rafmacalaba/datause-encoder-data huggingface dataset:
on less elastic products (such as Transport Equipment, Plastics, Foods and Apparels), while lower tari¤s are on the more elastic products (such as Pulp and Paper, Scienti...c Instruments, Precious Stones). This negative relationship between tari¤ and trade elasticity causes the EU’s OTRI to be lower than its import-weighted average tari¤, which leads to a smaller trade impact in the short run.

# 5 Concluding Remarks

This note constructed the overall trade restrictiveness facing the UK’s goods exports in its major markets. Based on new estimates on trade elasticity and the ad valorem equivalent of NTMs, we show that the overall short-run trade impact on the UK’s goods trade due to Brexit is less than 2 percent. This result assumes that the UK will not have any preferential access to the EU market and all its products will enter the EU, the US and China on the Most Favored Nations (MFN) basis. This result also assumes that in the short-run, ...rms and investments are not free to move across borders. The long-run consequences of Brexit most likely depend on whether the UK can continue to attract and retain its foreign direct investment.

# References

- [1] Anderson, J. and Neary, P. (1992). ‘Trade reforms with quotas, partial rent retention and tari¤s’, Econometrica, vol. 60(1), pp. 57-76.

- [2] Anderson, J. and Neary, P. (1994). ‘Measuring the restrictiveness of trade policy’, World Bank Economic Review, vol. 8(2) (May), pp. 151-169.

7
of being employed. However, women exposed to conict are signicantly more likely be employed in agricultural sector. As a measure of a household's cumulative living standard, the DHS reports a wealth index using ownership of easy to collect assets, materials used in housing construction, and access to type of water and sanitation facilities (see Rutstein and Johnson, 2004). Women exposed to conict early in their childhood are likely to live in households with poorer living conditions (column 7). The wealth factor score is lower by 977 and 675 per additional month of exposure to conict among women aged 0 to 3 and ages 4 to 8 in 1996 respectively. In addition to providing information on women's adult living conditions, outcomes discussed in this subsection provide a window to what types of households the children of individuals exposed to conict in early childhood are living in. Therefore, these eects are likely to provide explanations for the second-generation health impacts presented in the next subsection.

# 5.4 Impact on Intergenerational Health

The intergenerational impacts of conict on health are presented in Table 10. As discussed in section 4.2, the estimation strategy used is the same as for the rst generation, except I add child-specic controls. The sample consists of children under the age 5 whose anthropometrics were measured. I also include children born to women aged 22 to 29 in 1996 to provide support for the identication validation required by dierence-in-dierences assumptions. The falsication test supports that identication strategy, as estimates for all outcomes for children born to the experimental control cohort of mothers are statistically zero.

Although statistically imprecise, mothers' exposures to conict negatively aects child's height (column 1: table 10). Children born to women in all the three treatment groups are shorter by 0.005 to 0.011 standard deviations for their age. Child development in terms of weight gain is signicantly hampered by mother's exposure to conict in her childhood. Compared to children born to the control cohort of mothers, an additional month of mother's exposure to conict during her entire growth period decreases her child's weight for height z-scores by 0.030 standard deviations (5.2 percent less than control mean). Exposure to conict starting at older age is even more severe for second-generation weight for height. Women in cohorts age 4 to 8 and 9 to 15 in 1996 have children 0.039 and 0.041 standard deviations lighter for their height (6.7 and 7.1 percent less than the control mean). All the coecients are estimated precisely. Column 3 reports the conict's 22
<!-- Start of picture text --> 100%<br>90%<br>=oOoO 60% TM Rudimentary++<br>S so% Informality<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>90%<br>=oOoO 60% TM Rudimentary++<br>S so% Informality<br>= 40% ® Modern<br>O 30% Informality<br>S 20% TMFormality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br>100%<br>90%<br>@ 80%<br>5 70% .<br>= 60% TM Rudimentary<br>S 50% Informality<br>2 40% TM Modern<br>S 30% informality<br>S 20% ® Formality<br>10%<br>0%<br>2010 2020 2030 2010 2020 2030 2010 2020 2030<br><!-- End of picture text -->
45 Table 9 (Dependent Variable is **Rl:** Mobility Among the Top 10 Banks) Tobit Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|C|8.1069|2.2610|
|ENT (-5)|-.1274|-1.8081|
|CR5|-13.4431|-2.4905|
|AVG|-0.2088E-06|-4.2828|
|MDGR|.0349|4.1027|

