rafmacalaba/datause-encoder-data huggingface dataset:
the slope and intercept of the IGRC or IGC equation in most of the cases. The truncation bias in the rank-based absolute mobility estimate is lower in 80 percent of cases in both Bangladesh and India.

The evidence in this paper suggests that when working with standard household surveys it is, in general, better to rely on rank-based measures of relative and absolute mobility. The results can be helpful and reassuring for many researchers who, following the lead of Chetty et al. (2014), have adopted the rank-based measures to understand intergenerational mobility in developing countries (see, for example, Fan et al. (2015), Asher et al. (2017)). The rest of this paper is organized as follows. Section 2 provides a discussion on truncation due to coresidency in household surveys in developing countries. The next section discusses the standard measures of relative and absolute mobility widely used in the literature. Section (4) is devoted to data and variables definitions. The estimates of truncation bias for different samples are reported and discussed in section (5). Section (6) provides a brief discussion on possible intuitions behind the results. The paper concludes with a summary of the findings and the implications for research on intergenerational mobility in developing countries.

# **(2) Coresidency, Household Definition, and Sample Truncation in Household Surveys in Developing Countries**

The availability and quality of household surveys in developing countries have improved substantially over last few decades. For example, starting from early 1980s, the Living Standards Measurement Study (LSMS) has generated high quality household surveys in more than 40 countries, while the Demographic and Health Surveys (DHS) cover more than 90 countries. Although there is no uniformity in the definitions of ‘household’ across different surveys, almost all of the surveys use coresidency criteria to define household membership such as ‘living together’, ‘eating together’, and sometimes ‘pooling of funds’ (United Nations (1989), Deaton (1997)). For example, according to the Tanzania DHS, “a household is defined as a person or group of persons, related or unrelated who live 4
forces while minimizing its direct ownership involvement. Many of these features characterized the U.S. Reconstruction Finance Corporation’s (RFC) program of taking temporary preferred equity positions in banks after the great depression.<sup>14</sup> Another alternative that has gained popularity after its successful application in Spain in the early 1980s, is a centralized approach where banks’ nonperforming loans are carved off into an asset management company (AMC) which continues the restructuring process. The carving-out of an insolvent bank’s bad loan portfolio and its organizational restructuring under new management and ownership may be appropriate when large parts of the bank’s information capital is dysfunctional. The bad loan portfolio may be sold back into the market, or disposed of by a government-owned AMC.

A survey by Klingebiel (2000) shows, however, that the AMCs formed in developing countries have not been as successful in restructuring nonperforming loans. The effectiveness of AMCs has been quite mixed, better when the assets to be disposed are primarily real estate, less good when they are loans to large politically connected firms. In countries with developed private markets and institutions, individual banks –if well capitalized – would be in a superior position to engage in restructuring their own assets. But policymakers in weak institutions should not expect to achieve the same level of success in restructuring as in more developed economies, and may do well to design simple resolution mechanisms that offer little discretion to government officials (Honohan and Laeven, 2005).

> 14 It is difficult to evaluate the success of programs such as RFC, but in this case it is credited to have contributed to a recovery of confidence and output (until monetary tightening reversed both in 1937).  The government recovered its initial capital and did not bail out nonviable banks. See Mason (2000) for an analysis.

19
Reeve, J. (2009). Why teachers adopt a controlling motivating style toward students and how they can become more autonomy supportive. _Educational Psychologist_ , 44(3), 159-175.

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35
**Figure 3: Total labor content of exports and domestic production, South Africa 2001-2011**

<!-- Start of picture text --> 2000 2005 2010<br>Year<br>Total LVAD Total LVAX<br>200000<br>150000<br>US$ Mil 100000<br>50000<br>0<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **b. Jobs**

The slower growth in the labor value added of exports compared to domestic final output is mainly due to the fact that the export sector has been slowly shedding jobs. The LACEX data combined with South Africa’s Labor Force Survey data show that between 2001 and 2011, the total number of jobs supported by South Africa’s exports declined from 3.0 million to 2.9 million, despite positive growth in exports (10.3% annually) as well as the wages paid to produce exports (7.9% annually). This finding confirms previous literature that exports in South Africa have not been job-creating (Chinembiri 2010, Smet 2013).

