rafmacalaba/data-use-mentions-extended huggingface dataset:
- Fields, Gary S. and Efe Ok. (1999). “Measuring Movement of Incomes”. _Economica_ , 66: 455– 471.

- Fitzgerald, John, Peter Gottschalk, and Robert Moffitt. (1998). “The impact of attrition in the PSID on intergenerational analysis”. _Journal of Human Resources_ , 33(2): 300–44.

- Gorodnichenko, Yuriy, Klara Sabirianova Peter and Dmitriy Stolyarov. (2010). “Inequality and Volatility Moderation in Russia: Evidence from Micro-level Panel Data on Consumption and Income”. _Review of Economic Dynamics_ , 13:209-237.

Greene, William. (2018). _Econometric Analysis, 8_<sup>_th_</sup> _edition_ . New York: Pearson.

- Jantti, Markus, and Stephen P. Jenkins. (2015). “Income Mobility”. In A. B. Atkinson and F. Bourguignon, ed., _Handbook of Income Distribution_ , 807-935, North Holland, Oxford.

- Jappelli, Tullio. and Luigi Pistaferri. (2010). “Does Consumption Inequality Track Income Inequality in Italy?” _Review of Economic Dynamics_ , 13(1): 133–153.

- Jenkins, Stephen P. (2017) “Pareto Models, Top Incomes, and Recent Trends in UK Income Inequality”. _Economica_ , 84: 261–289

- Jovanovic, Branko. (2001). “Russian roller coaster: expenditure inequality and instability in Russia, 1994–1998”. _Review of Income and Wealth_ 47: 251–272.

- Kapelyuk, Sergey. (2015). “The effect of minimum wage on poverty: Evidence from RLMS-HSE data”. _Economics of Transition_ , 23(2): 389–423.

- Kim, Bonggeun, John Gibson, and Chul Chung. (2017). "Using Panel Data to Estimate Income Under‐Reporting by the Self‐Employed." _Manchester School_ , 85(1): 41-64.

- Kopczuk, Wojciech, Emmanuel Saez,
globally, committing to be on a trajectory consistent with the global goal of limiting the increase in global average temperature to 1.5C.<sup>36</sup> This goal is also consistent with that noted in Costa Rica’s long-term National Decarbonization Plan. Through global value chains, international commitments also have a bearing on countries’ specific GHG emission trends (Kaltenegger et al. 2017). The implementation of an orderly transition, particularly in the case of ambitious NDCs, is key to the mitigation of transition risks for the banking sector (NGFS 2020b).

Bolivia, Paraguay, and Uruguay are the largest emitters of GHG when adjusting for the size of their economy. While Mexico and Brazil are the largest emitters of GHG in absolute terms, this is not the case when we account for the size of their economies and look at emissions per unit of GDP (Figure 7).<sup>37</sup> Bolivia, Paraguay, and Uruguay sit around or below the median emitters of the sample countries but are in the higher end of CO2e per unit of GDP, which is largely explained by their agricultural sector emissions.

To obtain a preliminary indication of banks’ loans value-at-risk to the transition to a low carbon economy, we analyze the sectoral distribution of bank loans to nonfinancial corporations. We find that more than one quarter of banks’ credit portfolios in Argentina, Uruguay and Bolivia are tilted towards transition-sensitive industries that are also high CO2 emitters. The relatively higher exposure to transition risks in these countries is driven by the agricultural sector, and to a greater extent in Uruguay where agriculture explains two-thirds of the total exposure (Figure 8). Although we could not retrieve credit outstanding information with the required granularity to compare Paraguay across LAC, data from Paraguay Central Bank show that banks allocate around 30 percent of their total credit portfolios to the agricultural sector. This allocation is larger than the exposure to all transition-sensitive sectors in the rest of our sample of countries. Banking systems less exposed to transition
Reflecting the focus of this paper, and the availability of relevant research, our literature review here is broken into two parts. First, we review the small number of descriptive, observational studies of attitudes toward COVID-19 vaccines that come from developing countries.<sup>2</sup> This literature, while small, is the most relevant for situating the findings of our observation phone survey study. Second, we review the more explicitly causal literature that attempts to explain why vaccine hesitancy exists, and whether it can be changed. Here, we do not limit our coverage either to COVID-19 or developing countries – neither have been studied enough. Rather we cast our net wider, covering vaccines for other diseases, and studies from developed countries. We use this second literature to feed into the conceptual framework used in the experimental component of our own research.

