rafmacalaba/datause-encoder-data huggingface dataset:
12 Latin American countries based on data from household surveys are used to estimate both trends in RTS as well as deviations from trends. Deviations are equated with the cyclical components of RTS.

In a second stage, the resulting panel data of cyclical deviations from RTS trends is used to analyze the relationship between the transitory component of the returns to schooling and foreign demand, controlling for other determinants of the business cycle. More specifically, the transitory component of returns to schooling is allowed to vary with domestic and external shocks, real and financial shocks and labor market rigidities. Each variable in this stage is decomposed into two parts: a trend and a cyclical component. Only the cyclical component is used in the second stage.

Main findings can be summarized as follows. First, the cyclical components of returns to schooling are significant in 8 out of the 12 countries, whereas long-term trends are significant in less than a handful. Second, the most robust determinant of cyclical fluctuations in returns to schooling is fluctuations in foreign demand for Latin American and Caribbean (LAC) exports. These tend to be positively correlated to returns to schooling. Thus, increases in export volatility might increase the risk of investments in human capital. Third, the effect of export fluctuations (driven by changes in foreign demand) seems to be attenuated by labor-market rigidities, such as constraints on employers to hire temporary workers on an hourly basis.

In regards to the last finding, it is tempting to conclude from a policy perspective that such rigidities can stabilize returns to schooling and thus income inequality (between skilled and unskilled workers). However, this effect needs to be weighed against long-term costs for the economy as a whole, accounting for the fact that rigidities are associated with quantitative impacts on labor markets. Otherwise stated, rigidities should be evaluated by taking into account the potential reduction in hiring as well as the loss of jobs during downturns, rather than as a means to smooth out fluctuations in skilldriven inequality.

The rest of this paper is organized as follows. Section 2 provides a brief literature review on the cyclical properties of returns to schooling, and highlights the literature’s lack of coverage of research in this area for developing countries. Section 3 discusses methodological issues concerning estimates of RTS (including transitory cyclical components) and section 4 presents stylized facts from repeated cross sections of employment surveys from Latin American countries. Section 5 studies the determinants of cyclical fluctuations in the returns to schooling, and section 6 concludes.

3
# **3.1 The Household Survey Data**

For the empirical analysis, we use panel data from the new Vietnam Household Living Standard Surveys (VHLSS). The first round of the VHLSS was carried out in 2001-2002, before the imposition of U.S. tariffs on catfish in 2003. The second round was carried out in August 2004, after the introduction of the trade barriers. The availability of ex-ante and ex-post panel data makes the AD on Vietnamese catfish an ideal case study.

The VHLSSs were conducted by The General Statistics Office of Vietnam (GSO) with technical assistance from UN Statistics Division, the World Bank and Statistics Sweden. In both surveys, GSO used a stratified two-stage sampling design. The primary sampling units were enumeration areas in urban areas, and supervisor areas in rural areas, identified in the 1999 Population and Housing Census. The surveys are representative at the national level. VHLSS’02 surveyed more than 74,000 households while VHLSS’04 surveyed over 44,000. A fraction of this sample forms a panel, with a total of 16,518 households surveyed in both years. The size of the panel is smaller than the initially planned figure of 20,000, both because of attrition and because errors in inputting household identifiers makes it impossible to match some panel households between the two rounds of the survey. Unfortunately, it is not possible to establish which or how many of the remaining 3,482 households are lost from the panel because of attrition or because of the miscoding.

The VHLSSs comprise several modules with information on demographics, education, employment, health, income and labor supply. There is also an expenditure module, which was however used only for a subsample of the interviewed households, 29,000 in VHLSS’02 and 9,000 in VHLSS’04. In practice, the expenditure module is not usable for our purposes because there are only a few dozen observations in the panel sample of aquaculture households in our focus Mekong provinces. Extensive modules record information on farm activities related to agriculture, livestock and aquaculture. Data include production, sales, input use and investment. The information on aquaculture activities distinguishes between raising and catching fish, shrimp, or all other aquaculture products (like mollusks). It must be emphasized that the data do not explicitly separate catfish from more general fish production. Hence, although in the rest of the paper we will refer to ‘catfish income’ and to ‘catfish 12
# **Table 3. Panel: Probit Analysis**