The LACEX data allow us to consider not only the direct impact of exports on jobs but the indirect impact, by considering the backward linkages with the rest of the economy that exports support. In fact, the LACEX data combined with South Africa’s Labor Force Survey data highlight the importance of considering both the direct and indirect contributions for understanding how exports support jobs in South Africa: only two in five jobs generated by exports were generated directly in 2011. Failing to account for the indirect jobs presents only a partial view of how exports contribute to labor outcomes in South Africa and other countries throughout the world, which have become more important over time. The direct number of jobs supported by South Africa’s exports declined from 1.47 million to 1.15 million between 2001 and 2011, but the indirect number of jobs increased from 1.51 million to 1.71 million. However, the increase in indirect jobs was not enough to offset the decline in direct jobs. Because export growth has been more positive for labor in input-providing sectors, studies that only consider the jobs and wages to directly produce exports will underestimate how jobs growth has accompanied export growth.

These trends have also translated into a reduction of the total number of jobs contained in $1 million of gross exports (Figure 4 and Figure A1). South Africa moved from having 49 jobs per $1 million exports in 11
# **<u>Appendix S6: Clinic Attendees under Clinic and Vaccine conditions</u>**

|Condition:<br>|Clinic<br>(1)<br>|Vaccine<br>(2)<br>|Difference<br>(3)<br>|
|---|---|---|---|
|_Baseline Characteristics:_||||
|Age|25.157|25.537|-0.380 (0.416)|
|Muslim|0.475|0.444|0.031 (0.017)*|
|Any education|0.542|0.536|0.006 (0.023)|
|Marital status = Single|0.166|0.151|0.015 (0.025)|
|Currently pregnant|0.18|0.168|0.012 (0.022)|
|Have children|0.771|0.796|-0.025 (0.028)|
|Has paid work|0.443|0.467|-0.024 (0.026)|
|Monthly HH earning per capita (naira)|5918.662|5605.113|313.55 (378.61)|
|Distance to clinic (km)|1.58|1.586|-0.006 (0.017)|
|Transport to clinic (naira)|114.096|118.289|-4.193 (9.361)|
|Opportunity costs to clinic (naira)|3.728|4.192|-0.464 (0.728)|
|_Baseline behaviors:_||||
|Ever used clinic|0.693|0.721|-0.028 (0.024)|
|Received tetanus vaccine before|0.389|0.384|0.005 (0.028)|
|_Baseline perceptions:_||||
|Likely to contract tetanus|0.378|0.371|0.007 (0.020)|
|Number of people who die of tetanus|29.92|30.584|-0.664 (1.165)|
|Very worried about Tetanus|0.343|0.324|0.019 (0.021)|
|Tetanus is very bad|0.411|0.404|0.007 (0.025)|
|Very important to be protected from tetanus|0.484|0.453|0.031 (0.225)|
|Vaccine efficacy|22.789|22.603|0.186 (1.347)|
|Vaccines protect from disease|0.902|0.912|-0.010 (0.016)|
|Needles are scary|0.587|0.630|-0.043 (0.025)|
|Vaccines have side effects|0.656|0.671|-0.015 (0.025)|
|Vaccines give diseases|0.244|0.270|-0.026 (0.019)|
|Vaccines give HIV|0.163|0.183|-0.020 (0.018)|
|Heart rate (beat/min)|86.327|86.628|-0.301 (0.844)|

_Source:_ Authors estimate _Notes:_ These are based on the analysis sample of 1,268 women who visited the assigned clinic under Clinic condition (N=611) or under Vaccine condition (N=657). Transportion and opportunity costs to clinic are the costs for one-way. "Likely to contract tetanus" is a binary variable; "Number of people who die of tetanus" is the number of people out of 100; "Very worried about tetanus", "Tetanus is very bad", and "Very important to be protected from tetanus" are binary variables. "Vaccine efficacy" is the difference between the hypothetical number of unvaccinated people whom each respondent thinks get tetanus and the number of vaccinated people who get tetanus (range: -100 to 100). 150 naira = $1 approximately. Standard errors are in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%
|Blue Line (second extension),|13|13<br>—|
|---|---|---|
|Nov 11, 2006|||

Source: Authors’ analysis based on data provided by CPCB.

**Table 4. Effect of Delhi Metro on Air Quality: Nine Week Symmetric Window**

||NO2|CO|PM2.5<sup>1</sup>|
|---|---|---|---|
||(Percentage c|hange in level of|pollutant)|
|ITO||||
|Yellow Line First extension (Jul<br>03, 2005)|-6.6|-69.4***|—|
|Std. error|(16.5)|(10.5)|—|
|Number of observations|1457|1497|—|
|Blue Line introduction (Dec 31,<br>2005)|-30.6**|—|—|
|Std. Error|(9.4)|—|—|
|Number of observations|1639|—|—|
|Blue Line second extension (Nov<br>11, 2006)|-10.4|-13.1|-12.4|
|Std. error|(9.8)|(8.2)|(16.0)|
|Number of observations|1605|1605|1268|
|Siri Fort||||
|Blue Line second extension (Nov<br>11, 2006)|-25.9|-3.0|—|
|Std. error|(24.0)|(9.9)|—|
|Number of observations|1601|1532|—|

Source: Authors’ analysis based on data described in the text.

