rafmacalaba/data-use-mentions-extended huggingface dataset:
We then create two alternative measures of pro‐immigration attitudes. The first variable ( _proimmigsame_ ) is equal to 1 if the individual would like many or some immigrants of the same race or ethnic group as the majority, and 0 otherwise (that is, if the individual would like few immigrants or none from the same race or ethnic group). The second variable ( _proimmigdiff_ ) is constructed following the same criteria used for _proimmigsame_ but it refers to immigrants of a different race or ethnic group from most people in the country. While _proimmigsame_ is less likely to capture compositional concerns regarding immigration and more likely to capture purely economic concerns, _proimmigdiff_ is more likely to capture a mix of both concerns regarding immigration (see Table 1). Since we find the same patterns using either _proimmigsame_ or _proimmigdiff_ , we report the results using _proimmigdiff_ only as the dependent variable. Figures reporting the results for _proimmigsame_ variable can be found in the Appendix.

# **4. Age and Cohort Effects in Attitudes toward Immigration: Estimation Results**

Figure 2 shows the estimated marginal effects of age from equation (1) using the dependent variable _proimmigdiff_ and two specifications – both excluding the set of individual controls, �� . The first one only includes age and survey year as explanatory variables (red line); we call this specification “cross‐sectional” as it captures the age patterns that typically emerge from cross‐ section survey data. The second specification incorporates age, year of birth and survey year dummy variables as explanatory variables (blue line). According to the model without any cohort controls, older people are less likely to exhibit positive attitudes toward immigrants than their younger peers in almost all countries considered. For instance, an 80‐year‐old individual in Poland is about 50 percent less likely to report positive attitudes toward immigrants than an 14
was built to be as representative as possible at the global level of the GHSL sample of cities. As a first check, the same regression is run using both the universe and the subsample. The within-estimator of the income and population elasticities of built-up is obtained for each sample and are shown to be very similar. These estimates can be shared on request.

> 9 Given that most surface is expected to be built around the highest point of the city, the measured height per pixel in this 5-kilometer radius will correspond to both the average over the buildings and the average for the total area of the pixel. DLR data provide height averages over the area of the pixel.

> 10 Population and Income from year 2000 are used for two reasons. First, height data have been collected between 2007 and 2011, with the year of collection reported in the data. Second, adding some lag in the explanatory variable can be thought as an instrumental strategy to deal with the endogeneity issues associated with the initial specification.

20
of automated variable selection, it selects the point on the accuracy-vs-enumeration-cost spectrum that maximizes model accuracy. This section revolves around the idea that other points – and especially earlier points – along the variable sequence should also be considered. A shorter questionnaire may be less accurate, yet it may result in lower overall exclusion due to the consideration of more households. We suggest methods which replace the single point with a range of options that allow policymakers to select a length-accuracy profile that is suitable for their conditions.

> 5Klasen et al. [2016] estimated exclusion errors for a forward stepwise regression model of 27% for a simulated 50% program coverage. The forward stepwise model constructed mirrors closely the approach taken in generating PMT rankings in the update of the UDB/DTKS in 2015. Appendix A shows a PMT exclusion error rate of 21% at 50% coverage.

13
unmarried men aged 18 to 40 who co-reside with both parents at baseline. The ENOE, which interviews households for five consecutive quarters, allows observing the occurrence of a migration episode from variations in the household roster across interviews, coupled with questions to remaining household members about the destination of the individual who has left the household. The restriction to individuals who co-reside with both parents is necessary to minimize the risk that the migration episode remains unrecorded because of the dissolution of the household of origin of the migrant (Bertoli and Murard, 2020), and the ensuing household attrition, but it clearly reduces the external validity of our

> 24Bertoli et al. (2023) provide evidence, drawing on the Biannual June Supplement of the CPS, that a substantial share of married couples of Mexican migrants in the U.S. have left one or more of their children behind; this data source does not provide information on the timing of the marriage, but these instances are most likely to relate to couples that got married before migrating.