# - <u>2.1 Descriptive surveys of views on COVID 19 vaccines in developing countries</u>

The most significant published study of attitudes to COVID-19 vaccines across developing countries is that of Solís Arce et al. (2021). Their paper reports on results from surveys conducted in 13 countries, spanning the Americas, Africa, South Asia, and Europe. All countries except the Russian Federation, Colombia and the United States are low or lower-middle income countries. One clear takeaway from the study is just how much variation there is between countries in willingness to take the COVID-19 vaccine. Also striking is the fact that surveyed willingness to take the COVID-19 vaccine is higher – on average over 80 percent – in the low, and lower-middle income countries, than it is in either Russia or the United States (Solís Arce et al., 2021, p. 1389). Although results varied between countries, women tended to be somewhat less willing to be vaccinated than men, and younger respondents marginally more willing (Solís Arce et al., 2021, p. 1387).

Between-country variation in attitudes to COVID-19 vaccines is a similar standout point in another
space. This works well in developed countries, where there is wide and uniform coverage of weather stations across the entire territory, but not so much in developing contexts, where often gridded data aggregate weather information from a few old stations spread across the country. Sparse coverage is a serious issue given the interpolation method adopted by gridded products. Finally, entry and exit of stations (quite common, especially in poorer countries) can be endogenous and represents an additional source of measurement error of true weather conditions experienced by people.<sup>11</sup> Such diversity in weather data products can affect econometric estimates of the relationship between climatic events and a given socioeconomic outcome of interest. Second, the need for spatial anonymization for privacy protection in household surveys, usually implemented through a random offset of true household

> 11 See Auffhammer et al. (2013) and Dell, Jones and Olken (2014) for further discussion. For a comprehensive overview of the availability and quality of climate data in the context of Africa see, instead, Dinku (2019).

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**<mark>Short case studies (vignettes)</mark>**

# _1)_ _<u>Sea level rise in the coastal United States</u>_

In a simulation of disadvantaged populations at risk of future sea level rise, Martinich and colleagues (2013) assess not only disproportionate impacts of sea level rise stemming from exposure of coastal communities, but also differential merit in protecting some areas from inundation while leaving others at risk of abandonment. In the Gulf region of the United States, the model results suggest that over 99% of the most socially vulnerable populations live in areas unlikely to benefit from protection compared to only 8% of the least socially vulnerable groups (those with highest resilience) who live in the same areas. The study focuses on four regions (North Atlantic, South Atlantic, Gulf, and Pacific) and combines the Social Vulnerability Index (SoVI), introduced by Cutter et al. (2003) and applied to demographic variables available for coastal census tracts in these four regions, with the national coastal property model (NCPM), developed by EPA and collaborators. Three distinct sea level rise scenarios throughout the 21<sup>st</sup> century are used (low = 28.5cm rise by 2100 compared to 1990 levels; medium = 66.9cm rise; and high = 126.3cm rise) to identify areas at risk and adaptation responses considered economically efficient based on property value, elevation, and protection costs. The adaptation responses are protection (e.g. sea wall construction and maintenance), beach nourishment through replacement of sand, and abandonment.

<!-- Start of picture text --> (those with highest resilience) who live in the same areas. The study focuses on four regions<br>(North Atlantic, South Atlantic, Gulf, and Pacific) and combines the Social Vulnerability Index<br>(SoVI), introduced by Cutter et al. (2003) and applied to demographic
may nevertheless be totally misconceived and so forth. Financial monitoring Is not enough, but It Is a vital starting point and first priority.

36. As far as Investment programmes are concerned, a second elementary requirement is that it must be possible to relate expenditure information to a financial plan for each project. The development and maintenance of standardised project profiles for all approved projects is an Invaluable technique (see Box 6). Initially, at least, monitoring data should follow the classifications used In project administration, even If these do not correspond precisely to economic or other relevant categories.

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Policy Research Working Paper 7617

# **Abstract**

The collection of survey data from war zones or other unstable security situations is vulnerable to error because conflict often limits the implementation options. Although there are elevated risks throughout the process, this paper focuses specifically on challenges to frame construction and sample selection. The paper uses simulations based on data from the Mogadishu High Frequency Survey Pilot to examine the implications of the choice of second-stage selection methodology on bias and variance. Among the other findings, the simulations show the bias introduced by a random walk design leads to the underestimation of the poverty headcount by more than 10 percent. The paper also discusses the experience of the authors in the time required and technical complexity of the associated back-office preparation work and weight calculations for each method. Finally, as the simulations assume perfect implementation of the design, the paper also discusses practicality, including the ease of implementation and options for remote verification, and outlines areas for future research and pilot testing.

