rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7519

# **Abstract**

Despite significant improvements in per capita expenditures and a marked decline in poverty over the 2000s, a large fraction of Eastern Europe and Central Asia’s population reports their economic situation in the late 2000s to be worse than in 1989. This paper uses data from the Life in Transition Survey to document the gap between objective and subjective economic mobility and investigate what may drive this apparent disconnection. The paper aims at identifying some of the drivers behind subjective perceptions of economic mobility, focusing on the role of perceptions of fairness and trust in shaping people’s perceptions of their upward or downward mobility. The results show that close to half of the households in the region perceive to have experienced downward economic mobility, that is, that their position in the income distribution has deteriorated. The results also show that perceptions of higher inequality, unfairness, and distrust in public institutions are associated with downward subjective economic mobility. The findings from this study confirm that factors beyond objective well-being are associated with the perceptions of mobility observed in Europe and Central Asia and may explain why the region has had such a pessimistic view of economic mobility during the past two decades. Understanding what drives people’s perceptions of their living standards and quality of life is important, because regardless of objective measures, perceptions could influence people’s behavior, including support for reforms and labor market decisions. For Eastern Europe and Central Asia, a region that has undergone substantive transformations and which is still going through a reform process, accounting for these aspects is critical.

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 ccancho@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 Research Support Team
The coefficients of interest in this regression are 1, 2, and 3, which give the impact of each treatment on the difference in outcomes before and after the individual intervention in Group C ββ ββ ββ villages.

The advantage of the within-subject design is that it potentially improves power by controlling for individual-specific levels of the outcome pre-intervention. However, the disadvantage is that there could be an anchoring effect, since individuals are asked the questions only about one hour apart. They may therefore anchor their post-intervention responses on their pre-intervention responses and not change them as much. The across-subject design is not subject to this concern, but may have larger variance.

# **4.2 Follow-up survey data**

Our primary hypothesis was that our three treatments would reduce the likelihood of backway migration from The Gambia, increase the likelihood of migration to Senegal and internal migration, and improve the well-being of the young men in our sample. We pre-specified primary outcomes in three domains to test this. The first domain consists of measures that capture steps, attempts, and actual backway migration to Europe. The second domain consists of measures of migration to Dakar and other parts of Senegal, and of internal migration. The third domain consists of broader measures of well-being, such as life satisfaction, experiences of violence, and monetary income. Appendix B details how these outcomes are measured.

These outcomes were measured by a three-step endline survey, which took place between September and November 2020, approximately 18 months after the information intervention, and approximately six months after COVID-19 had led to border closures worldwide. We first conducted phone calls to obtain the current location of half of the youth who were interviewed in the baseline survey. In case of repeated unsuccessful call attempts, the household head was contacted and surveyed. This provided a first data point on migration for those contacted, as well as helped plan the logistics for in-person surveying. We then conducted face-to-face surveys, attempting to interview in-person all youth interviewed in the baseline survey. These surveys asked detailed questions about migration episodes and attempts, work, well-being, and the influence of the COVID-19 pandemic. We also administered an elder survey in each of the 391 settlements, during which the most knowledgeable elder (generally the village leader) was interviewed. The third step was an extensive tracking approach through a second round of phone surveys for all 24
# **Table 5** Benchmark Parameter Values

This table summarises the parameter values used for the calibration exercise. Rate of depreciation, mean trend growth rate, and volatilities of trend growth rate and transitory component of TFP are in percentage (%).

|Parameters||Values|
|---|---|---|
|Discount factor|β|0.968|
|Rate of Depreciation|δ|5.000|
|Share of labour|α|0.700|
|Adjustment cost<br>parameter|φ|2.820|
|Mean trend growth rate of<br>labour productivity|1<br>_g_<br>μ −|2.790|
|Persistence in transitory<br>component of technology|_c_<br>ρ|0.760|
|Volatility in transitory<br>component of technology|_a_<br>σ|0.320|
|Persistence in growth of<br>permanent component of<br>technology|_g_<br>ρ|0.266|
|Volatility of shock to<br>permanent component of<br>technology|_g_<br>σ|1.590|