|**Dependent variable: Pr(Do**|**nor ****_j _provides a**<br>|**id after disaster **<br>|**_i_ =1)**<br>|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||<br>Pooled Probit|<br>Pooled Probit<br>Donor FE|<br>RE-Probit<br>Donor FE|<br>Conditional<br>Logit<br>|<br>Conditional<br>Logit<br>|
|||||Donor FE|Emergency FE|
|_Impact measures_||||||
|Log(Nr.affected)|0.099***<br>(0.014)|0.116***<br>(0.015)|0.116***<br>(0.013)|0.215***<br>(0.024)||
|Log(Nr. Killed)|0.199***|0.233***|0.233***|0.417***||
||(0.015)|(0.016)|(0.016)|(0.028)||
|_Socio economic indicators:_||||||
|Log(Population)|-0.204***|-0.221***|-0.221***|-0.423***||
||(0.026)|(0.030)|(0.029)|(0.052)||
|Pop. density|0.000|-0.000|-0.000|0.000||
||(0.000)|(0.000)|(0.000)|(0.001)||
|Log(GDP per capita)|<br>-0.146***|<br>-0.175***|<br>-0.175***|<br>-0.291***||
||(0.043)|(0.045)|(0.046)|(0.081)||
|Oil dummy|0.324***|0.391***|0.391***|0.661***||
||(0.068)|(0.073)|(0.073)|(0.129)||
|_Policy performance indicators_||||||
|Trade openness|-0.001|-0.001|-0.001|-0.003||
||(0.001)|(0.002)|(0.002)|(0.003)||
|ELF index|-0.044|-0.007|-0.007|0.003||
||(0.112)|(0.120)|(0.126)|(0.217)||
|Gastil index|-0.000|-0.000|-0.000|-0.001||
||(0.010)|(0.011)|(0.011)|(0.019)||
|_Bilateral relation indicators_||||||
|Geograph. Distance|-0.018*|-0.036***|-0.036***|-0.056***|-0.086***|
||(0.009)|(0.009)|(0.009)|(0.015)|(0.017)|
|Affinity index|-1.414***|-1.299***|-1.299***|-2.377***|-2.749***|
||(0.101)|(0.259)|(0.263)|(0.469)|(0.820)|
|Former colony|0.519***|0.632***|0.632***|1.067***|1.354***|
||(0.096)|(0.112)|(0.112)|(0.192)|(0.216)|
|Donor FE|NO|YES|YES|YES|YES|
|Disaster FE|NO|NO|NO|NO|YES|
|Observations|5153|5153|5153|5153|4754|
|Pseudo R-squared|0.16|0.28||0.18|0.31|
|Wald/LR Statistic|Wald chi2(38)|Wald chi2(57)|Wald chi2(57)|LR chi2(38)|<br>LR chi2(22)|
||739.2|1157.2|1123.3|903.8|1117.9|

_Notes:_ Coefficients reported; robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% All specifications include year, regional and disaster type fixed effects. The donors included are.Austria, Australia, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK and the US 26
POLICY RESEARCH WORKING PAPER 1572

# Summary findings

_Averting the Old Age Crisis: Policies to Protect the Old and Promote Growth,_ the publication for which this technical annex provides supporting documentation, is the third in a series of major World Bank Policy Research Reports. Unlike its predecessors, _The East Asian Miracle_ and _Adjustment in Africa,_ it does not concentrate on a specific region but focuses rather on the general topic of income security for old age.

More than two years of research were required to gather data, review the theoretical literature, examine empirical evidence, and write the book that represents the Bank's most important study of the issue to date. This annex explains in detail the data sources, concepts, and definitions used in the book and provides additional information.

It describes the demographic data used in the report and discusses data about public and privately managed pension schemes around the world (giving specific sources for individual countries). An attempt has been made to cross-reference the data available on *STARS* diskettes, which can be downloaded and analyzed in most database or statistical software packages.

