rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8702

# **Abstract**

The impact of urban form on economic performance and quality of life has been extensively recognized. The studies on urban form have focused in developed countries; only a few cities in developing countries have been studied. This paper utilizes nighttime lights imagery and information on street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. The paper classifies these cities into clusters according to these spatial metrics.

It also studies the relationship between the urban form metrics and some factors that can correlate with urban form (topography, size, colony, and economic performance) and performs a spatio-temporal analysis of urban growth from 1996 to 2010. Among the results, the paper highlights the identification of five typologies of cities, the tendency of a group of cities to grow at a steeper slope, some worrying cases of urban growth over protected areas, and a trend toward increasing sprawl in some Latin American and Caribbean cities.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at nlozano@worldbank.org and prestrepocadavid@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
# **REFERENCES:**

- Adams, R. (2003), _Economic Growth, Inequality and Poverty: Findings from a New Data Set_ , World Bank Policy Research Working Paper 2972, Washington, D.C.

- Bourguignon, F. (2002), “The Growth Elasticity to Poverty Reduction: Explaining Heterogeneity across Countries and Time Periods,” in T. Eicher and S. Turnovsky (eds.), _Growth and Inequality_ , Cambridge, Mass: MIT Press.

- Bruno, M., M. Ravallion and L. Squire (1998), “Equity and Growth in Developing Countries: Old and New Perspectives on the Policy Issues,” in Vito Tani and Ke-Young Chu (eds.), _Income Distribution and High Growth_ , Cambridge, Mass:  MIT Press.

- Kakwani, N.C. (1977), Measurement of Tax Progressivity: An International Comparison. Economic Journal, 87, 71-80.

- Pen, J. (1971), _Income Distribution_ , New York:  Praeger Publishers.

Pinto, B. (2002), Poland – Macro-Micro Policy Linkages, Unpublished World Bank Mimeo.

- Ravallion, M. (1995), “Growth and Poverty: Evidence for Developing Countries in the 1990s,” _Economics Letters,_ 48.

- Ravaillon M. (2001), _Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?_ , World Bank Policy Research Working Paper 2665, Washington, D.C.

- Ravaillon, M., and S. Chen (1997), “What Can New Survey Data Tell Us about Recent Changes in Distribution and Poverty?”, World Bank Economic Review, 11(2).

- Ravaillon M., and S. Chen (2003), “Measuring Pro-poor Growth,” _Economic Letters_ , 78.

Shorrocks, A. (1982), “Inequality Decomposition by Factor Components,” _Econometrica,_ 50 (1).

Theil, H. (1967),
from Haiti, 38,232 migrants from Colombia, 9,063 asylum seekers from Cuba and 3,986 refugees from the Syrian Arab Republic.

<!-- Start of picture text --> 2016 2017 2018 2019 2020<br>World Venezuela<br>Haiti Bolivia<br>Colombia Argentina<br>China<br>1,500<br>1,000<br>Total(’000)<br>500<br>0<br><!-- End of picture text --> Figure 1: Migration Trend in Brazil Source: Author’s calculation from SISMIGRA and STIMAR.

This section discusses the trends in Venezuelan refugees and migrants in Brazil, reports their key demographics and reviews the available literature on integration, especially focusing on Venezuelans in Latin America.

# **2.1 Refugee and Migration trends of Venezuelans in Brazil**

Figure 2 shows the trend of Venezuelan refugees and migrants in Brazil. Although the number of Venezuelans increased quickly from about 118,000 in 2018 to about 265,000 in the first quarter of 2020, the stock started dwindling after the onset of the COVID-19 pandemic. Concentrating on the monthly flows, we see that half of the Venezuelans, who entered Brazil, exited Brazil either to go back to the Republica Bolivariana de Venezuela or move onwards to other countries. When the pandemic hit, the movement across borders was restricted and we see that more Venezuelans were leaving than coming in and as a result the total number started falling. The Federal Police database also reveals that half of the Venezuelan refugees and migrants entered Brazil and requested temporary residence permits, while the other half registered as asylum seekers. Of the migrant and asylum seeker population, 95% have residence permits, as opposed to more permanent forms of stay. As of October 2020, there were 145,462 Venezuelan migrants and refugees, 96,556 Venezuelan asylum 5
Policy Research Working Paper 9517