_Source_ : Virmani (2004), Verma (2008), Aguiar and Gopinath (2007), and authors’ estimates outlined in the Consumption Volatility and Permanent versus Transitory Income Shocks section and in the Case Study section 44
framework, we estimate: where _Yij_ is a dummy variable that takes 1 if a woman _i_ in village _j_ receives a vaccine; _V accineCondition_ & _Fear_ =1 if a woman _i_ is shown the “scared straight” flipchart, rather than the control flipchart. Clinic condition is controlled for; thus, Vaccine condition is the comparison group.<sup>54</sup> Recall that all respondents shown the “scared straight” flipchart were offered cash incentives under Vaccine condition. We also examine the potentially differential effects of priming by the amount of cash incentives through the interaction terms between the priming intervention and the amounts of cash incentives Priming does not significantly influence vaccination. The point estimate for the effect of the priming about disease severity (Vaccine condition & Fear) is negative with no statistical significance (Table 3 column 1). The interaction terms between the priming intervention and any amount of cash incentives also yield insignificant point estimates (column 3). These results indicate that the priming intervention does not influence vaccination at any amount of cash incentives.

We then evaluate if the priming increased women’s perception of the severity of the disease.<sup>55</sup> Estimating equation (5) with perceived severity of tetanus as a dependent variable reveals that the priming did increase respondents’ perceived severity at the post-intervention individual survey done at respondent’s house (Table 4).<sup>56</sup> Women under Vaccine condition & Fear were also more likely to feel frightened, tense, nervous, and uncomfortable than others (Appendix S8). The priming also induced women’s emotional response according to the objective measure: Those who viewed the “scared straight” flipchart had a higher heart rate, by 6.55 beats per minute, than those who viewed the control flipchart (Table 5 column 7). These results provide evidence that the priming is salient enough to increase the perceived severity of the disease.<sup>57</sup> The priming is ineffective in promoting vaccine take-up arguably because psychic costs of vaccination are not 19
get priced out with fare increases. Reduction in inequality, when observed, hides how much further from being able to afford transit services some of the poorest households are, an element that we also document in this study.

The paper is structured as follows. Section 2 presents the datasets and methods used to conduct the study, including the use of Pareto frontiers that represent specific itineraries that optimize for both travel costs and time. Section 3 presents the results of the study. It first presents how average accessibility varies as a function of travel budget and time thresholds in Rio de Janeiro and Kinshasa. Second it discusses how these average accessibility profiles are impacted by fuel price increases ranging from 0 to 500%. Thirdly, this section disaggregates these impacts both spatially and by income group, to document who and where are the most affected. The subsequent sub-section specifically looks into inequality in accessibility levels between the wealthiest 10% and the poorest 40% and how fuel price increases impact these as a function of travel budget thresholds. The fourth results sub-section looks specifically at households that might be excluded from using transit services altogether because of fare increases, where they reside and which income groups they belong to. Section 4 summarizes the rich set of results derived in this study, discusses them and points to some avenues for pushing ambitious climate targets while mitigating the negative consequences that they might entail.

# 2 Materials and methods

Socio-economic data, including the location of jobs and households, was derived from various household and travel surveys, and merged with transit data in General Transit Feed Specification (GTFS) format and street network information from OpenStreetMap (OSM). The transport data was processed using r5r (Pereira et al., 2021) to conduct baseline analyses of accessibility to economic opportunity and derive Pareto frontiers (all optimal itineraries between each origin-destination combination optimized on both travel time and travel budget) for every origin-destination combination within the two cities.

In order to better understand the nuance around the impact of fuel price increases and their impacts on specific communities as well as to identify potential mechanisms for reducing negative outcomes, the analysis was split into five components: Identifying baseline accessibility behavior, assessing the sensitivity of overall accessibility to fuel price increases across both cities, identifying specific locations within cities and income groups that may be more severely impacted, assessing inequality (utilizing the Palma ratio as a metric) and assessing the exclusion of residents from accessing jobs. Figure 1 shows a high-level overview of the workflow of this study. Each of these processes and the corresponding datasets are described in the data and methodology sections below, however, additional details are included in Appendix A – Detailed Data Description and Appendix B – Detailed Methodology.

