rafmacalaba/datause-encoder-data huggingface dataset:
Our approach yields different results from previous studies on surges and stops in FDI flows which do not differentiate between these events based on the mode of entry (e.g., Dell’Erba and Reinhart, 2012). We show that different factors are associated with the onset of GF-led and M&A-led FDI surges and stops. Global liquidity is the only common predictor of the two types of FDI surges, while a decline in global growth and a FDI surge in the preceding year are the only significant and consistent predictors of FDI stops. GF-led sudden stops and surges are more likely in lower income and resource-rich countries than elsewhere. Policies aimed at increasing financial openness are enablers of M&A-led surges, which are also more likely during periods of global growth and domestic economic and financial instability. The results are also policy relevant as we show that GF-led extreme events occur more frequently than M&A-led ones.<sup>7</sup> Thus, countries relying mostly on GF investments, the more stable type of FDI inflows, are not immune to sudden stops in capital flows and should prepare to withstand them. Knowledge of the factors behind different types of FDI surges and stops can help policy makers in developing countries craft policies to successfully weather such episodes.

The remainder of this paper is organized as follows. The next section defines FDI surges and stops and presents information on the types and frequency of such surges. In section 3, we turn attention to the empirical models for predicting GF-led and M&A-led surges and stops, the discussion of the econometric results, and the robustness checks. Section 4 summarizes the findings and offers concluding remarks.

# **2. Identifying GF-led and M&A-led FDI Surges and Stops**

Using UNCTAD data on gross FDI inflows from the World Investment Report (UNCTAD, 2011) and building on the work by Calvo et al. (2004), Reinhart and Reinhart

> 7 However, M&A-led surges are more likely to be short-lived and followed by a stop.

6
_extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.5   Investment Policies and the Broader Governance Environment**

|Variables|Independence of<br>central bank|Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Estimated risk of the portfolio|0.275**|0.268**|0.253*|0.265**|
|Allocation to nontraditional assets|0.162|0.114|0.177|0.239**|
|Number of eligible currencies|0.06|0.076|0.041|0.146|
|Number of eligible assets|-0.032|0.091|0.204*|0.217**|
|Investment horizon – Total tranched and untranched portfolio (months)|0.347**|0.348***|0.182|0.237*|
|Duration – Total portfolio (months)|0.219|0.346**|0.087|0.308**|
|Duration – Liquidity tranche (months)|0.349**|0.451***|0.225*|0.433***|
|Duration – Investment tranche (months)|0.250*|0.124|0.185|-0.008|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.6 Country Risk and Investment Policies**

|Variables|Country risk<br>score|
|---|---|
|Estimated risk of the portfolio|-0.408***|
|Allocation to nontraditional assets|-0.423***|
|Number of eligible currencies|-0.041|
|Number of eligible assets|-0.157|
|Investment horizon – Total tranched and untranched portfolio (months)|-0.404***|
|Duration – Total portfolio (months)|-0.384**|
|Duration – Liquidity tranche (months)|-0.521***|
|Duration – Investment tranche (months)|-0.296**|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.7  Independence of the Investment Committee and the Broader Governance Environment**

|Variables|Independence of<br>Central Bank|Corruption<br>perception index|Governance Pillar<br>Score|Government<br>effectiveness|
|---|---|---|---|---|
|Independent Investment Committee|-0.018|-0.186*|-0.180*|-0.204**|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

# **Table I.8  Independence of the Investment Committee to Approve Investment Policy and Portfolio**

21
et al., 2020; Hastings et al., 2015; Dinkelman and Mart ́ınez A, 2014). However, they tend to focus on results for native students. This paper instead uses a sharp RDD to analyze the impact of college financial aid on overall higher educational enrollment for migrant students. Using three cohorts of students, graduating from high school in 2017, 2018 and 2019, the results confirm previous findings from the literature, and demonstrate that access to financial aid makes students more likely to enroll in higher education.

The results in Table 9 provide evidence that financial aid has a positive and significant effect on the enrollment of all students, which aligns with the results from previous studies (Solis, 2017; Meneses et al., 2020; Hastings et al., 2015; Dinkelman and Mart ́ınez A, 2014). The results are robust to different bandwidths and functional form specifications. For migrants, the LATE is not statistically significant at the small– – est margins and it is only statistically significant at the 10 percent level when we combine both forced and voluntary migrants. Despite the lack of statistical significance, the direction of the CATE is positive, which constitutes suggestive evidence of the larger effects of financial aid on university enrollment. We believe the lack of significance is the result of a lack of statistical power. RD designs need a large number of observations around the margin to work and analyze the continuity assumption (Cunningham, 2021); despite having three graduating cohorts, the number of migrants around the cutoff might not be enough.