# **Abstract**

This paper employs the synthetic control method to examine the impact of using a non-native language as the medium of instruction in schools on a student’s learning. Exploiting an unanticipated policy change in Malaysia and using data from the Trends in International Mathematics and Science Studies, the results show that changing the medium of instruction negatively influenced eighth graders’ achievement in mathematics and science. The differential performance, by year and gender, suggests that using a non-native language throughout a student’s schooling may have greater negative impact on the student’s learning than switching the language of instruction in the middle of the student’s schooling does. This paper sheds light on the various manners in which a language policy can adversely affect a student’s learning outcomes. It also highlights how the transition in switching the language of instruction in schools can be implemented more effectively to mitigate its adverse effects.

This paper is a product of the Global Indicators Group, Development Economics and the Poverty and Equity Global Practice in collaboration with the Department of Economics, Monash University Australia. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ysoh@worldbank.org, xdelcarpio@worldbank.org, or liang.c.wang@monash.edu.

_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
# 3. Methodology

# 3.1. Developing the Proxy Means Test models

PMT predicts consumption using verifiable and measurable proxies (variables) closely correlated with consumption. Using data from the national household surveys, it identifies the best predictor of the welfare variables from a set of independent variables such as location, housing quality, household characteristics, and ownership of durable goods and assigns weights to each proxy. Although the two main targeting models examined in this paper - the MOLSA-PMT-model and the CLCI-PMT model are similar in adopting the PMT design, the eligibility criteria, variables, and scoring procedure differ.

The MOLSA PMT model was developed from the 2017-2018 Rapid Welfare Monitoring Survey, also known as the Survey of Well-being via Instant and Frequent Tracking (SWIFT) (Sharma & Poi, 2019).<sup>7</sup> The CLCI, however, has two PMT models, a legacy Vulnerable Assessment model (Legacy -VA) and the updated MPCA model (SEVAT). The Legacy-VA differed considerably from the MOLSA-PMT. It estimates the income-to-earnings ratio while the latter estimates household per capita consumption. The Legacy-VA uses a stepwise binary regression model to select the proxies and adopts a single model for all regions. The MOLSA-PMT, in contrast, uses continuous per capita consumption as the outcome variable in a postLasso procedure and has separate models for each region. The SEVAT, which replaced the Legacy-VA and uses the 2018 Multi-Cluster Needs Assessment (MCNA) survey, is more similar to the MOLSA-PMT. Both have regional models and estimate household per capita consumption per month as a dependent variable.

The variables included in the three targeting models – MoLSA, Legacy-VA, and SEVAT equally differ (Figure 1). The MOLSA-PMT variables are mostly geographic location, durable assets, housing, and other socioeconomic variables that are closely correlated to monetary poverty. Conversely, the variables included in the two CLCI-PMTs (Legacy-VA and SEVAT) are mostly coping strategies and employment, which are more likely to predict vulnerability to poverty. It should be noted that vulnerability to
sup> s of the models reflects the elimination of the considerable noise in the sample data, which drove down the R<sup>2</sup> s of the models estimated using the simple random sample.

Finally, Appendix Figure A1 shows quantile-quantile plots for both components of the residuals, which is useful to check whether they appear to be normally distributed as assumed. The residuals are highly non-normal in most cases, except for the random area effect for male and female LFP. The non-normal random effects for unemployment, as well as non-normality in the household error terms may contribute to biased estimates of outcomes and uncertainty, which we will assess by comparing the small area estimates with the census. Future research can explore alternative transformations, such as the rank transformation used in Masaki et al.(2020), that might help achieve a more normal distribution.

# **B. State-Level Results**

We first consider estimates at the state level, the level at which the survey is considered to be representative. We present the seven statistics listed in section 2.e, separately for direct and small area estimates, in Table 3 .