- - This paper a product of the Poverty and Human Resources Division, Policy Research Department provides supporting documentation for the World Bank _publicationAvertingthe Old-Age Crisis: Policies to Protect the OldandPromote Growth_ (1994), available from the World Bank bookstore. Copies of this paper and the *STARS* data diskette are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Montserrat Pallares, room Hi 1-094, telephone 202-473-0435, fax 202-477-1692, Internet address mpallares@worldbank.org. February 1996. (142 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange_<sup>_of ideas about_</sup> _development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished,_<sup>_The_</sup> _papers carry the names of the authors and should be used and cited accordingly. The findings, interpretations, and conclusions_<sup>_are the_</sup> _authors'_ **_own_** _and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member_<sup>_countries._</sup> Produced by the Policy Research Dissemination Center
Mode 2amallwino the Ga. **Levl** (for Esalishi 7 Intake Variable to be Random at tho School Whereas Model 2 assumes that tho relationship between Grade 7 achievemont and Form I achiovement is the **sme** in all schools, Model 2a allows this relationship to vary, so that some schools may produce better results than others, gLven partlcular levels of student achieveont on lntake. This model proved to be significant only in the case of English, and not Mathematics, for which a constant slope - or constant relationship - **was evident.** For English, however, taken at the average Grade 7 score of 6.4085, this model resulted in a reduction in the between school variance of 5S, to 28% of **the overall** variance. The total variance itself was reduced by 10%, when compared with Model 2 /

# _Modl_ **3_t** schoo_nu Differentiation

Dummy variables were created for the dlfferent schooltypes under investigation in the study, the Group A schools s*rving as **the reference** point. Tho inclusion of those 5 schooltype variables elirilnated the between school variance (it **was** no longek statistically significant at **the** 5% confidence level) and wubstantiall; reduced the total variance. For English, VA further model **was tested** which allowed the Grade 7 **score** to be random at **the** class _level_ **a** well. This means that different classes have dlfferent relatLonships between Lntake and outcome. Taken at the average GR73, the total variance *xplained was less than for Model 2a, but variance remained to be explained at all thres levels after the inclusion of th@es random paramoters. **This** model produced the following statistics fixed coefficLents: CONS - .7024 (.12) GR7U .6736 (.04)1 random **parameters:** school **levels** CONS .05875 (.09) GR7I .0244S (.01) covarianco -.03657 (.028); **class level:** **_CONS_** .408 (.17) GR7U .03067 (.007) covariance -.1104 (.04)i pupil levels 1.349 (.03) 22
<!-- Start of picture text --> Annex I: Designation of PPI projects in Korea<br>Application to Preliminary<br>Feasibility Studies<br>Designation of Projects<br>For preliminary Feasibility<br>studies<br>Begin of Preliminary<br>Feasibility Study<br>Government financed Projects possible for<br>projects private Investment<br>Feasibility Study<br>Application for PPI Project<br>Designation<br>Designation and Public<br>Notification of PPI project<br><!-- End of picture text --> 31
**Table 11. FSAP Focus and Breadth-Regression Analysis**

|**Income**|**Total**<br>-4.89<br>[1.08]***|**Total**<br>23.68<br>[10.9]**|**Total**<br>7.38<br>[15.58]|**Standard**<br>**Deviation**<br>150.76<br>[20.54]***|**Standard**<br>**Deviation**<br>-451.21<br>[205.25]**|**Standard**<br>**Deviation**<br>-392.00<br>[292.91]|
|---|---|---|---|---|---|---|
|**Income Squared**||-1.76|-0.66||37.18|31.66|
|||[0.67]**|[0.99]||[12.61]***|[18.59]*|
|**Population**|0.66|1.45|1.64|-14.88|-31.48|-35.93|
||[1.09]|[1.10]|[1.19]|[20.74]|[20.82]|[22.40]|
|**Density**|0.61|1.14|1.46|-22.87|-34.17|-47.28|
||[1.12]|[1.11]|[1.20]|[21.28]|[20.92]|[22.55]**|
|**Date**|0.48<br>[0.85]|1.00<br>[0.85]|0.95<br>[0.86]|-17.62<br>[16.13]|-28.43<br>[16.02]*|-35.29<br>[16.14]**|
|**Update Dummy**|-1.38|-4.49|-6.21|90.26|155.99|174.39|
||[4.75]|[4.77]|[4.90]|[90.20]|[89.99]*|[92.18]*|
|**LCR**|||18.68|||-177.64|
||||[8.95]**|||[168.34]|
|**ECA**|||9.17|||-87.41|
||||[8.78]|||[165.10]|
|**AFR**|||9.20<br>[9.59]|||-225.37<br>[180.34]|
|**MENA**|||7.41|||-349.70|
||||[8.73]|||[164.22]**|
|**EAP**|||0.19|||-64.74|
||||[10.44]|||[196.31]|
|**SAR**|||8.96<br>[12.19]|||-68.62<br>[229.21]|
|**Observations**<br>|115|115|115|115|115|115|
|**Adjusted R-**<br>**squared**|0.13|0.18|0.2|0.32|0.36|0.38|
|Standard errors in b<br>* significant at 10%;<br>significant at 1%|rackets<br>** significant a|t 5%; ***|||||