This paper 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 khimelein@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
# **The Effect of School Type on Academic Achievement: Evidence from Indonesia**

# **ABSTRACT**

Using data from Indonesia, this paper evaluates the impact of school type on academic achievement of junior secondary school students (grades 7-9). Students that graduate from public junior secondary schools, controlling for a variety of other characteristics, score 0.15 to 0.3 standard deviations higher on the national exit exam than comparable privately-schooled peers. This finding is robust to OLS, fixed-effects, and instrumental variable estimation strategies. Students attending Muslim private schools, including Madrassahs, fare no worse on average than students attending secular private schools. Our results provide indirect evidence that higher quality inputs at public junior secondary schools promote higher test scores.

# **INTRODUCTION**

The existing evidence on how the characteristics of schools in developing economies, including whether they are publicly or privately administered, affect students’ acquisition of cognitive skills is surprisingly mixed. The effect of educational policies in general on learning in developing countries is poorly understood, as a recent survey on the topic conceded that “most of what has been learned has been methodological” rather than substantive (Glewwe, 2002). Better understanding of the effect of school characteristics on learning is important because public policy can influence the characteristics of public schools, as well as the cost of private schools through vouchers and scholarships. As the first step towards understanding the determinants of student achievement in Indonesia, this paper focuses on how the type of school attended by junior secondary school students influences their academic achievement.

This study presents evidence that Indonesian public junior secondary schools are more effective than their private counterparts at imparting cognitive skills, as measured 1
Formally, the model takes a generalized least squares form to estimate the joint distribution of per capita income _y_ in the household _h_ located in municipality _m_ , conditional on two sets of covariates: _Xhm_ , which includes household and individual characteristics, and _Zm_ , which includes fixed characteristics of the municipality of residence. The parameter _α_ is a household-specific effect; _β_ and _γ_ are the correlation parameters between the corresponding sets of covariates and _ln_ ( _yhm_ ); and, _μhm_ = _ηm_ + _εhm_ represents an error term, where _ηm_ is the component that is common to all households located in the same municipality (assumed to be homoscedastic and _i.i.d._ ), and _εhm_ is the component that is specific to each household (assumed to be heteroscedastic because it depends on the characteristics of the household and the municipality). The estimates of _β_ , _γ_ , and _μhm_ are then applied to the corresponding sets of covariates _Xhm_ and _Zm_ in the census to simulate, using the bootstrap method, the distribution of household per capita income.

The empirical support of extensive applications has shown this methodology to be robust (Alderman et al. 2002; Bedi et al. 2007). It has become common practice to use the methodology to improve targeting in developing countries facing the dilemma between precision and geographical detail. In Mexico, the small area estimation technique has already been applied to map income-based poverty officially across municipalities, but only in 2000 and 2005.<sup>4</sup> This paper relies on data on five points in time over a 22-year period by pairing available rounds of the Household Income and Expenditure Survey (ENIGH) and censuses collected in or around the same years (1990–92, 2000, 2005, 2010, and 2014–15).<sup>5</sup> At each point in time, the survey is a random sample of the corresponding census sample frame.<sup>6</sup> This allows strict comparability of the distributions of a given variable
Finally, the legal rights index from Doing Business (LEGAL_RIGHTS), is positive and very significant (although in an unclustered regression only), suggesting that the life sector develops faster under a supportive legal framework.<sup>11</sup> Models (5) and (6) show that financial development is strongly associated with LLIFE even when LGDPPC is controlled for. A 1-percent increase in the log of private credit to GDP (LPC) is associated with a 1.12 percent increase of LIFE. This finding implies that deeper credit markets spur personal loans, which often requires life insurance as collateral. Additionally, financial development in general can help insurance companies conduct better risk and asset and liability management which can increase the supply of insurance. Interestingly, the inclusion of LPC renders INFLATION insignificant in Model (5). This could be driven by the fact that inflation is detrimental to financial development. Indeed, INFLATION and LPC are negatively correlated (rho=-0.4). Together, these findings suggest that inflation exerts influence over life insurance activity in part via its strong impact on credit market development. In addition, Model (6) suggests that deeper private bond markets help insurance companies manage their assets more efficiently. Note, however that the number of countries in the sample falls significantly since bond market data are typically only available for more developed countries.