26
LAC Region 28.7 7.2 3.602 1.618 66.4 56 23.1 6.2 2.762 1.285 69.6 42 16.4 7.2 2.102 1.005 73.5 25 1 Brazil 28.0 7.5 3.415 1.531 65.3 63 22.6 6.5 2.730 1.270 68.8 48 16.3 7.6 2.090 1.000 73.1 28 2 Central America 30.4 6.0 3.909 1.786 67.6 49 25.0 5.1 2.863 1.343 70.7 35 16.7 6.1 2.093 1.004 74.0 22 3 Caribboan and Other 30.2 7.5 3.725 1.623 65.3 60 23.4 6.5 2.795 1.275 68.5 42 16.7 7.3 2.149 1.021 72.6 26 4 Temperate and Tropical 26.2 8.2 3.416 1.559 67.7 51 21.1 7.2 2.633 1.231 70.5 35 15.9 7.9 2.090 1.000 73.9 18 Note: Crude birth rates (CBR)<sup>**and**crude</sup> death rates (CDR) are per<sup>1,000 population; total fertility</sup> rates (TFR) and net reprodcktion<sup>rates</sup> {"RR) are per woan 15-49 years of<sup>age; life expectancy at birth, E(0),isgiven in years; and infant mortality rates (INR) are per 1.000 live</sup> births. (See definitions in World Bank<sup>1988.) World Bank regions and country departments are defined in Table 14.
respondent selection can vary by context and experimental design — in a separate experiment from Ethiopia of 1,200 Fairtrade coffee households (Galdo et al., 2019), survey respondents were randomly selected to test, across three different seasons, the effects of proxy reporting on reporting of child labor (children themselves versus the household head or spouse). Proxy reporting was found to significantly underreport work of girls in agricultural settings, relative to children’s own reports.

Our paper builds on this emerging literature by testing whether implementing individual interviews makes a significant difference in the quality of labor reporting, using two concurrent, nationallyrepresentative surveys from Malawi. We also examine how individual and household characteristics vary with survey approach to affect labor reporting.<sup>3</sup> Large-scale studies on labor survey methods from developing countries are also critical, given changes in international definitions of work and employment under the 19<sup>th</sup> International Conference of Labour Statisticians (19<sup>th</sup> ICLS) in 2013 that, for the first time, expand the definition of “work” to include all paid and unpaid activity, but at the same time narrow the definition of employment strictly to

> 3 Other recent developing-country experiments have found that specific characteristics of respondents can matter in accuracy of estimates; recent studies surveying farm households on recall periods and labor reporting, for example, find that recall bias is lower among better-educated respondents (see Gaddis et al, 2019 for a study from Ghana, and Arthi et al., 2018, for findings from Tanzania).

4
as a proxy for the cost of _guanxi_ . ETC is an expenditure item in standard accounting books of firms in China, and it is a large sum, amounting to about 20% of the total wage bills in the sample firms of the investment climate survey of the World Bank. Cai, Fang and Xu (forthcoming) find strong evidence that

> 7 The two components are closely correlated.  We bundle them together to avoid multicollinearity.

13
# **Micro Dynamics of Turkey’s Export Boom in the 2000s**<sup>#</sup>

**Tolga Cebeci**<sup>a</sup> **Ana M. Fernandes**<sup>b</sup> The World Bank

**Keywords:** Export growth, Exporter dynamics, Entry, Exit, Intensive margin, Extensive margin, Turkey.

## **JEL Classification codes** : C81, F14.

> # We acknowledge the generous financial support from the World Bank research support budget. the Knowledge for Change Program (KCP), a trust funded partnership in support of research and data collection on poverty reduction and sustainable development housed in the office of the Chief Economist of the World Bank <u>(www.worldbank.org/kcp) as well as from the</u> governments of Norway, Sweden and the United Kingdom through the Multi-Donor Trust Fund for Trade and Development. We thank Turkstat President Birol Aydemir, and staff Doğan Böncü, Bülent Tungul, Şahin Çelik, Akın Bodur, Nusret Kılıç, Nilgün Arıkan and Sabit Cengiz Ceylan for allowing us access to the micro data and conduct the empirical analysis in their premises. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank. aConsultant, Trade and International Integration Unit, Development Research Group, World Bank (tcebeci@worldbank.org).

> bSenior Economist, Trade and International Integration Unit, Development Research Group, World Bank (afernandes@worldbank.org).
**Table 1: Data Sets Used in this Report**

|**Data Title**|**Year(s)**|**Type**|**Source**|
|---|---|---|---|
|**Demographic and Health Survey**|2011|Household<br>Survey|CSA|
|**Urban Bi-Annual Employment and**|2009, 2010, 2011,|Household|CSA|
|**Unemployment Survey**|2012|Survey||
|**Large and Medium Scale Manufacturing**<br>**and Electrical Industries Firm Survey**|1999-2010|Firm Census|CSA|
|**Welfare and Monitoring Survey**|2011|Household<br>Survey|CSA|
|**Household Income and Consumption**<br>**Survey**|2011|Household<br>Survey|CSA|

Notes: XXXX-YYYY: Refers to panel data; XXXX, YYYY: Refers to cross-sectional data.