44
**_VI.4_ Changes in quarterly growth rates of European foreign claims to the average EMDE have mainly been driven by demand factors, although its magnitude has diminished since 2009. Growth was significantly restricted** **<u>due to supply conditions during peak financial stress conditions</u>**

Explaining Foreign Claims Growth of Euro Banks for the average EMDE: Supply vs. Demand _% (2005Q1-2012Q2)_ <!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5%<br>Supply driven Demand driven<br><!-- End of picture text --> Source: BIS; World Bank analysis.

**Acronyms** BIS Bank for International Settlements ECB European Central Bank EMDEs Emerging and developing economies EURIBOR Euro Interbank Offered Rate GDP Gross Domestic Product IIF Institute of International Finance IMF International Monetary Fund LTRO Long-term refinancing operation OIS Overnight indexed swap OMT Outright monetary transactions

# **References**

Aiyar, S. and S. Jain-Chandra, 2012. The Domestic Credit Supply Response to International Bank Deleveraging: Is Asia Different? IMF Working Paper 12/258.

Blundell-Wignall, A., and P. Atkinson, 2012. Deleveraging, Traditional versus Capital Markets Banking and the Urgent Need to Separate and Recapitalize GSIFI Banks. OECD Journal: Financial Market Trends 1.

Dailami, M. and J. Adams-Kane, 2012. What Does the Future Hold for the International Banking System? World Bank Economic Premise Note 94.

Deloitte, October 2012. Capital Gain, Asset Loss: European Bank Deleveraging.

European Central Bank (ECB), June 2012. Financial Stability Review.

25
**Table 1** : Regional comparisons

|Agricultural G<br>($US 2000 bill|DP<br>ion )|Agricultural<br>(million hect|area<br>ares)|Annual renewable<br>(billion cub|water resources<br>ic meters)|
|---|---|---|---|---|---|
|Turkey|88.81|Russian Federation|215.5|Turkey|227.0|
|Italy|79.32|Algeria|41.3|France|200.0|
|France|68.55|Ukraine|41.3|Italy|182.5|
|Spain|67.45|Turkey|39.2|Spain|111.2|
|Germany|63.57|Morocco|30.0|Georgia|58.13|
|Egypt|62.75|France|29.3|Greece|58.0|
|Russian Federation|55.32|Spain|27.9|Morocco|29.0|
|Netherlands|30.05|Yemen|23.5|Uzbekistan|16.3|
|Morocco|27.98|United Kingdom|17.6|Algeria|11.3|
|Poland|26.08|Germany|16.9|Tunisia|4.2|
|Romania|24.31|Poland|16.2|Saudi Arabia|2.4|
|Uzbekistan|21.33|Libya|15.6|Jordan|0.7|
|Algeria|20.83|Italy|14.2|Qatar|0.1|
|Source: FAO (2013). N<br>reported for 2011.|ote: Agricultu|ral GDP is average for 200|8-10; land area|is an average for 2007-0|9; water resources|

24
- 28 -

necessary and sufficient conditions for desirable regional redistributions discussed in Section 2 can also be used to characterize optimal regional targeting. It can be shown that a similar result holds for additively absorbed transfers for FGT poverty measures with a > 1.

# Simulations of Poverty Measures

Since (like most researchers) we do not have access to the unit record data from the NSS, simulation is required to estimate poverty measures from the published grouped data. Simulated distributions are also required for the policy simulations. For these purposes we have used Kakwar.is (1989) parameterization of the Lorenz curve: which is the cumulative proportion of total income or consumption held by the poorest p proportion of the population. The parameters a, 7 and 6 are positive, and e is a random error. The parameters 7 and 6 not exceeding unity is sufficient to ensure convexity of the Lorenz curve. The Lorenz parameters themselves are estimated by OLS for each state/sector from the following regression: ln[p-L(p)] - lna + 71np + 61n(l-p) + e All simulations are at EE - 0. Given the mean and Lorenz function, the distribution function is fully characterized noting that the slope of the generalized Lorenz curve, L'(p)p=x, is simply the inverse of the distribution function p=F(x). In earlier work on Indonesian data, the
**Table 2: Land market restrictions and wages** OLS Regression Results