# (Dependent Variable is **R2:** Mobility Among All Banks) Tobit Results

|Variable|Coefficient|T-Statistics|
|---|---|---|
|**C**|2.5356|2.98|
|ENT (-5)|1.6780|1.23|
|CR5|-6.8759|-3.76|
|AVG|-1.6884|-2.95|
|MDGR|.1349|3.11|
process evaluation of this sort is Norad (2010), which focuses on the IOM’s anti-trafficking programs. Another example is Asis and Aguinas (2012), which provides a high-level assessment of shortcomings and potential improvements in the pre-departure programs of the Philippines, Indonesia, and Nepal. Of the more limited number of impact evaluations, none to our knowledge use credible research designs that can allow identification of causal effects of the programs on migrant welfare or other outcomes. For example, IOM (2006) seeks to assess the impacts of an anti-trafficking informational program in Cambodia on the knowledge of anti-trafficking messaging among individuals remaining in Cambodia (non-migrants). It uses survey data on program participants and compares responses to surveys of non-participants, finding that participants have better knowledge about information relevant for anti-trafficking. The study does not describe how study participants and control group respondents were selected, so it is not possible to assess whether the two populations can be credibly compared so as to establish the causal impact of the program. The study also does not estimate impacts on migrants themselves, but only on knowledge of non-migrants.

Given the popularity of these programs, it is important going forward to conduct prospective, randomized evaluations of pre-departure trainings, or to find opportunities to use natural experimental designs so that causal effects can be established using observational data. It will also be important to measure impacts on the migrants themselves (rather than, for example, simply measuring information of non-migrants remaining behind). One ongoing study (Omar Mahmoud et al 2013) is doing precisely that, by randomly assigning departing labor migrants from the Philippines to different types of modified pre-departure orientation seminars, and tracking impacts on migrants over time.

# **2.3 Financial literacy programs for migrants and their families (whether before or after departure)**

Owing to the large wage gains possible when individuals migrate from developing to developed countries, and the large amounts sent home by migrants in the form of remittances, migrantorigin households are very often faced with managing amounts of money substantially larger than the household budgets of those without migrant members. This has raised concerns among policy-makers and non-government organizations that financial decision-making in migrant households may be suboptimal, particularly in households whose members migrated relatively 8
According to the National Bureau of Statistics of China, rural out-migrant workers are defined as individuals who have rural household registration status but left their homeland and have worked outside the towns and counties for at least 6 months. As shown in Figure 1, the number of rural out-migrant workers rose from around 20 million in 1990 to 62 million in 1993, 132 million in 2000, and nearly 160 million in 2011.<sup>4</sup> Most of these migrant workers, constrained by their lower education levels, work in the manufacturing and construction sectors in cities. However, rural-to-urban migration has significantly benefited both recipient and sending areas. For example, the Pearl Delta and Yangtze River Delta regions have emerged as one of the most important global manufacturing bases since the 1990s, partly because of the constant supply of cheap rural labor (Huang and Zhan 2005). And the large amount of remittances has greatly contributed to the economic development of inland rural areas through both consumption and investment and has helped reduce rural poverty since the late 1990s (de Brauw and Rozelle 2008).

Unlike many other international or internal migrations, rural-to-urban migration in China has its own features. Because of the presence of the household registration system, rural migrants find it difficult to permanently settle down in recipient cities for a long time.<sup>5</sup> Also, they are largely denied access to many of the social welfare programs, such as education and medicine, to which their urban counterparts are entitled. Indeed, typically, migrant workers on average return home two to three times annually and spend less than 9 months in recipient cities (Zhao 1999). Another important feature of rural-tourban migration in China is the emergence of the village-based migrant network. Because of decades of separation, rural households have limited ties with urban communities and little access to institutional supports at the destinations, making them rely on their originbased networks to find jobs (e.g., Solinger 1999; Zhao 2003). This is also common in

> 4 Some other estimates are greater. For instance, Wong (2012) suggests that China’s urban population expanded by 210 million.

> 5 This issue has changed substantially in the past few years. It has become easier for migrants to settle down in small cities, though the access to vital social services remains disadvantaged for migrants relative to residents with local urban _hukou_ .

7
# Enhancing Human Capital in Children: A Case Study on Scaling<sup>∗</sup>

Francesco Agostinelli<sup>†</sup> Ciro Avitabile<sup>‡</sup> Matteo Bobba<sup>§</sup> Originally published in the Policy Research Working Paper Series on _May 2021._ This version is updated on _August 2022._ To obtain the originally published version, please email prwp@worldbank.org.