23
growing debt and economic imbalances, which has contributed to the state’s inability to respond to growing demands and dissatisfaction from citizens (Blas 2023).

**Figure 1. Political stability and economic development in Sub-Saharan Africa (2021)**

<!-- Start of picture text --> 4.6<br>Seychelles<br>South AfricaEquatorial Guinea Mauritius 4.4<br>Gabon Namibia Botswana 4.2<br>Eswatini Cabo Verde 4<br>Nigeria Cote d'IvoireAngola<br>Sudan Cameroon Kenya Mauritania Ghana 3.8<br>Zambia<br>Mali Ethiopia Burkina Faso Chad ZimbabweGuineaUgandaCongo, Rep.MadagascarTogo TanzaniaBeninComorosSenegalLesotho Rwanda Sao Tome and Principe 3.63.4<br>Somalia South Sudan Niger Eritrea Guinea-BissauSierra LeoneMalawiGambia, The 3.2<br>Congo, Dem. Rep. Burundi Mozambique Liberia 3<br>Central African Republic<br>2.8<br>-3.00 -2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00<br>Political Stability<br>Note: The x-axis represents the World Governance Indicator on political stability. Oil rich economies are represented in yellow; non-oil but<br>resource-rich economies in green; non-resource rich countries in blue. The data is the latest available (2021). Source: WGI database for political<br>stability indicator and WDI database for database for GDP per capita.<br>Figure 2. Rule of law and economic development in Sub-Saharan Africa (2021)<br>4.6<br>Seychelles<br>Mauritius 4.4<br>Equatorial Guinea Gabon South Africa Botswana 4.2<br>Eswatini Namibia 4<br>Angola Cote d'Ivoire Ghana Cabo Verde 3.8<br>Nigeria Mauritania Kenya<br>ComorosGuinea-BissauSudan ZimbabweCameroonCongo, Rep.Guinea MaliEthiopiaZambiaTogoSao Tome and PrincipeBeninBurkina FasoTanzaniaLesothoGambia, TheSenegalUga... Rwanda 3.63.4<br>Somalia South Sudan Congo, Dem. Rep.Eritrea Chad LiberiaMozambiqueMadagascarSierra Leone Niger Malawi 3.2<br>Central African Republic 3<br>Burundi<br>2.8<br>-2.40 -1.90 -1.40 -0.90 Rule of law -0.40 0.10 0.60<br>log GDP pc PPP<br>log GDP pc PPP<br><!-- End of picture text --> <mark>Note: The x-axis represents the World Governance Indicator on rule of law. Oil rich economies are represented in yellow; non-oil but resourcerich economies in green; non-resource rich countries in blue. The data is the latest available (2021). Source: WGI database for rule of law indicator and WDI database for database for GDP per capita.</mark> 3
||||countries, 1985-<br>2006||markets reduced<br>transmission and<br>distribution losses|
|---|---|---|---|---|---|
|Nepal and<br>Jamasb<br>(2012a)|econometric|Transition<br>economies|panel data: 27<br>countries, 1990-<br>2010|bias<br>corrected<br>fixed<br>effects|power sector reform<br>on its own did not<br>produce any<br>significant impacts on<br>T&D losses|
|Nepal and<br>Jamasb<br>(2012b)|case study|Nepal|power sector<br>indicators|single<br>country|electricity losses in<br>South-Asia including<br>Nepal still remain<br>high, capacity and<br>power shortages<br>prevail|
|ESMAP<br>(2009)|case study|Sub-<br>Saharan<br>Africa|power sector<br>indicators|multi<br>country|high number of<br>outages per year and<br>long delays with<br>electrical connections|
|Pollitt (2008)|case study|Argentina|power sector<br>indicators|single<br>country|reforms successful in<br>improving quality<br>prior to the collapse<br>of Argentine peso|
|Millan<br>(2005)|case study|Latin<br>America|power sector<br>indicators|multi<br>country|generation capacity<br>expanded vigorously<br>except in Brazil after<br>reforms|
|Smith (2004)|case<br>study/econo<br>metric|global|102 countries:<br>electricity<br>losses,<br>governance<br>indicators for<br>1980 and 2000|correlation/<br>multi<br>country|losses have increased<br>in many developing<br>countries after<br>reforms|
|Hall (1999)|case study|global|power sector<br>indicators|multi-<br>country|reforms have<br>coincided with rising<br>power cuts and<br>blackouts|
|Erdogdu<br>(2013)|econometric|global|panel data: 27<br>countries, 1974-<br>2008|fixed<br>effects;<br>random<br>effects|reform progress led to<br>decline in R&D<br>investments|
|Prasad<br>(2008)|case study|Africa|energy sector<br>indicators|multi-<br>country|energy reforms only<br>impacts access when<br>adjusted to local<br>conditions of the poor|

