rafmacalaba/datause-encoder-data huggingface dataset:
- Gruber, J and Wise, D (1999) _Social security and retirement around the world_ , Chicago: University of Chicago Press.

- Gruber, J and Wise, D (2004) _Social security programs and retirement around the world: Microestimation_ . Chicago: University of Chicago Press.

- Gruber, J and Wise, D (2010) _Social security programs and retirement around the world: The relation to youth employment_ . Chicago: University of Chicago Press.

- Gustman AL, Steinmeier TL (1984) Partial retirement and the analysis of retirement behavior, Industrial and Labor Relations Review 37: 403-415.

- Gustman, AL, Steinmeier TL (2000) Retirement outcomes in the Health and Retirement Study, Social Security Bulletin 63: 57-71.

- Gustman AL, Steinmeier TL (2004) Social security, pensions and retirement behavior within the family. Journal of Applied Econometrics 19: 723-737.

- Gustman AL, Steinmeier TL (2009) How changes in social security affect retirement trends? Research on Aging 31: 261-290.

- Jalan J and Ravallion M (2002) Geographic poverty traps? A micro model of consumption growth in rural China, Journal of Applied Econometrics 17(4): 329-346.

- Kanbur, R Zhang, X (1999) Which regional inequality? The evolution of rural-urban and inlandcoastal inequality in China from 1983 to 1995, Journal of Comparative Economics, 27(4): 686-701.

- Kinsella, Kevin and Wan He (2009) _An Aging World: 2008_ , US Census Bureau, International Population Reports, PS95/09-1, Washington D.C.: US Government Printing Office.

- Kreuger, AB and Pischke, J (1992) The effect of social security on labor supply: A cohort analysis of the notch generation. Journal of Labor Economics 10: 412-437.

- Lazear EP (1979) Why is there mandatory retirement? The Journal of Political Economy 87: 1261-1284.

- Lee C (2009) Labor force participation of older males in Korea: 1955-2005, NBER Working Paper Series 14800, Cambridge, MA.

- Lee C and Lee J (2011) Employment status, quality of matching and retirement in Korea: Evidence from the Korean Longitudinal Study of Aging, Rand Working Paper Series WR-834.

- Lee R and Mason A (2011) _Population Aging and the Generational Economy: A Global Perspective_ , Abingdon, Oxon, UK: Edward Elgar Publishing.

27
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|---|---|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Hydrometerological System Modernization Project|Adriana Jordanova Damianova|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Nutrition and Social Protectionproject|LucyKatherine Bassett|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Central America HIV/AIDS Project|Fernando Montenegro Torres|
|Croatia Programmatic Adjustment Loan|Satu Kristiina Jyrintytar Kahkonen|
|Lima CityPovertyStrategyIDF Grant|David N. Sislen|
|Coastal Cities Environmental Sanitation Project|HungDuyLe|
|Coastal Cities Environmental Sanitation Project|HungDuyLe|
Table B4: Robustness check using the sample of the aged 60 and below—Job tenure

|||Log (tenur|e in years)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Social networks|-0.134<sup>_∗_</sup>|-0.120|-0.068|-0.135|
||(0.080)|(0.093)|(0.098)|(0.115)|
|Schooing in years||-0.004|-0.004|-0.009|
|||(0.008)|(0.009)|(0.010)|
|Age at hire||0.001|0.003|0.007|
|||(0.005)|(0.006)|(0.006)|
|Father’s pri.school||-0.061|-0.092|-0.115<sup>_∗_</sup>|
|||(0.064)|(0.068)|(0.064)|
|Mother’s pri.school||0.046|-0.007|0.036|
|||(0.053)|(0.056)|(0.070)|
|Math z-score||-0.011|0.033|0.036|
|||(0.045)|(0.058)|(0.062)|
|Language z-score||0.076|0.065|0.061|
|||(0.058)|(0.059)|(0.073)|
|Big 5 index z-score||0.053|0.060|0.114|
|||(0.060)|(0.082)|(0.085)|
|Firm fxed efect|||X||
|Firm-occ fxed efect||||X|
|R-squared|0.014|0.022|0.011|0.020|
|N|3763|3757|3757|3757|

_Note_ . The dependent variable log( _tenure_ ) is constructed by treating 0 year of tenure as 0.5 year, 1 year as 1.5 year, and so forth. The other controls included are establishment-entry occupation fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels:<sup>_∗∗∗_</sup> = 1%,<sup>_∗∗_</sup> = 5%,<sup>_∗_</sup> = 10%.