4
a — a-1 gt — Sgt—5 _ a-1 a-1 ~ gjt-5 jt-5 a — gi g=m a t a=1:15-19 a = 2:20— 24, t-5 Gita gta —= Lij( gjt-sa + Aenrollsa ;,) Durjr_sa _=D (giesa jft-s+a Aenrollgj,a js)a —=aaa Soest djAenrollgi, — jt-s a t a— <u>Pope Pop?</u> c =Sr, oa X ~~—yt~~ +S a x ~~—m,t~~ fit Pop? mt Pope _y1o a <u>Pope</u> t — Ya=1 t x <u>Pop</u> t(15-64) t-—5 <!-- Start of picture text --> (15-64)<br><!-- End of picture text --> t
9 ∂E�⁄∂S�, the effect on years of schooling achieved by the date of entry into the labor force of being stunted in childhood; ∂H�⁄∂S�, the effect on height in adulthood of being stunted in childhood; and ∂C�⁄∂S�, the effect on cognition in adulthood of being stunted in childhood. The results of our literature search are shown in Table A1 in the Annex. The parameter values we decided on based on that literature search are summarized in Table 1.

<u>Table 1: Parameters used in estimating the cost of childhood stunting</u>

|_Parameter _|Assumed value|Explanation/ source|
|---|---|---|
|_Effects of stunting on:_|||
|Education (∂E�/∂S�)|-1.594 fewer years of education|See Table A1|
|Height (∂H�/∂S�)|-5.981 cm shorter|See Table A1|
|Cognition (∂C�/∂S�)|-0.625 SD lower cognition|See Table A1|
|_Returns to:_|||
|Education (_r_)|Region-specific return per extra<br>year of education|Montenegro and Patrinos (2014)|
|Height ()|1.7% extra income per extra cm|See Table A1|
|Cognition ()|4.3% extra income per extra SD|See Table A1|
|_Elasticity of income with respect_|0.67|Hanushek and Woessmann (2012)|
|_to human capital, i.e. labor share_|||
|_(__)_|||

We set  equal to 0.666 (Hanushek and Woessmann 2012). For the returns to education parameter, _r_ , we use the results from Montenegro and Patrinos (2014); we use their Table 3a which shows average returns across men and women for each World Bank region. For the other parameters, we averaged the parameter estimates across the studies in Table A1, giving a weight of 5 to the estimates based on the COHORTS study since these estimates are derived from data from five developing countries (India, Guatemala, India, Philippines and South Africa). Panel A in Table A1 provides micro estimates of the effect of having been stunted in childhood on adult and adolescent height, in centimeters ∂H�⁄∂S� , as well as the returns to height on earnings in the labor market (γ) conditional on years of schooling. Most estimates are drawn from longitudinal studies that have both stunting at childhood and earnings. The effects of being stunted in childhood on attained adult (or adolescent) height are very similar when looked at as unconditional associations, or as conditional
11 ���� ����� , i.e. the subsidy distribution entirely reflects the utilization distribution.

# III. **Illustration Using Data from Vietnam**

This section shows the implications of the three different approaches outlined above for the case of Vietnam. The incidence of government spending in Vietnam has been the subject of several studies (Prescott 1997; Wagstaff 2005; O'Donnell et al. 2007; O'Donnell et al. 2008), all of which show a pro‐rich distribution of government health spending.

## **_Data and assumptions_**

The household‐level data for this study are taken from the 2006 Vietnam Household Living Standards Survey (VHLSS). In 2006, according to Vietnam’s NHA<sup>1</sup> , 60% of health spending in Vietnam was financed via out‐of‐pocket payments. According to the VHLSS, over half were payments to public providers, the rest being split fairly evenly between spending on drugs and payments to private providers (mostly private clinics rather than hospitals) (Lieberman and Wagstaff 2009 p6). The survey records outpatient visits over a period of a year to the principal government providers of outpatient care, namely commune health centers (CHCs), polyclinics, and government hospitals (district and provincial). For each visit, respondents are asked to record the out‐of‐pocket payments associated with the visit. The survey also records inpatient admissions to the main government provider of inpatient care, namely government hospitals. Respondents are also asked to record the out‐of‐pocket spending they incurred during the admission. The survey thus has good data on _qk_ and _Fk_ for the four major government 1 The data can be downloaded from <u>http://www.who.int/nha/country/vnm/en/.</u>
- 28 -