23
**Table 33. Reactions to political risks: Expropriation**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|-0.174**|-0.138*|||-0.225**|-0.194*|||
||(0.0790)|(0.0795)|||(0.101)|(0.110)|||
|Export % of sales|-0.000341|0.00103|-0.000443|0.000976|-0.000135|0.00140|-0.000455|0.00130|
||(0.00110)|(0.00111)|(0.00110)|(0.00111)|(0.00144)|(0.00157)|(0.00147)|(0.00162)|
|Share of sourcing via imports|0.000128|0.00147|0.000649|0.00197*|0.000159|0.00222|0.000777|0.00284|
||(0.00118)|(0.00119)|(0.00118)|(0.00119)|(0.00157)|(0.00167)|(0.00162)|(0.00175)|
|High-income source country|-0.00884|0.0644|0.0534|0.114|-0.00789|0.0840|0.0737|0.155|
||(0.109)|(0.109)|(0.108)|(0.106)|(0.136)|(0.144)|(0.140)|(0.145)|
|>250 employees|-0.0410|-0.0864|-0.0270|-0.0972|-0.0803|-0.134|-0.0598|-0.149|
||(0.0968)|(0.0948)|(0.0970)|(0.0935)|(0.124)|(0.129)|(0.129)|(0.132)|
|Cumulative inv. >US$10M|0.129|0.104|0.159*|0.118|0.171|0.170|0.218*|0.201|
||(0.0962)|(0.0945)|(0.0964)|(0.0934)|(0.127)|(0.132)|(0.132)|(0.135)|
|Years in country|-0.00505*|-0.00475*|-0.00527*|-0.00473*|-0.00607*|-0.00596*|-0.00634*|-0.00589*|
||(0.00274)|(0.00258)|(0.00277)|(0.00262)|(0.00325)|(0.00327)|(0.00338)|(0.00337)|
|Level of foreign ownership (%)|0.00257*|0.00285*|0.00267*|0.00303**|0.00341**|0.00424**|0.00357**|0.00459**|
||(0.00144)|(0.00158)|(0.00139)|(0.00154)|(0.00167)|(0.00203)|(0.00169)|(0.00207)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.010|0.109|0.042|0.135|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "How would the following situations affect your investments in this? Please tell us whether they would cause you to significantly delay an investment, to cancel a planned investment, to withdraw an existing investment, or to consider a delay or cancellation. [Expropriation or taking of your property or assets by the government]". Higher numbers denote more adverse reactions. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

50
of imported materials. We ...rst empirically establish these channels without imposing the translog cost structure and let the data show the relationship between these variables. In the next section, we will formally estimate the translog parameters to assess how well our highly stylized model may explain ...rm DVAR.

