eroded Labor’s Share?
Hence, the rural-urban poverty-gap has increased in recent years. Moreover, not only is the income gap to urban areas increasing but the gap between richer and poorer segments of the population in the rural areas is also growing. In 2002, rural Mexico had poverty rates for higher than urban Mexico. Dispersed rural areas had a poverty incidence double that of semi-urban areas. Furthermore, dispersed rural areas had a poverty incidence four times higher than urban areas in Mexico.

**Figure 3.1: Extreme Poverty (P0) in Mexico Measured,** **<u>Selected years 1996-2002</u>**

<!-- Start of picture text --> 70.0<br>National<br>Urban<br>60.0<br>Rural (0-15,000)<br>localities with 1 to 2,500<br>50.0 localities with 2,501 to 15,000<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>1992 1996 2002<br>Source: calculations based on ENIGH 1996, 1996, and 2002.<br><!-- End of picture text --> Mexico has steep gradients in conditions of living from more developed urban areas, through the urban periphery and smaller towns, through to the more remote rural areas. Figure 3.1 shows that this pattern has not changed much over the last decade and in rural localities with less than 2,500 people more than 40 percent were extremely poor compared with those localities with 2,500-15,000 people where 21 percent were poor in 2002. This translates into 13.2 million people in poverty. Most of the rural poor live in more dispersed rural areas, as 2.8 million were poor in semi-urban areas and 10.4 million poor in dispersed rural areas (information on population in rural areas in Appendix A)
**Table A.3.5: Per student average lifetime earning loss at present value by region, income group, and lending type**

|||Post-COVID-19<br>||
|---|---|---|---|
|Aggregate|Optimistic|Intermediate|Pessimistic|
|EAP|6,965|17,239|27,901|
|ECA|10,361|27,039|44,394|
|LAC|4,422|9,750|15,229|
|MNA|8,331|20,273|32,647|
|NAC|11,923|33,534|56,092|
|SAR|2,110|3,949|5,813|
|SSF|2,375|4,593|6,848|
|Grand Total|6,472|15,901|25,680|
|HIC|12,252|34,021|56,732|
|UMC|6,049|12,337|18,775|
|LMC|2,920|5,483|8,079|
|LIC|1,306|2,320|3,341|
|Grand Total|6,472|15,901|25,680|
|Part I|13,065|36,150|60,231|
|IBRD|5,765|12,195|18,798|
|IDA/Blend|2,076|3,912|5,770|
|Grand Total|6,472|15,901|25,680|

Source: Authors’ calculation. Decrease on average lifetime earnings per student at present value (2017 PPP $). Simulation 1 results based on latest available LAYS of 157 countries (unweighted average), with the change in LAYS expressed in foregone lifetime earnings per student at present value.

49
5|34.9|58.7|2,734.0|1089.2|27.6|23.7|74.9|29.8|

_Source:_ Household Income and Expenditure Survey (HIES) 2017 and World Development Indicators (WDI).

_Notes:_ Deciles are created based on household per capita consumption, based on the ECAPOV harmonized aggregate by the World Bank. Households are classified as “smokers” if they report positive expenses in tobacco products. Exchange rate = 2.51 GEL/USD.

16
has been done to date.<sup>3</sup> Ideally, we would expect to understand the degree to which a metric tracks the underlying dimensions of food access that are intended to be captured. The FIES was initially implemented globally in the Gallup World Poll (GWP), and unfortunately the GWP cannot completely validate the FIES in this way given that there is no information on actual household food consumption, on other food access metrics, or on other traditional welfare metrics (e.g., Gallup World Poll 2019). Rather, much of the validation of the FIES has been done by correlating the FIES to other subjective welfare metrics and the limited household characteristics captured in the GWP (e.g., Smith et al. 2017; Wambogo et al. 2018; etc.). However, given that a number of other closely related experiential measures of food access align poorly with other food access metrics (e.g., Headey and Ecker 2013; Maxwell et al. 2014; Broussard and Tandon 2016; etc.), it is important to more thoroughly validate the FIES and understand what aspects of poor food access are being captured.

We perform a more thorough validation of the FIES in Nigeria using data from a nationally representative multipurpose survey that captures a number of food access and welfare indicators from each household. Nigeria is important for regional and global measurement of poor food access. By official global measures, Africa is the continent with the highest prevalence of both undernourishment and the inability to afford a diet sufficient to maintain an active and healthy lifestyle (e.g., FAO 2021), and Nigeria alone accounts for approximately 20 percent of Africa’s entire population.

In this important setting, we compare estimates of food access using the FIES to monetary poverty. Although monetary poverty and food access are distinct concepts, we illustrate there exist empirically supported assumptions under which models of consumer theory predict that many key manifestations of poor food access —undernourishment and undernutrition — are more
observation being the initial levels of employment. In addition, we used the inverse of the firststage standard error of γi,t as weights, and the results were virtually identical, because the standard errors of γi,t are negatively correlated with the size of the industry. For the sake of brevity, the results reported below are limited to the WLS estimates with initial employment as weights.<sup>5</sup>

# **_4.3. Labor Market Adjustment Costs: Changes in the Composition of Workers_**

At least since Reder (1955), the labor literature has emphasized compositional changes in employment during business cycles. Reder (1955) argued that upturns (when labor is scarce) are associated with downgrading of the skill composition of workers and downturns (when employers have a larger pool to choose from) are associated with upgrading.<sup>6</sup> Gautier et al. (2002) found evidence in favor of these predictions in matched employer-employee data from the Netherlands. Since skilled workers in Mexico have been found to have higher adjustment costs (Robertson and Dutkowsky 2002), it seems possible that the financial crisis led to changes in the firm-level mix of skilled and less skilled workers.