Data on firms, industries<sup>4</sup> and employment within the report are taken from different data sources. All of the data used in the report are taken from surveys undertaken by the Ethiopian Central Statistical Agency (CSA). The _Large and Medium Scale Manufacturing and Electrical Industries Survey_ (LMMIS) covers all establishments with 10 or more employees that use power-driven machinery, often manufacturing firms. It is undertaken in over a 100 of the largest towns and cities in Ethiopia, which relate to the urban administrations.<sup>5</sup> The _Urban Bi-Annual Employment and Unemployment Surveys_ (UBAEUS) were conducted first in 2003, 2004 and 2006. After 2009, they have been conducted annually. The survey provides information on the economic characteristics of the population aged 10 years and over, including their activity status, employment, and unemployment situation. It also covers detailed sociodemographic background variables such as
region A are expensive three-bedroom apartments. Both mean and median values of housing rents are indeed lower in region B than in region A. If one assumes housing stock is unlimited like other goods, then the hedonic regression approach may work. Otherwise, it would be better not to control for quality differences in measuring housing costs in spatial price adjustment. This statement sounds contradictory to our emphasis on the detailed product specification information in other goods and services. This is because housing is a unique case in which its supply and stock are limited in a given market.

**Table 2. A simple example of housing** **<u>price index</u>**

||Reg<br>|ion A<br>|Reg<br>|ion B<br>|
|---|---|---|---|---|
||Units|Rent ($)|Units|Rent ($)|
|One-bedroom|10|1,000|40|1,500|
|Two-bedroom|10|3,000|10|4,000|
|Three-bedroom|40|5,000|10|6,000|
|Price index based on hedonic regression|60|1,000|60|1,310|
|Mean rent|60|4,000|60|2,670|
|Median rent|60|5,000|60|1,500|

_Note:_ Number of housing units is expressed in thousands.

# **2.3. What are the alternatives to the standard price index approaches? How are they better?**

With the aforementioned methodological discussion in mind, we introduce here the CPD approach based on the CPI price data. Before getting there, however, we briefly touch upon another approach in spatial adjustment, which calculates a spatial price index based on multiple poverty lines.

## _Multiple poverty line approach_

An alternative and
a new flexible tool to simulate various pension reform plans in a general equilibrium setting. We differentiate production and employment by ownership type and organize the demographic data in a matrix with four dimensions. We then conduct three sets of simulations with 18 experiments, (1) limited changes within the current system (no structure reform), (2) a structural reform (phasing out notional individual account) and financing the transition cost by taxes, (3) various options after the proposed structural reform including levying a new social security tax. Results from simulations are promising and summarized in the executive summary.

Our simulation results provide useful insights for the design of Chinese pension system reform and illustrate how CGE models can be valuable tools for evaluating different pension policies. However, several important limitations need to be mentioned. First, few CGE models have been able to incorporate rational expectations into the behavior of agents (though there are perfect foresight models). In our recursive dynamic model, agents adjust their behavior according to information received in the last period. Thus, the existence of a future pension benefit will not affect agents' saving behavior today. Second, the model does not specify explicitly the different behavior rules of the various types of firms in China, but assumes that all operate to maximize profit. A third limitation is that the model may overestimate the impact of various pension reform policies on macroeconomic variables because it does not include an explicitly specified financial market. Gross national savings, including pension reserves, are assumed to become gross investment, implying a perfect capital market, which is far from the reality in China. It is assumed that pension funds can earn interest that is equal to the average return on capital, except that in experiment 4 and 5 interest rates are assumed as zero. To our knowledge, only one paper had attempted to incorporate financial market into a 33
# **1. Introduction**

Protracted displacement — displacement for five years or more — is ubiquitous and a programming priority. For instance, in more than 50 countries around the world, people were living in internal displacement for more than 10 years in 2014 (Kälin and Chapusiat, 2017). Moreover, 78% of all refugees worldwide were in protracted situations in 2018.<sup>1</sup> Policy makers have recognized the urgent need to act and to prevent these millions of people from being left behind. For example, the Agenda for Humanity and the 2016 World Humanitarian Summit or the 2018 UNHCR Global Compact on Refugees signed by the UN general assembly focused on the need to adopt a new approach to protracted crises that addresses displacement needs, fosters durable solutions and embraces gender equality as a central principle.

A surge of studies in recent years has produced detailed insights into the characteristics, causes, and consequences of forced displacement (Ruiz and Vargas-Silva, 2013; World Bank, 2017). Specifically, it is increasingly recognized that risk factors for gender equality are often especially pervasive in situations of forced displacement (Rohrwerder, 2016), and that impacts of gender and forced displacement can not only add to but also interact with each other (Arango et al., 2021).