48
firm ownership at the country-and-sector-pair level. Accordingly, it addresses effects at the interface of ones on direct investment as well as global value chains (GVCs). Specifically, we analyze effects of entering deep PTAs in a unique data set on the frequency of shareholder-affiliate links across all pairs of 209 countries and 38 sectors over 9 years between 2007 and 2015.

Global input-output tables permit assigning to every shareholder sector and country whether it is up the stream or down the stream of an affiliate sector and country. Hence, every shareholder-affiliate link can be classified as horizontal (within the same sector) or vertical and then forward (the shareholder being up the stream of the affiliate) or backward (the shareholder being down the stream of the affiliate).

Theoretical work on the activity of multinational firms provides guidance as to the expected effect of PTA membership on foreign ownership (see Markusen, 2002; Egger et al., 2007): whereas lower preferential tariffs should reduce the propensity of horizontal ownership, they should increase the propensity of vertical ownership (in both the forward and backward directions). On average, positive effects of PTAs on foreign direct investment appear to dominate (Orefice and Rocha, 2014; Osnago et al., 2017; Kox and Rojas-Romagosa, 2020; Laget et al., 2020). This points to a relative dominance of vertical ownership links, consistent with the findings of Alfaro and Charlton (2009). However, the evidence is implicit only, because, as Kox and Rojas-Romagosa (2020) put it, ” _we cannot separate the FDI data between horizontal and vertical FDI_ ”.

In the light of the latter, the present study provides three innovations. First, it provides a new measurement by focusing on the extensive margin of investment in terms of shareholder-affiliate ownership links. Second, it differentiates those links as to be horizontal versus vertical (and then forward versus backward) in terms in the light of GVC data. Third, it identifies parameters and provides insights based on a very large panel data set covering all pairs among 209 countries and 38 sectors over a period of 9 years. The latter permits conditioning on a host of unobservable factors in a high-dimensional fixed-effects design. The latter ensures that the effects – of PTAs and their depth can be identified from the time variation in the data i.e., – the new membership in PTAs only.

The key insights from our study are the following. First, entering a PTA raises the number of new foreign ownership links. Second, the latter is completely driven by vertical links, i.e., ones in the forward or backward integration direction. The effects tend to be somewhat stronger in the forward than in the backward direction. The propensity of horizontal ownership links declines with the formation of PTAs. The effects of PTAs on vertical ownership links increase with a higher PTA depth. Finally, PTA effects on vertical investment are stronger, if the specificity of inputs 3
(Wood, 2008), with downstream implications for social cohesion and mobilization.

There are thus potentially different effects and mechanisms at play depending on the type of victimization, the timing of the victimization, and the context in which victims find themselves. Withthisinmind,weturnnowtoadiscussionofhowdisplacementconstitutesadistinct form of victimization.

# _3.2 Displacementasadistinctformofvictimization_

Inotherstrandsoftheconflictliterature–forinstance,onthe _causes_ ofviolence–itistodaywellestablished that displacement is a type of violence that should be studied separately (Ibáñez and Vélez, 2008; Steele, 2017). Yet when it comes to research on the effects of displacement on victimoutcomes,displacementtendstobelumpedinwithotherformsofvictimization(Fearon andShaver, 2021). Forexample, LupuandPeisakhin (2017)focusonthedeathsofrelativesamong thesurvivorsofalargeforceddisplacementcampaign,butdon’tconsiderhowthedisplacement itself might have affected victims. Similarly, Hazlett (2020) studies the determinants of attitudes towards peace among refugees but focuses on the effects of their exposure to violence and not on the experience of displacement itself.<sup>11</sup> The key way in which displacement differs from other forms of victimization is that, in displacement,victimsareforcedtorelocate,oftenfarfromtheirhomes. Thisforcedrelocationhas a number of distinct implications for victim outcomes. First, forced relocation means that displacedhouseholdsareoftenseparatedfromsubstantialproportionsoftheirwealth,intheform of lost property, assets, and the costs associated with moving and resettling (Ibáñez and Vélez, 2008). Resettlement also means moving to areas where there is often a mismatch between victims’ human capital and local labor market demand – seen most strikingly, for instance, with rural, agrarian households that are forced to relocate to urban environments (Calderón and Ibañez, 2009). The implication should be reduced economic welfare of displaced households even in the longer-term, as these effects are not one-off shocks but likely to persist over time.