Lastly, Model (7) displays regression results that test for the statistical significance of individual factors in all categories to address a potential confounding variable problem. Although the sample size decreases significantly, the regression shows that the main results continue to hold with coefficients of the same order of magnitude. Interestingly, LINCOMETOP20 and MUSLIM have now become statistically significant in clustered regressions as well.

_Non-life premium regression results_ Next, we turn to a regression analysis of non-life insurance development in Table 6. The dependent variable is the log of non-life premiums to GDP (LNONLIFE). Depending on the

> 11 Admittedly, none of the legal indicators from Doing Business captures
Our study faces several limitations. Most notably, we cannot assess the reliability of administrative data directly. Our focus on survey-based sources of error is justified due to the importance of survey data for health research and policy but leaves the possibility of (non-negligible) error in the administrative data (see Sandefur and Glassman 2015, Lim et al. 2008). In this regard, the evidence we present on potential issues in administrative data sources should only be considered indicative. Having access to disaggregated administrative data, for example by sex, age, and lower administrative levels, or gaining systematic insights into the data pipeline would allow for a more complete assessment of the matter. Similarly, we cannot benchmark our estimates against an objective truth that would allow us to exactly quantify the amount of bias in either data source or collect self-reported information from each household member. Our study is limited to assessing the relative bias of different design choices. We further cannot rule out that there are interaction effects between different sources of potential bias, such as between survey mode and other sources of error. While most of our experiments were conducted across multiple countries, we cannot ascertain whether they would in all cases provide the same results when repeated for a different set of countries or at a different time.

Returning to this paper’s title, our study suggests that for vaccination campaigns in LMICs to be accurately informed, researchers and policy makers should pay close attention to discrepancies between different sources of data and the possibly diverging policy conclusions they purport. This study advances our understanding of the sources of such discrepancies in (phone) surveys and concludes that when adequately designed, they can become an important asset in the health data collection toolkit of researchers and policy makers alike.

32
work in low-skill occupations (in the non-primary sector) and have a lower household income than natives.

**Intermarriage** Refugees are more socially assimilated than recent Albanian immigrants in terms of intermarriage. Figure 3 reports the intermarriage rate of refugee women who were displaced at a young age, below 15. By 2001, almost 50% of refugee women got married with a Greek native. This represents a very high intermarriage rate in international comparison, with a typical rate between 2% and 25% among immigrant women in Western Europe in the 2000s (Lucassen and Laarman, 2009). In particular, the refugees’ high propensity to intermarry stands in contrast with Albanian immigrant women, who, despite living in Greece since childhood (15 or younger), have a less than 10% probability of getting married to a Greek native.

> 23The occupation of first-generation refugee cannot be observed in the 2001 census since most refugees are retired (being 79 or older).

15
the types of self-employed occupations that are typically promoted by policy efforts to create more jobs in sending regions, and our experimental studies are all either in urban settings or are trying to foster off-farm work. Secondly, both the migrant selection effects and causal impacts of working in agricultural work may be different from non-farm self-employment: for example, the idea of being able to take your business with you as you migrate is less viable for a farmer than for someone selling clothing or cooking food. After these restrictions, our total sample is almost 140,000 respondents.

Each of the panel surveys attempts to track individuals who move,<sup>9</sup> and migration is defined as having moved from one administrative area to another between survey rounds. The distance that constitutes ”migration” varies across studies (see online appendix B), so we further split migration into internal and international migration, and then examine the robustness of our results to different distances of internal migration. The samples we have are large, and allow tracking individuals over periods ranging from 3-4 years (Mexico), up to 14-15 years (Egypt, Indonesia). However, even with and a international is a rare long panels large sample, migration phenomenon, accounting for only 1 percent of our observations.

# _Does self-employed at home mean self-employed after migration?_

In their review of immigrant entrepreneurship, Fairlie and Lofstrom (2015) note that the “relationship between home country self-employment experiences and host country self-employment is not well understood”, and that a key reason is “due to a lack of suitable individual level data that includes pre-migration self-employment information”. Our panel data enable us to provide additional evidence on this question, by examining the extent to which self-employment at home makes it more likely to be also self-employed after moving. This also helps address the question of whether the self-employed can simply move their job with them.

Figure 1 puts together evidence from six countries for which we have information on type of occupation at destination for the majority