Using data from the Mexican Industrial Census, Bernard, Robertson, and Schott (2010) show both maquila/total employment ratios, state-wide non-production/production employment ratios, and maquila non-production/production employment ratios by Mexican states (and the Federal District) that have non-zero maquiladora employment. Using these data, Table 1 shows that the maquiladora industries have a much lower non-production/production worker employment ratio, as might be expected if one of the main motivating factors for offshoring in Mexico is to take advantage of lower labor costs. If adjustment costs are lower for production

> 5 The results with the standard errors as weights are available upon request. 6

> On the labor force upgrading and business cycles, see also Hamermesh (1993).

15
The data were sourced from the National Health Information System (NHIS). This system provides monthly data at the health center level on the quantity of each contraceptive method distributed within their designated catchment area by the Burundian government as well as the number of CHW referrals to health center facilities. An important limitation of the NHIS data is the difficulty of distinguishing between true zeros and missing values when a health center reports no data for a contraceptive method in a given month. To partly address this issue, we classify observations as missing if all contraceptive methods show zero quantities for a given health center and month. This classification applies to approximately 5% of our sample.

The primary outcome of our study is the quantity of SP injections reported by health centers in the NHIS data. As secondary outcomes, we employ the quantities of relevant alternative contraceptive methods to investigate potential substitution effects.

# **Study Sample**

Table 1 summarizes key information on the 138 study health centers, revealing the limited capacity of health centers and low contraceptive utilization (column 1). Panel A shows that only 38 health centers (28%) had HPO availability. Among these health centers, each had only one HPO, indicating very limited in-home administration capacity before the CHW intervention rollout. On average, there were 16 CHWs serving a population of 15,775 within the health centers’ catchment areas. Of this population, an estimated 3,739 are women of childbearing age, translating to roughly 230 women per CHW.

Panel B presents the baseline number of SP injections administered per month within the catchment area of health centers as well the overall quantity of all contraceptives dispensed, including injectables (SP and DMPA-IM), condoms, birth control pills, emergency contraceptive pills, implants, and intra-uterine devices. It is worth noting that this measure should be interpreted with caution given the varying protection duration across contraceptive methods. A detailed breakdown by each contraceptive method is provided in Table A1. Given the estimated average of 3,739 women of childbearing age residing
**Figure 17.  One-Year Probability of Finding a Job by Age Group and Field of Education** (controlling for level of education, gender, ethnicity and residence)

<!-- Start of picture text --> UE<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>15-19 20-24 25-34 35-44 45-54 55-ret ret<br>age<br>IT & other technologies, construction, mathematics<br>transport & communications<br>Other<br><!-- End of picture text --> _Note:_ “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Ethnicity appears to be more important than gender** . While unemployed males and females of the same age, education and residence have had virtually equal chances to find a job within a year, for nonLatvians this probability was, on average, 0.08 lower than for otherwise similar Latvians (Figure 18).

**Figure 18. One-Year Probability of Finding a Job by Age Group and Ethnicity** (controlling for level and field of education, gender, and region)

<!-- Start of picture text --> UE<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>15-19 20-24 25-34 35-44 45-54 55-ret ret<br>age<br>Non-Latvian Latvian<br><!-- End of picture text --> _Note:_ “Ret” stands for retirement age. _Source:_ Calculations based on LFS 2002.

**Young people are more likely to find jobs than prime-age jobseekers, while the elderly are trapped in unemployment.** As Figures 16 and 18 show, the probability
the total estimated losses from the Gorkha earthquakes, and for 60 percent of the total damages (National Planning Commission 2015).

# **5. Data description**

The dataset used to illustrate the proposed approach to estimating the cost of disasters combines information from three sources. These are: a household survey conducted in 2010/11 (Central Bureau of Statistics of Nepal 2012a), from the 2011 population and housing census (Central Bureau of Statistics of Nepal 2012b), and from official figures on assets/housing destructions covered by the Disaster Recovery and Reconstruction Information Platform established following the Gorkha earthquakes (Government of Nepal 2015).

The first of these three sources, the Nepal Living Standards Survey III (NLSS III), reports data on multiple welfare dimensions for 5,998 households in 2010/11. Its sampling frame is representative at the level of districts or Village Development Committees (VDCs).

Households are not geocoded in the NLSS III dataset but the districts or VDCs they live in are. The latter are taken as the reference geographic areas in what follows. It should be noted that in September 2015, as Nepal embraced a federalist government structure, its more than 3,100 districts and VDCs were consolidated under 753 local governments in seven provinces. However, this regrouping does not affect the analysis.

In addition to welfare-related indicators, the NLSS III contains household-level data on dwelling configuration, occupancy, endowments, and location. Imputed housing prices come from self9
are essential ingredients for economic development generally and the results in this report also indicate that they might be key mediating factors. However, in a post-conflict setting changes in formal institutions might not appear possible or even risky.<sup>57</sup> It might then still be possible to introduce informal institutions. Blattman et al. (2014) study the role of new institutions in support of the formal ones in the development of Liberia. They argue that the promotion of “Alternative Dispute Resolution” institutions for the protection of property rights inhibits local violence.

The codebook published for the EPR data reveals how these variables are coded.<sup>58</sup> All politically relevant ethnic groups were categorized according to the degree of access to central state power by those who claimed to represent them. State power refers to executive power only, disregarding access to legislative and judicial institutions. Depending on where political power is effectively exercised, this can be the presidency, the cabinet, and senior posts in the administration in democratic regimes; the army command in military dictatorships; or the ruling party leadership in one-party states. The three categories included in excluded groups are

> _• powerless_ : Elite representatives hold no political power (or do not have influence

> 57Flores and Nooruddin (2009), for example, argue that post conflict democratizations hinder growth because they reduce commitment power.

> 58See codebook for Vogt et al. (2015).

68