Figure 1 presents a visual representation of the results, with the financial aid cut-off centered at zero and the enrollment in higher education. The lines show the average probability of enrollment in higher education based on scores on the college selection test. Although financial aid seems to have a greater impact on migrant students than native-born students, the confidence intervals overlap.

We also analyze the CATE between the types of schools students can attend in Chile. We further divide our sample between students who attended a vocational or general education school. We find that native students attending a general education school who applied for financial aid were around 50 percentage points more likely to enroll than those who did not apply. The results are robust to different bandwidths and different functional forms. We also find similar results for migrant students who attended general education schools. Although at the very margin the coefficient is not statistically significant (probably due to a lack of statistical power), the CATE among specifications is 0.29, which indicates that migrant students who applied and qualify 20
capital) are perfectly competitive.

At the second stage, gross output, Yct; is produced competitively by aggregating the Nct di¤erentiated ...nal goods as follows: where �(> 1) is inversely related to the price elasticity of substitution across goods. Producers of di¤erentiated output must pay every period an overhead cost, oct; given by Free entry equalizes the per period operating pro...ts to the overhead costs determining the number of ...nal goods ...rms Nct.

# 2.5 Energy endowments

Oil represents a signi...cant share of Mexican exports to the US. To account for this in the calibration of the model, we assume that the government in country S is endowed with ESt<sup>e</sup> units of energy. Let Ect denote the aggregate consumption of energy in country c. Country N imports Et<sup>xunitsofenergytocountryS;andbuystherestofitsenergyneeds,E</sup> t<sup>w,fromthe</sup> rest of the world. The energy consumption in each country satis...es the following identities: For simplicity, we assume that the price of energy, P<sup>E</sup> , is ...xed.

# 2.6 Households

There is a representative household that consumes, supplies labor and saves. It may save by either accumulating capital or lending to innovators. The household also has equity claims in all monopolistically competitive ...rms. It makes one period loans to innovators and also rents capital that it has accumulated directly to ...rms. It is important to stress, though, that there is no international lending and borrowing. That is, US FDI in Mexico is the only item in the Mexican ...nancial account.

12
the differences in returns Δβ , (e.g., Reimers, 1983; Cotton, 1988; and Neumark, 1988).<sup>4</sup> The general expression allowing for these alternative weights is where I is the identity matrix and D is a matrix of weights. The traditional Oaxaca-Blinder decompositions can be considered to be special cases, in which D=0 yields (4) and D=1 yields (5). In addition to using D=0 and D=1, we have followed Reimers (1983) and used as weights the average of the coefficients and the average of the characteristics, that is, the diagonal of the D matrix = 0.5.

At this point, it is important to point out that the use and interpretation of the decomposition method discussed above involves a number of caveats. First of all, these decompositions are simple descriptive tools that provide a useful way of summarizing the role of endowments and returns in explaining existing welfare differentials. For this reason, we refrain from attributing causality to either endowments or returns for the welfare differences between or within regions.

Secondly, the variables that we use in the vector _X_ above are composed only of portable nongeographic household characteristics. Our specification intentionally excludes infrastructure and access to basic services. The influence of infrastructure, as well as other omitted variables, is captured by default by the estimated coefficients of the portable characteristics of the household. As the formula for omitted variable bias suggests, the estimated coefficients of the household characteristics can be considered as including the direct effect of the omitted variables (such as infrastructure, local institutions and other household variables possibly correlated with the location of the household) on welfare and their correlation with the included household characteristics.

Thirdly, the decomposition results may be biased because of the presence of selection bias. To the extent there is free internal migration within and between different regions, then the current place of residence may not be exogenous. The role of selection bias in the decomposition results was explored, as in Ravallion and Wodon (1999), and the decomposition results did not change significantly after correcting for selection bias (see Appendix A).

Lastly, the decomposition formula in equation (7) holds only at the mean of the two regions being compared, and the decompositions are performed at only one point in time. The extent to which the results of these decompositions change substantially over time is a question that is worth looking into. The potential variation at different points of distributions is examined through quantile decompositions.

> 4 For a comprehensive summary, see O’Donnell, et al. (2008) or Ben Jann (2008).  In our study, the decompositions employed are done using the Stata command “oaxaca” written by Ben Jann using the “weight ()” option.