Second,forcedrelocationalsomeansthatlong-standingnetworksofcooperation,reciprocity, andexchangewithincommunitiesareoftensevered(Dorff, 2017; TorresandCasey, 2017). While this should ultimately reduce social cohesion and the capacity for collective action among victimsofdisplacement,therearealsosomepotentialcounter-trendsinhowforceddisplacement affectssocial cohesion. Forone, refugeesoftenclusterin particularplaces: a functionofgeogra-

> 11Anexceptionisarecentstudyby Steeleetal. (2021),whosurveyvictimsinColombia–includingIDPs. However, they mainly focus on the political effect of IDPs registering as victims. They find that IDPs who register with the statearemorelikelytoengageinpoliticsthroughformalchannels(forexample,voting),inlinewith Voytas (2021)’s findings of Pinochet’s victims in Chile.

8
Moreover, we only observe a significant difference in 2011, the year when the policy took effect for the first group.

|**Table 7: Testing Com**|**mon Trend Assumpti**<br>(1)<br>IDEB Elem (SD)|**on - IDEB Elementary **<br>(2)<br>IDEB Elem (SD)|**Schools**<br>(3)<br>IDEB Elem (SD)|
|---|---|---|---|
|Single-Shift 2011*Post 2007|0.099||0.022|
||[0.421]||[0.379]|
|Single-Shift 2011*Post 2009|-0.591||-0.553|
||[0.425]||[0.387]|
|Single-Shift 2011*Post 2011|1.355||1.133|
||[0.233]***||[0.219]***|
|Single-Shift 2011*Post 2013|-0.031||-0.291|
||[0.292]||[0.269]|
|Single-Shift 2012/13*Post 2007||0.074<br>[0.073]||
|Single-Shift 2012/13*Post 2009||0.039<br>[0.103]||
|Single-Shift 2012/13*Post 2011||0.066<br>[0.090]||
|Single-Shift 2012/13*Post 2013||0.301<br>[0.084]***||
|Constant|-0.851|-0.894|-0.962|
||[0.023]***|[0.021]***|[0.051]***|
|Observations|2670|3096|462|
|Number of schools|445|516|77|
|R-squared|0.6|0.6|0.66|
|School FE|Yes|Yes|Yes|
|Year FE<br>* significant at 10%; ** significant at 5|Yes<br>%; *** significant at 1%.|Yes<br>Notes: IDEB Low Funda|Yes<br>mental Education 2005,|

* significant at 10%; ** significant at 5%; *** significant at 1%. Notes: IDEB Low Fundamental Education 2005, 2007, 2009, 2011, 2013 and 2015. Single-Shift schools include CES in this model. Standard errors clustered in school level.

Table 8 is similar to Table 7, but analyzes middle school outcomes. Column (1) shows that schools that became Single-Shift in 2011 had slightly higher results in 2007, compared to control schools. The difference is not statistically significant in 2009 and then we see the jump associated with the policy in 2011 and 2013. Differently from the elementary school comparison, we continue to observe a significant difference between the treatment and control in 2013 and 2015, 2 to 4 years after the policy was first implemented, suggesting that the effect did not fade out over time. Column (2) of Table 8 shows the differences between the schools that received the treatment in 2012 and 2013 and the control group (those that were not Single-Shift in any of the analyzed years). The coefficients do not indicate any significant difference between the treatment and control groups prior to the treatment implementation, only after. Finally, column (3) of Table 8 shows the difference between those schools that started to be Single-Shift in 2011 and those that 25
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1970-1980 birth cohorts, which increased to 0.442 for the 1981-1988 birth cohorts. The extended Becker-Tomes model suggests that the pattern of educational persistence across farm and nonfarm households should also change for the younger generation because of the changes in the economic mechanisms.

To check if the pattern of educational mobility across farm and nonfarm households has changed in the younger generation, we estimate the intergenerational educational mobility equation for 18-28 years age cohorts who were excluded from our main estimation sample.<sup>42</sup> The estimates are reported in Table 5, with the upper panel containing the results for years of schooling, and the lower for rank-based estimates. There is, in fact, evidence of emerging divergence between farm and nonfarm households in relative mobility as measured by IGRC and IRC. For example, the IGRC estimate for the farm households has declined a bit from 0.31 (main sample) to 0.27 (younger sample), while the IGRC estimate has increased for the nonfarm households from 0.316 (older) to 0.34 (younger). Similarly, the IRC estimate for farm households declined from 0.331 (main sample) to 0.304 (younger sample), and it has increased from 0.337 (main sample) to 0.373 (younger sample) for the nonfarm households. The difference between farm and nonfarm households is significant at the 10 percent level in the case of IRC estimates (P-value=0.097). While the difference in IGRC estimates is not significant at the 10 percent level, the p-value in the younger sample is much smaller: 0.107 (younger sample) vs. 0.90 (main sample), providing evidence in favor of emerging complementarity between father’s education and nonfarm occupation.