The results are striking: Even though the direct estimates are considered to be reliable at the state level, we see large improvements in all seven statistics, across all four indicators, when incorporating geospatial data. The estimated mean squared error is smaller, the estimated correlation with the full-census benchmark increases, and the estimated absolute deviation from the benchmark decreases. Median relative bias declines for all outcomes. Coverage rates improve, sometimes dramatically. The estimates of unemployment rates improve on the direct estimates on most dimensions, even though the R<sup>2</sup> of some models is low. Because the outcomes measured in

> 21 For example, suppose as in this case that the sample covers 15 percent of the enumeration areas. Even a model that explains 10 percent of the variation in the remaining 85 percent of the population contributes at least the additional 8.5 percent
In China, evidence from China Health and Retirement Longitudinal Study (CHARLS) pilot suggests that, of urban residents over age 60, 79 and 54 percent of men and women, respectively, have access to pension

> 15Government Regulation No 32 (1979) specifies 55 as the retirement age for civil servants and became the benchmark for the “normal retirement age” followed in the private sector as well. Ministry of Labor Regulation No 2 (1995) stipulates that the normal retirement age is 55 and the maximum is 60, and these are also the ages at which eligible retirees are able to start collecting pensions.

> 16McKee (2006) finds that in Indonesia, half of government workers move into either the private sector or into selfemployment, and 61 percent of workers who leave their private sector jobs move into self-employment. In Korea, self-employment was one response to lay-offs from larger employers in the wake of the 1997/8 financial crisis (Sohn, 2007), and continues to be an important source of employment for older males (Lee 2009). Lee and Lee (2011) note differences in the retirement ages of self-employed and wage-salary earners, but do not discuss the incentive to move into self-employment ahead of mandatory retirement among wage and salary earners who wish to continue working.

9
account. The use of informal financial services, on the other hand, is widespread: 33% of workers had informal savings, such as keeping cash at home or with local savings groups, while approximately half of all participants had informal loans outstanding, typically at extremely high interest rates.

The baseline summary statistics also reveal extremely low trust in formal accounts. We asked respondents “ _would you be comfortable leaving BDT 1,000 in a [bank or mobile] account for 30 days?_ ”. Answers were given on a 10-point scale. Averaging across bank and mobile responses, the mean report is 3.7. Panel (a) of Figure 1 splits the sample into groups who report _>_ 5 versus _<_ = 5 on the trust question, averaged over account type. 73% of respondents fall into the low average trust category.<sup>31</sup> Figure 1, panel (b) shows that trust in formal accounts is even lower among respondents that have never used a bank or mobile account.<sup>32</sup> Figure 2 reports summary

> 29At one study location, the first follow-up survey was conducted before treatment assignments had been revealed. In that location, we restrict our sample to individuals still working in the factory at the time of that follow-up.

> 30In some of the follow-up rounds, we did not have the permission of the factories to survey the full set of participants. In these “thin” rounds, we randomized which workers were contacted. This explains why the set of workers with a full panel is much smaller than the set of workers who completed an endline survey.

> 31Focus group discussions suggest that workers are worried about being overcharged by financial agents in regular transactions as well as the lower probability event that an agent fraudulently uses personal account information to make withdrawals.

> 32This masks differences in trust between types of accounts. Trust in bank accounts is generally higher than trust in mobile accounts. This may be due to
causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea‐Bissau. They report higher malnutrition and higher mortality for children living in a non‐camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘normal’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non‐camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e.g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non‐refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. When general living conditions in one’s residence or home area are worse compared to a camp environment, e.g. because health services are available in the latter, mortality may also be lower in the camp.

The strong presence of children in Africa’s refugee population implies that we should also look at the potential long‐term effects of forced displacement on survivors. Given the composition of the refugee population, such long‐term effects will be more important in Africa compared to elsewhere. Few studies have followed children exposed to forced displacement over a long time to directly infer the long‐term effects of forced displacement, in particular on health, education and labor market participation. Most studies of the long term effects
finance ministries. Third, we classify each revenue source following the OECD’s tax classification (see OECD, 2020). Table B1 details the data sources used.

When available, OECD tax revenue data is our preferred source, because it covers and classifies all types of tax revenues, usually back to 1965 for OECD countries. OECD data accounts for 41% of the country-year observations in our dataset. Its drawback is its limited coverage of non-OECD countries: in total it covers 93 countries, and only over the past two decades.