Yet, quantitative empirical research and micro-level evidence on the gendered causes, forms and consequences of livelihoods during displacement remain scarce, which is at least partly due to a lack of suitable, high-quality large-n survey data (ISDC, 2019; Brück and Stojetz, 2020). Specifically, how the two dimensions of gender and forced displacement combine and interact is not well understood in particular (for a detailed review, see Arango et al., 2021). Knowledge is particularly limited for protracted situations, which, due to the long time horizons, can severely compound and create new gender-based vulnerabilities and disparities due to displacement. Little is known about the livelihood strategies of men and women in protracted displacement and how they compare to
this paper, we develop and compare two versions of the CO2 tracking template. The first tracks changes in residuals after fitting the econometric model to satellite-based observations, while the second version simply tracks changes in the observations themselves. Using data from thousands of urban areas, we find an extremely close correspondence between results for the two versions. We opt for simplicity and select the second version for template development. We also introduce a technique for identifying city-level changes that are distinct from regional changes induced by broader atmospheric circulation patterns. We provide several illustrative applications for urban areas, while noting that the same approach could be used for any other areas of interest.

In an additional exercise, we use our regression model results to compute expected emissions from urban areas. The regression residuals identify the directions and relative magnitudes of departures from expected values for individual areas. We convert the residuals to their emissions equivalents using high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For 1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose emissions are higher and lower than their expected values. Converting deviations to percentages of expected values, we find that percent deviations are typically greater in absolute value for cities with lower-than-expected emissions.

The remainder of the paper is organized as follows. Section 2 motivates the comparison of tracking models by reviewing the econometric model and supporting data from Dasgupta, Lall and Wheeler (2022). Section 3 develops the candidate tracking models and Section 4 compares their results for a large sample of urban areas. Section 5 applies the selected model to illustrative urban cases, while 2
. Smaller exporters are more affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. Macedoni and Weinberger (2019) use a large panel of countries to show that countries have fewer exporters selling to destinations with larger numbers of standards,

> 9To rationalize their results, they propose a trade model with heterogeneous firms and endogenous adoption of product standards. Product standards reduce information asymmetries and ensure the compatibility of inputs and devices and this increases demand by consumers. This increases firms’ incentives to produce goods that meet the standard despite high investment costs and higher marginal costs to produce meeting the standard. Their model’s prediction is that a new standard (through harmonization) leads to both entry of exporters as well as increases in exports of existing exporters.

7
Political Stability (percentile rank : 0 to 100) <!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br><!-- End of picture text --> _<mark>Source:</mark>_ <mark>Worldwide Governance Indicators (WB).</mark> _<mark>Definitions:</mark>_ <mark>Percentile rank ranges from 0 (lowest) to 100 (highest).</mark> _<mark>Regions:</mark>_ <mark>EAP (East Asia & Pacific); ECA (Europe & Central Asia); LAC (Latin America & Caribbean); MENA (Middle East & North Africa); SA (South Asia); SSA (Sub-Saharan Africa).</mark> The next sections delve more deeply into the region’s governance record. They look at four building blocks of inclusive and effective governance, namely: (i) state effectiveness and capability; (ii) inclusive and accountable political institutions; (iii) civic capacity; and (iv) political stability. More disaggregated data provide some nuance to the governance diagnostic. It helps identify some positive trends that are hidden by aggregate variables, such as the progress made on selected fundamentals of government effectiveness (including public financial management and revenue generation), the durability of democratic (and electoral) processes, and the decline in interstate wars. Yet, it also shows that, despite this trio of advances, progress has often been subject to reversals and stagnation, and many areas of governance continue to lag behind.

#
income generation. In fact, it is the variable with the single highest impact on income developments of the bottom 40 percent. This result holds even when the possible effect on mean income is factored in.

Improvements in school enrollment seem to be associated with faster income growth for the two poorest quintiles than better infrastructure, given a certain growth in mean income. However, physical capital seems to be correlated with faster mean income growth than education, so once the indirect effects through average growth are included, this ranking is reversed. The importance of a stable macroeconomic environment is also enhanced once its indirect association with average income growth is included. A stable political environment, as measured by the democratic accountability index, is associated with faster overall income growth, but not with any change in the income distribution.

An alternative approach would be to define a meaningful reference group to implement the benchmarking exercise. Several previous studies performed a benchmarking exercise of overall growth rates. For example, Loayza et al. (2005) aim to explain differences in Latin American and Caribbean growth rates over the 1990s by benchmarking key policies against the best performing country over that period, Chile. Building on this idea, Araujo et al. (2014) developed a benchmarking exercise that serves as the key reference point of our analysis.

The main idea in Araujo et al. (2014) is to derive a benchmark in key policy areas (such as education, infrastructure, and stabilization policies) which is defined as the 90<sup>th</sup> percentile best performer in this variable in the region i.e., the benchmark is set in a way that only 10 percent of regional peers perform better in that particular policy dimension. The growth impetus of these policy variables is identified using a cross-country regression model. By multiplying the difference in policy variables between the country of interest and the benchmark (policy gap) with the estimated growth regression parameter, this exercise provides a counterfactual per capita income for the country of interest, assuming