7
unobserved ability and preference can explain away the observed occupational linkages in 1988. For the sake of completeness we also report the sensitivity results for 2002. Note that we do not use the Kelin and Vella (2009) approach to estimate the causal effects in the case of occupation as it requires a continuous out‐come variable. If we ignore the binary nature of the dependent variable and implement Klein and Vella (2009) approach, the conclusions regarding the evolution in intergenerational occupational linkages reported in this paper remain intact.

The results from the biprobit sensitivity analysis for alternative values of  are presented in Table 7. The first column reports the estimate from univariate probit that assumes that  0 which implies in the present context that the genetic transmissions of ability and preference from parents to children can be treated as insignificant. The second column reports the estimates for  0.05 implying that children with at least one parent in the non‐farm occupation would be 5 percentage points more likely to choose non‐farm occupation simply because of genetic transmissions or other similar unobserved factors. The estimated effect in 1988 declines from 0.12 to 0.09 for daughters and from 0.12 to 0.10 for sons, but it remains statistically significant in both cases at the 1 percent level. The effect of parental non‐farm participation on both sons and daughters remain statistically significant at the 5 percent level even when is increased to 0.10. The evidence thus indicates that the occupational linkages in column (7) of Table 6 in 1988 are robust to allowing for moderate positive correlations between the parents ’and children’s unobserved characteristics. Interestingly, the occupational persistence appears to be more robust in the case of daughters, it remains significant at the 10 percent level when the value of  is as high as 0.15, but the effect in the case of sons becomes insignificant at the 10 percent level.

35
- While there is ample evidence of growing intra-industry trade<sup>14</sup> in the Czech Republic, Slovenia and Hungary, Latvia’s performance is near the bottom of all NMS’s.  This suggests that Latvia’s industries and enterprises are not well integrated with industries and enterprises in other EU countries.  In other words, they are not part of EU value chains.  Compared to the early period of transition (and even more so with the pre-transition period), intra-industry trade between the more advanced NMS’s (the Czech and Slovak Republics, Hungary and Poland) and the EU has steadily increased whereas Latvia is clearly an outsider.  Most of the countries that have relatively low and stable intra-industry manufacturing trade have the largest share of non-manufactured goods in total exports.  In Latvia’s case, the low share of intra-industry trade suggests that a high proportion of country’s manufactured exports consist of relatively simple transformations of raw materials, processes that are not suited to a division of labor and activities across different countries.  The low share of intra-industry trade also suggests that Latvian enterprises do not have the skills required to become suppliers to foreign companies and find a higher value added niche in global value chains.

**Figure 8 Indicators of intra-industry trade with the EU (15)**

<!-- Start of picture text --> 0,8<br>0,7<br>Bulgaria<br>0,6<br>Czech Rep.<br>Estonia<br>0,5<br>Hungary<br>Latvia<br>0,4 Lithuania<br>Poland<br>0,3 Romania<br>Slovak Rep.<br>0,2 Slovenia<br>0,1<br>1993 1994 1995 1996 1997 1998 1999 2000 2001<br>Grubel-Lloyd Index<br><!-- End of picture text --> _Sources_ : Eurostat COMEXT Database, WIIW Industrial Database, own calculations.

> 14  The ‘new’ trade theory suggests that intra-industry trade, measured by the share of simultaneous export and import of similar products, is motivated by product differentiation and economies of scale. Intraindustry trade is measured by Grubel-Lloyd index (GL=1-�ABS(xij+mij), where xij and mij are country i's export and import of NACE 3 digit sector j, respectively).

17
At the individual level, researchers measure migration and define individuals as migrants in a number of ways, for example based on time spent away, destination location or links to those remaining behind (Smith and Thomas 2003). Correspondingly, there are even more ways to characterize migration at the household level and to define a “migrant household” (Carletto and de Brauw 2007). Since individuals can leave or return over time, the definition is further complicated when considering longer timeframes. For example, in recent decades there has been both short- and long-term international migration from the region, comprising short-term seasonal migration (e.g., for harvest season) to nearby Chiapas, Mexico and longer-term, though not necessarily permanent, migration to the United States.<sup>vi</sup> The latter migrants are typically undocumented and commonly spend several years away, with some returning for extended visits in between periods of migration (Saenz de Tejada 2009).