To increase coverage, we augment the OECD data with the tax revenue data from the ICTD/UNU-WIDER (2020) (17% of observations). This dataset achieves near worldwide coverage but, for our purposes, faces limitations: it only starts in the 1980s; it does not follow the tax classification of the OECD; it sometimes mixes personal and corporate income taxes; and it often lacks payroll taxes and decentralized taxes. To address these shortcomings, we use historical public finance data from government reports, primarily from the Harvard Library archives (30% of country-year observations) and from the IMF GFS (2005) offline historical database (10% of observations).14 To stitch together country-by-country time series of tax revenues, we follow three principles. First, we aim to only rely on a maximum of two data sources by country: the OECD when it exists, and the alternative source with the best coverage over time and by tax type. Archival data is our second in priority since it often dis-aggregates revenue by source, and goes back to the 1960s. Our data hierarchy choice also depends on which source best matches the OECD data over their shared time frame. Second, we interpolate series with gaps, but only up to four years between two data points. Finally, we check country-specific policy reports and scholarly studies to triangulate across data sources and to identify events which may explain discordance across sources.

Tax revenues are disaggregated as finely as possible by source, according to the
to statutory property rights. A key feature of the model is that land tenure is risky and brokers who purchase land from customary owners have the capacity to formalize tenure and reduce insecurity. Information on tenure insecurity and on the ease of formalization may be symmetric or asymmetric across customary land sellers and brokers. Under symmetric information, brokers perfectly evaluate the idiosyncratic formalization probability of each land plot. As a result, we show that the share of customary land smoothly decreases with distance from the city center. Under asymmetric information, brokers are unable to evaluate these risks. As a result, there may be a failure in the land transformation market so that land transformation abruptly ceases at some distance from the city center. The paper then checks the predictions about tenure conversions using a geo-referenced survey of land plots in Bamako, Mali and its peri-urban area. The empirical analysis conrms the main features of the model and concludes that intermediaries may have asymmetric information on the plot that they purchase. Our conclusion regarding the existence of information asymmetry is even clearer for the left bank of Bamako and at a distance from paved roads, suggesting broker heterogeneity across locations.

To our knowledge, our paper is the rst to study the conversion of land use and land tenure in the light of an urban theoretical framework. Although we are able to provide an indirect but conclusive empirical test of the model using the limited available data for one particular city, there is a clear need for additional empirical studies to further describe, collect relevant data on, and analyze the ongoing process of urban expansion in Sub-Saharan African cities. Such studies will be necessary to understand in more detail the barriers associated with land use transactions and land tenure transformation, and, in line with our model, the specic informational barriers and information asymmetries that can aect the process. In this respect, improving information on risks and tenure conversion processes would help improve the eciency of the process. Equity issues regarding asymmetric bargaining power in land transactions, dispossession of customary owners through distressed
one and nine years. Information on the rest of the population relevant for the analysis – those eligible but not participating and those with _Sisben_ scores above the threshold of eligibility— is obtained from a census of the poor that was collected between 1994 and 2003 used to compute the _Sisben_ proxy-means test. Both datasets are merged together to create the sample of analysis for the RDD.<sup>24</sup> In practice, the _Sisben_ poverty score predicts substantial but not perfect changes in the probability of receiving the treatment. In fact, the data show a significant discontinuity in the probability of assignment to treatment at the threshold of around 66 to 72 percentage points as presented in Figure 1 and Table 3.<sup>25</sup> Therefore, we perform a _fuzzy_ instead of a _sharp_ RDD. The average causal effect of this design is given by the ratio of the jump in the outcome variable at the threshold to the jump in the probability of participation in FA also at the threshold. This is equivalent to an instrumental variable setting in which the average effect of the treatment is obtained from compliers -- individuals whose participation is affected by the cutoff. To check the sensitivity of the results to different specifications, estimates of program impacts based on the RDD are computed using different parametric functional forms and nonparametric procedures. Besides, to restrict the sample close to the cutoff, optimal bandwidths for the nonparametric analysis were also estimated using Imbens and Kalyanaraman (2010) methods.

Our first stage regression formally tests if the probability of treatment for individual _i_ , in municipality _m_ , and in year _t_ changes discontinuously at the cutoff point. We estimate different polynomial specifications of the model allowing the regression function to differ on both sides of the threshold as follows:

> 24 Given that the information system of the program does not  reports SISBEN scores for participant families, uniform scores for the whole sample are calculated by using  the proxy means testing  algorithm  and the information from the Census of the Poor 1994-2003. 25 This may happen