# Table 10: DIAGNOSTICS, TESTS AND EQUILIBRIUM ELASTICITIES OF **TiE** NIESIRICTED ESTIMATIONS

|||Ln IlCP33|LnICPAG|LnICPMF|LniCPAGF|LnifPAGNF|
|---|---|---|---|---|---|---|
||h|26|22|'4|24|24|
|R2||0.786|0.881|0.689|<br>0.827|<br>0.845|
|**s.o.**||0.058|0.045|0.087|<br>0.059|<br>0.070|
|IN:test for serial<br>correlation|F13,hl|<br>0.54|4.28|1.46|0.62|0.76|
|Normality|X2|1.370|0.365|0.502|<br>1.673|<br>1.642|
|Forecast Stability<br>Chow: 1979-88|FllO.hl|<br>1.09|1.410|<br>1.300|<br>1.940|<br>1.490|
|Test against restricted<br>equations 1/|Fll,hl|<br>0.474|0.533|0.612|<br>0.881|<br>0.371|
|Long-|Run<br>Solutio|ns<br>of TRB from|the Unrestr|icted<br>Estima|tes||
|TRB||-5.682|-5.804|<br>-4.630|<br>-4.283|<br>-3.681|
|LnPOIL|||||0.016||

Note: h is the number of degrees of freedom for the test.

I/ These results are in Table 7.
# **Trade Diversion under**

# **Selective Preferential Market Access**

## Ingo Borchert<sup>_∗_</sup>

August 2008

**Abstract** . Through its diverse trade preference schemes, the European Union provides different groups of developing countries with different degrees of market access. This paper is the first to demonstrate empirically that such staggered market access induces sizable trade diversion to the detriment of relatively less preferred beneficiary countries. In particular, preferences granted to African, Caribbean and Pacific economies are shown to impair the export performance of seven developing countries whose products only qualify for basic preferences under the Generalized System of Preferences. Exports to the European Union decline by about 30 percent if the African, Caribbean and Pacific tariff falls by 10 percentage points. In terms of forgone trade volume, losses for these relatively disadvantaged countries amount on average to 9 percent of their total trade with the European Union, depending on the country and its main exports. These intra-developing country distortions are driven by highly substitutable, often labor-intensive commodities.

_Keywords:_ trade diversion, trade preferences, GSP, regionalism _JEL-Codes:_ F13, F15

> _∗_ Development Research Group, The World Bank, 1818 H Street NW, Washington D.C. 20433, phone: +1-202-4588526, email: `IBorchert` @ `worldbank.org` .

This research was mainly conducted while the author was with the University of St.Gallen, Switzerland. He is indebted to Heinz Hauser for valuable advice on an earlier draft, and to conference participants at the European Economic Association and the European Trade Study Group for many helpful suggestions. Data support from the International Trade Centre, Geneva, is gratefully acknowledged. The findings, interpretations, and conclusions expressed herein are entirely those of the author and should not be attributed to any of the aforementioned persons or institutions. The author assumes sole responsibility for any remaining errors.
asset index, whilst confirming the earlier conclusion that the two approaches (imputed welfare v asset index) are measuring different dimensions of welfare and that the asset index may be reflecting ownership of assets laid down in the past rather than current welfare.

**Table A2: Result of household economic index, urban area.**

||_(2)_<br>_Mean_|_(3)_<br>_Standard_<br>_Deviation_|_(4)_<br>_Eigenvectors_<br>_of first_<br>_principal_<br>_components_|_(5)_<br>_Scoring_<br>_factor/_<br>_Std._<br>_Dev. _|_(6)_<br>_Score if_<br>_they_<br>_have_<br>_asset_|_(7)_<br>_Score if_<br>_they don’t_<br>_have asset_|
|---|---|---|---|---|---|---|
|1. Gas Line|0.792|0.405|0.353|0.872|0.181|-0.690|
|2. Gas Cylinder|0.139|0.346|-0.277|-0.801|-0.689|0.111|
|3. Electric Oven|0.188|0.390|-0.233|-0.597|-0.485|0.112|
|4. Heating System (private, public)|0.110|0.313|0.116|0.371|0.330|-0.041|
|5. Stove|0.171|0.376|-0.294|-0.782|-0.648|0.134|
|6. Water|0.835|0.370|0.341|0.922|0.152|-0.770|
|7. Hot water|0.068|0.252|0.148|0.587|0.547|-0.040|
|8. Sewage System|0.695|0.460|0.443|0.963|0.294|-0.669|
|9. Bathroom|0.625|0.483|0.402|0.832|0.312|-0.520|
|10. Telephone|0.414|0.492|0.213|0.433|0.254|-0.179|
|11. Total living area (square meters)|57.82|46.99|-0.055|-0.001|**|**|
|12. Number of room|2.37|1.55|0.042|0.027|**|**|
|13. DwellingStructure(separate house,no)|0.440|0.496|-0.258|-0.520|-0.291|0.229|
|14. Ownershipof dwelling|0.594|0.491|-0.167|-0.340|-0.138|0.202|