Researchers are often constrained in their characterizations of migration status by the limited information available in a general-purpose survey not explicitly designed with migration in mind and are therefore unable to represent well the various patterns. For this research, we piloted and then implemented comprehensive migration modules designed for the specific context. To identify both past and current migrants, we collected detailed migration histories for all current household members 15 years and older. We began with a standard household definition, including all the individuals who ate and slept in the same dwelling. We defined an individual as a household member if they had been present for at least one of the previous 12 months. Many household surveys such as the Guatemalan ENCOVI use a longer, more restrictive, three-month minimum residence period. The one-month period facilitated the inclusion of migrants who had made return visits in the past year, which is particularly common during the year-end holidays. The histories included the first and the most recent migration events for each of these current household members. This allowed us to incorporate individuals who were currently present but may have been migrants in the past, collecting information on
(Romer 1986, Lucas 1988) allowed policy reform to generate large gains by moving the economy to a higher growth path. Grossman and Helpman (1991) expanded the endogenous growth model by applying it to the open economy. Based on the idea that goods embody technological know-how, they showed that countries can acquire foreign knowledge through trade and increase their growth rate through trade liberalization.

Coe and Helpman (1995) provided an empirical implementation of that model. They constructed an index of ‘foreign R&D’, defined as the trade-weighted sum of trading partners’ R&D stocks, and found for OECD countries that both domestic and ‘foreign R&D’ have a large and significant impact on _TFP_ , and that the latter increases with the economy’s openness. Coe et al. (1997) examined the impact of North-South trade-related technology diffusion on _TFP_ in the South and obtained similar results. This led to other studies by, inter alia, Engelbrecht (1997), Falvey et al. (2002), and LumengoNeso et al. (2005), which have tended to confirm Coe and Helpman’s (1995) findings. Other studies have extended the approach to the industry level, including Schiff and Wang (2006) who added South-South trade-related technology diffusion to the analysis and found a positive impact on _TFP_ in the South, though a smaller one than that obtained from North-South trade.

# 1.2. Brain Drain

This paper focuses on the impact of the brain drain and whether it is different for small than for large states. Brain drain figures are presented in Table 1. The figures are based on Docquier and Marfouk (2006). The table presents skilled and overall emigration rates in 2000, as well as the ratio of the former to the latter (the schooling gap), for 46 3
Supplemental Table 6. The association between receptive vocabulary at baseline and expressive vocabulary at follow-up

||x1|x2|x3|x4|x5|x6|x7|x8|x9|
|---|---|---|---|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Receptive vocabulary in|0.3236*|||||0.2361*|0.2127*|0.1728*||
|English (_z-_score)|**|||||**|**|**|0.0347|
||0.0541|||||0.0517|0.0468|0.0473|0.0375|
||<0.01|||||<0.01|<0.01|0.0003|0.3547|
|Receptive vocabulary in||0.3545*|||0.2300*|0.2727*||0.1891*||
|Swahili (_z-_score)||**|||**|**||**|0.0108|
|||0.0528|||0.0505|0.0518||0.052|0.0371|
|||<0.01|||<0.001|<0.001||0.0003|0.7721|
|Receptive vocabulary in Luo (_z-_|||0.3961*||0.3058*||0.3191*|0.2592*||
|score)|||**||**||**|**|0.0217|
||||0.0571||0.0579||0.0542|0.0552|0.0386|
||||<0.01||<0.001||<0.001|<0.001|0.5748|
|Expressive vocabulary (_z-_||||0.7385*|||||0.7084*|
|score)||||**|||||**|
|||||0.0409|||||0.0564|
|||||<0.001|||||<0.001|
|Constant|–0.0176|–0.0092|–0.0098|–0.0019|–0.0098|–0.0156|–0.0154|–0.0144|–0.0032|
||0.0472|0.0457|0.0461|0.0314|0.0439|0.0441|0.0447|0.0431|0.0316|
||0.7094|0.8405|0.8327|0.9529|0.823|0.7242|0.731|0.7387|0.9205|
|_R_-squared|0.1115|0.1239|0.1603|0.5538|0.2042|0.1767|0.2024|0.2306|0.5558|
|N. of cases|442|442|442|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary _z-_ scores were measured using a tool developed from the PPVT. Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
Laos. In fact, the ten poorest provinces are all in the Northern Uplands. This is probably a reflection of their mountainous topography, distance from major markets, and limited

**Figure 1. Incidence of poverty by province**

<!-- Start of picture text --> &t.<br>Poverty headcount<br>W 0-20% -<br>20 - 40%<br>40 - 50%<br>50 - 60%<br>60 - 70%<br>70 -100%<br>0 100 200 300 400<br>Kilometers<br><!-- End of picture text --> Source: Estinmted fromurban-rural regression models of 1998 VLSS and household characteiistics in the 1999 Population and Housing Census 23