|||Real|Annual wag|e||
|---|---|---|---|---|---|
||1|2|3|4|5|
|% Area Under LDO|-0.493|-0.366|-0.542|-0.435|-0.633|
||(3.07)***|(2.53)***|(3.37)***|(3.02)***|(4.18)***|
|Area LDO*Travel Time|0.135|0.094|0.122|0.095|0.093|
||(3.90)***|(2.90)***|(3.51)***|(2.92)***|(2.69)***|
|Marginal Effects (at median)||||||
|% Area Under LDO|**-0.24**|**-0.19**|**-0.31**|**-0.26**|**-0.46**|
|Individual controls|no|Yes|Yes|Yes|Yes|
|Education controls|no|Yes|no|Yes|Yes|
|Land quality Controls|no|no|Yes|Yes|Yes|
|Sectoral Composition + Area|Yes|Yes|Yes|Yes|Yes|
|District Fixed Effect|no|no|no|no|Yes|
|Observations|12363|12363|12363|12363|12363|

Robust t statistics in parentheses. Standard errors corrected for clustering * significant at 10%; ** significant at 5%; *** significant at 1%
WPS 3876

# **COMPETITION AND PERFORMANCE IN THE HUNGARIAN SECOND PILLAR**

## Gregorio Impavido and Roberto Rocha

The performance of the Hungarian second pillar since inception has been mixed. This is partly due to a less than satisfactory support for the 1997 pension reform, conservative fund portfolio distributions, the hybrid nature of the mandatory pension fund system, the segmented nature of the market in terms of costs, and a less than aggressive commitment on the part of the Hungarian Financial Supervisory Authority to a low-cost, transparent, and competitive equilibrium. In the accumulation phase, the authorities would need to further promote transparency and comparability of information on costs and investment performance, facilitate migration to lower cost funds, and more generally promote competition. The regulatory framework of the payout phase needs to be overhauled before the first cohort of workers retires.

## World Bank Policy Research Working Paper 3876, April 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_ † This paper was prepared by Gregorio Impavido (gimpavido@worldbank.org) and Roberto R. Rocha (rrocha@worldbank.org), both in the Financial Sector Operations and Policy Department of the World Bank. The paper benefited from inputs by David J. Goldstein (dgoldstein1@worldbank.org) in section VI dealing with capital markets. The usual caveat applies.
measure of housing adequacy. This unidimensional measure allows to assess the relative positions of emerging economies with regards to housing adequacy, provide an assessment of the general state of housing within a given economy, anticipate future developments and trends, and raise awareness regarding the overall housing situation within a given country.

Second, the soundness of the _AHI_ is dependent on the _a priori_ selection of its constituent variables and how they relate to the underlying concept of interest (in our case: housing adequacy) (e.g., Booysen 2002; Diener and Suh 1997; OECD 2008). In order to ensure cross-country comparability, the components of the index should be comprehensively capturing the phenomenon of interest, should be of universal significance, and should have cross-cultural applicability (Booysen 2002; OECD 2008). In our case, the selection of variables for the _AHI_ was guided by theory as well as data availability and reliability. We selected seven adequacy dimensions that have consistently been found to be positively correlated with socio-economic development by a large body of research, that are outlined in the Agenda 2030 and are, therefore, of great political and practical relevance, and that are comparable across countries. The selection of dimensions was constrained by data availability in most recent HH surveys across a large set of countries and hence excludes variables on e.g., environmental security or location of the housing unit (more on this issue below).

Third, index creation is often critiqued for arbitrary weighting of its components (e.g., Cox et al. 1992; Foster et al. 2013; Nardo et al. 2005). One frequently cited issue associated with equal weighting schemes is “double counting” of conditions that are highly correlated. If two collinear dimensions are included into composite index, with a weight of _w1_ and _w2_ , the unique dimension that the two indicators measure would have a weight of ( _w1_ + _w2_ ) (cf. OECD 2008). We test the pairwise correlation of the seven adequacy dimensions, which although related, show low correlation (cf. Annex 3). Further, to internally validate the robustness and sensitivity of our assigned weights, we employ alternate weighting schemes to ensure that changes in the assigned weights will not have a significant impact on the _AHI_ and the relative position of economies within the sample. In doing so, we test alternative weights obtained through principal component analysis (PCA), aggregating the seven adequacy dimensions into the _PCAAHI_ considering their relative contribution to the total variance (detailed description of PCA and results in Annex 4). Lastly, we externally validate the index by correlating it with items (validators) not included in the index such as infant mortality and national income (Section 4.2).

# **_3.5 Imputing Missing Data_**

Some economies’ HH surveys do not have variables available on some of the seven adequacy dimensions. To still allow for a comprehensive _AHI_ across all economies, we make use of