# **(8) Conclusions**

This paper develops a model of intergenerational educational persistence in a rural economy taking into account the role of parental farm vs. nonfarm occupations, and derives a theoretically-grounded estimating equation which we take to the data from rural India and rural China. We use two unique data sets that include the required information for all the children of the household head irrespective of their residency status at the time of the survey; thus eliminating the truncation bias common in the existing studies based on the standard surveys that use coresidency criteria to define household membership.

The empirical analysis delivers the following conclusions for the sons who went to school in the 1990s or earlier: (i) the intergenerational educational mobility in rural China was significantly higher compared to that in rural India, (ii) the farm/nonfarm occupations

> 42Our main estimation sample consists of 29-65 age cohorts in 2010 to ensure that the sample of children in China refers to the same age groups as in the data for India.

29
<!-- Start of picture text --> The state<br>iNTe)<br>Weak improvement<br>collective So enforcement<br>action 2<br>x ><br>Citizens/ clients Providers<br>Coalitions / inclusion Client power Management<br>Lack of<br>*“Low intensity” | *Weak voice for eee<br>civil society demands<br>*Fragmented<br>interests “se gewen<br>*Clientelism*Individualization persisten< 90% sew<br>of services Source: adapted from World Bank (2004).<br><!-- End of picture text -->
Table II. Exclusion and the Risk of Post-Settlement Conflict: Dyadic Level

||_Dyadic Peace_<br>_Duration_<br>|_Dyadic Peace Du_<br>|_ration (Signatories)_<br>|
|---|---|---|---|
||_Model 1_|_Model 2_|_Model 3_|
|Excluded|2.001***<br>(3.16)|||
|Exclusive-Peace||1.139||
|Agreement||||
|||(0.31)||
|Excluded-Fight|||1.045<br>(0.09)|
|Number of Parties|1.144|1.124|1.149|
||(1.34)|(0.59)|(0.66)|
|Dyad-Conflict Duration|0.996|1.013|1.014|
||(0.26)|(0.44)|(0.46)|
|Dyad-Conflict Intensity|1.300|1.027|1.048|
||(1.06)|(0.07)|(0.13)|
|Conflict Issue|1.724**|3.027**|2.992**|
||(2.14)|(2.01)|(2.03)|
|UN Peacekeeping|0.599|1.000|0.988|
||(1.55)|(0.00)|(0.03)|
|Non-UN Peacekeeping|2.050***|2.537***|2.533***|
||(2.84)|(2.77)|(2.79)|
|Power Sharing||0.566|0.567|
|||(1.33)|(1.34)|
|Observations|477|376|376|
|No. of Failures|62|33|33|
|Log Likelihood|-220.280|-104.411|-104.436|

Note: A Cox proportional hazards model is employed. Hazard ratios rather than coefficients are reported, with robust z statistics (given in parentheses) clustered on dyad. *Statistically significant at the .10 level. **Statistically significant at the .05 level. ***Statistically significant at the .01 level. Two-tailed tests are used. Stata 10.0 was used to generate the statistical results.

24
**25**

Interestingly, the constant term is not significantly different from zero in most of the empirical specifications reported in Table 6. With zero growth, poverty is predicted on average to remain unchanged, implying constancy on average in income/consumption inequality.<sup>24</sup> But for the SPG measure, the constant term is significantly negative, suggesting favorable changes at the bottom of the distribution. There inequality changes appear to exert a downward pressure on poverty when there is no growth.

To explore further whether the composition of growth matters for poverty reduction across the development spectrum, we split the sample in two groups of equal size based on the country’s GDP per capita and run separate regressions for the low and middle-income countries.<sup>25</sup> For the middle-income countries, the null hypothesis is rejected (see panel C of Table 6). Agricultural growth has a significantly greater impact on poverty ( _πa_ being greater than _πn_ by a factor of 13.8 for the H and 10.2 for the PG measure). But as with the whole sample, the evidence of the differential impact of sectoral growth on the SPG measure is less certain. While both the coefficients on agricultural and non-agricultural growth are statistically significant (at the 10% level), they are not statistically different, despite the fact that the coefficient on agricultural growth is 7.6 times larger than the one on non-agricultural growth.

> 24 We also applied a Gini correction to the GDP growth variables (following Ravallion, 1997) using initial year Ginis of each spell.  We obtained qualitatively very similar results to those reported in Table 6. 25  Taking the pooled sample, this resulted in a cut-off of  US$ 1,160 GDP/capita.