**Table A3: Result of household economic index, rural area.**

||_(2)_<br>_Mean_|_(3)_<br>_Standard_<br>_Deviation_|_(4)_<br>_Eigenvectors_<br>_of first_<br>_principal_<br>_components_|_(5)_<br>_Scoring_<br>_factor/_<br>_Std._<br>_Dev. _|_(6)_<br>_Score if_<br>_they_<br>_have_<br>_asset_|_(7)_<br>_Score if_<br>_they don’t_<br>_have asset_|
|---|---|---|---|---|---|---|
|1. Gas Line|0.310|0.462|0.122|0.264|0.182|-0.082|
|2. Gas Cylinder|0.303|0.459|0.190|0.414|0.289|-0.125|
|3. Electric Oven|0.321|0.467|0.081|0.173|0.118|-0.056|
|4. Heating System (private, public)|0.063|0.244|0.018|0.074|0.069|-0.005|
|5. Stove|0.681|0.465|0.161|0.346|0.110|-0.236|
|6. Water|0.261|0.439|0.107|0.244|0.180|-0.064|
|7. Hot water|0.002|0.053|0.032|0.604|0.603|-0.001|
|8. Sewage System|0.037|0.190|0.038|0.200|0.193|-0.007|
|9. Bathroom|0.068|0.252|0.127|0.504|0.470|-0.034|
|10. Telephone|0.076|0.265|0.143|0.540|0.499|-0.041|
|11. Total living area (square meters)|75.07|47.38|0.437|0.009|**|**|
|12. Number of room|2.37|1.42|0.406|0.286|**|**|
|13.Dwelling Structure(separate house,no)|0.899|0.301|0.506|1.681|0.170|-1.511|
|14. Ownershipof dwelling|0.931|0.252|0.501|1.988|0.137|-1.851|

66
capture how the impact of maternity leave on female workers varies with the level wage rate and labor productivity of the firm.

# 3.6 _Baseline_ c _ontrol variables_

The maternity leave variable does not change over time for most countries in our sample. Thus, the identification of the female workers and maternity leave relationship (henceforth, main result) is based on cross-country variation. It is well-known that identification based on cross-country differences is more prone to endogeneity problems than changes over time for a given country. Thus, we provide several layers of defense against endogeneity concerns. To begin with, we control for several determinants of female workers that may also vary systematically with the duration of maternity leave. The motivation for the controls comes from the existing literature on the drivers of female employment and is discussed in detail in section 2 above.

The controls used in the baseline model are as follows (see table A2 in the Appendix for a detailed definition): dummy variables indicating the year the ES was conducted (Year fixed effects); dummy variables indicating the industry (2-digit, ISIC Rev. 3.1) to which the firm belongs (Industry fixed effects)<sup>6</sup> ; log of GDP per capita last year (one-year lagged) taken from WDI; annual growth rate of GDP per capita taken from WDI; percentage of females in total adult population last year taken from WDI; fertility rate last year obtained from WDI; gender parity in primary education proxied by the ratio of female primary education enrollment rate to male primary education enrollment rate averaged over the last five years and taken from WDI<sup>7</sup> ; freedom from business regulations last year taken from Fraser Institute; index of gender parity in workplace laws

> 6 There are 46 industries at the 2-digit level in the baseline sample.

> 7 Data on primary education enrollment rates are not available for all countries for each year. Thus, we use average values of the variable over the last 5 years for which data are available to minimize missing values.

23