We ...rst isolate the part of the within-...rm changes in the DVAR that is common across all ...rms within an industry, given that PPjtj<sup>DI</sup> t<sup>is industry-speci...c.To this end,we estimate the</sup> average within-...rm change in the DVAR by industry according to (11) and allow year ...xed e¤ects to be industry-speci...c: The estimated �jt, �<sup>^</sup> jt, captures the average within-...rm change in DVAR of each industry j in each year relative to 2000.

We then estimate the following system of three equations using 3SLS: where !<sup>1</sup> j<sup>, !2</sup> j<sup>, and !3</sup> j<sup>stand for industry ...xed e¤ects in three di¤erent equations, and �1</sup> jt<sup>, �2</sup> jt<sup>,</sup> and �<sup>3</sup> jt<sup>arethecorrespondingerrorterms.</sup> The ...rst equation uses the change in the price of imported materials relative to domestic materials, 4 ln PPj I<sup>D</sup> t ; to explain the within-...rm change in the DVAR that is common across � jt � all ...rms within an industry. The second equation explains how 4 ln PPj I<sup>D</sup> t can be caused by � jt � the change in the exchange rate, 4 ln Ejt, de...ned as the increase in the foreign price of the 31
26

- Rothschild, M. and J. Stiglitz, 1970, "Increasing risk I: A definition," Journal of Economic Theory, Vol. 2, p. 225-243.

- Serven, L., 1991, "Anticipated real exchange rate changes and the dynamics of investment," World Bank paper series No. 562.

- Serven, L. and A. Solimano, 1991, "Adjustment policies and investment performance in developing countries," mimeo.

- Shapiro, S.S., Wilk, M.B. and H.J. Chen, *968, "A comparative study of various tests for normality", Journal of the American Statistical Association 63, 1343-1372.

- Thomas, V., J. Nash, K. Matin, et. al., 1990, "Best practices in trade reform," mimeo.
**Table A2.IR.B: Intergenerational Persistence in Schooling in Rural India -** **<u>(18 30 Years Age Cohorts)</u>**

|||**LINEAR CEF**<br>||**QU**|**ADRATIC C**<br>|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.42<br>(0.0071)***|0.51<br>(0.0079)***|0.083<br>(0.011)***|0.49<br>(0.021)***|0.63<br>(0.023)***|0.14<br>(0.031)***|
||[0.011]***|[0.013]***|[0.012]***|[0.028]***|[0.032]***|[0.034]***|
||{0.017}***|{0.027}***|{0.023}***|{0.033}***|{0.054}***|{0.0020}***|
|Quadratic Coefft.||||-0.0061|-0.011|-0.0051|
|||||(0.0016)***|(0.0019)***|(0.0025)**|
|||||[0.0019]***|[0.0023]***|[0.0025]**|
|||||{0.0020}***|{0.0033}***|{0.0025}*|
|Intercept|6.69|4.76|-1.93|6.63|4.67|-1.96|
||(0.050)***|(0.051)***|(0.072)***|(0.055)***|(0.054)***|(0.077)***|
||[0.12]***|[0.15]***|[0.11]***|[0.13]***|[0.16]***|[0.11]***|
||{0.26}***|{0.42}***|{0.23}***|{0.27}***|{0.41}***|{0.23}***|
|No. Observations|12946|12959|25903|12946|12959|25903|
|||**Estima**|**tes of Mobility**|**from Quadrat**|**ic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.63|4.67||**_IGME0 _**|0.49|0.63|
|**_ES5 _**|8.93|7.55||**_IGME5 _**|0.429|0.518|
|**_ES10_**|10.92|9.87||**_IGME10_**|0.369|0.407|
|**_ES16_**|12.91|11.93||**_IGME16_**|0.296|0.272|

Notes: (1) The data used are IHDS 2012 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at statelevel in braces; *** p<0.01, ** p<0.05, * p<0.1.