**Keywords** : Children’s Skills; Parental Investment and Community Engagement; Science of Scaling.

**JEL codes** : C90, C93, D02, I3, J1.

> ∗We are grateful to the _Consejo Nacional de Fomento Educativo_ (CONAFE) for the generous collaboration throughout this project, Alonso Sanchez for his initial input into the project, and Miguel Angel Monroy for excellent research assistance. We thank the anonymous referees, as well as Jere Behrman, Horacio Larreguy, John List, Giuseppe Sorrenti, and Stephane Straub for helpful comments and discussions. Avitabile acknowledges financial support for data collection from the Strategic Impact Evaluation Fund (SIEF) of the World Bank and the _Consejo Nacional de Evaluaci ́on de la Politica de Desarollo Social_ (CONEVAL). Bobba acknowledges financial support from the AFD, the H2020-MSCA-RISE project GEMCLIME-2020 GA No 681228, and the ANR under grant ANR-17-EURE-0010 (Investissements d’Avenir program). This study is registered in the AEA RCT Registry and the unique identifying number is AEARCTR-0001645.

> †Economics Department, School of Arts and Sciences, University of Pennsylvania (US). E-mail: `fagostin@sas.upenn.edu`

> ‡World Bank (US). E-mail: `cavitabile@worldbank.org`

> §Toulouse School of Economics, University of Toulouse Capitole (France).

E-mail: `matteo.bobba@tse-fr.eu`
As an additional robustness check, we use individual level observations from 173 of the original surveys to control for possible endogeneity in life expectancy at birth. While we cannot observe individual life expectancy at birth, we do know the average cohort life expectancy at � birth, ���� = ∑��������� , for _N_ individuals in cohort _j_ and economy _c_ . This average is exogenous to the individuals _i_ in that cohort. Consequently, the group mean life expectancy can be used as an instrumental variable for actual life expectancy.<sup>21</sup> We report the equivalent specifications of equation 1 using this endogeneity correction in table 7. The estimates confirm that an additional year of life expectancy at birth increases schooling by 0.11 year and that the effect is positive and statistically significant for males and females and for rural and urban residents. It also confirms that increases in life expectancy at birth of the parents further increases the schooling of their children across all these groups.

# **7 Discussion & Conclusion**

This study covers a wide group of economies, extensive time range, and exploits the crosscohort variation within an economy to identify the impact of life expectancy at birth on human capital accumulation. We find that a one-year increase in life expectancy at birth increases completed years of schooling over the lifetime by between 0.11 and 0.15 years. This is comparable to the estimates of 0.11 year in Sri Lanka (Jayachandran and Lleras-Muney 2009), and 0.17 year in a cross-section of economies (Hansen, 2013).

In our sample, life expectancy at birth and completed years of schooling increase by 31 years and 5 years, respectively, for the youngest cohorts compared to the oldest birth cohorts. Our

> 21 In the above specifications, ����� = ���� + ���, which states that individual _i_ ’s life expectancy at birth in the country _c_ deviates from cohort _j’_ s mean life expectancy by ���, which is by construction orthogonal to mean. Since ��� will be contained in the error term, the condition, ��������, �����= 0, must hold.  Royalty (2000) has used state tax rate as an instrument for marginal tax rate in explaining employees’ health insurance eligibility. Similarly, a series of studies following Ruhm (2000) exploited variation in state or county level unemployment rate while explaining individual health behavior during a recession.

20
2 implication being that hospitals ought to reduce their capital stock. It is important to realize that the issue of over-capitalization is different from the issue of economies of scale. The latter concerns the effect on costs of further expansion of output and therefore bears on the question of whether output levels are too high or too low. Authors of many early cost function studies confused the two issues. Writers such as Feldstein (1967) and Anderson (1980), for example, claim to address the issue of economies of scale, but in the event analyzed over-capitalization **.2** The currently favored approach to over-capitalization dates back to Cowing and Holtmann (1983), who proposed analyzing the issue by reference to the parameters of the variable cost function. In the short-run, when the capital stock is fixed, short-run costs, CS, can be written as the sum of total fixed costs and total variable costs

# (1) Cs = F + Cv(Y WV,K) ,

where F=wKK is total outlay on the fixed input, y is output, wv is the price(s) of the variable input(s) and K is the stock of capital. It is worth noting that K enters CS twice: once as a determinant of fixed costs and once as a determinant of variable costs - an issue to which we return below. The long-run cost-minimizing capital stock, K*, is that which minimizes costs at each level of output. Differentiating (1) with respect to K and setting the derivative equal to zero yields (2) -wK = 6Cv/6K.

To test for over-capitalization Cowing and Holtmann suggest estimating the variable cost equation Cv(y,wv,K) and testing the hypothesis that 6Cv/6K=-wK.

The intuition here can be illustrated with fig 1, which shows three possible input mixes to produce a single output y. Without loss of generality set wK equal to 1. Then the vertical intercept of each isocost line is equal to Cs, the value of K is equal to F and the vertical distance between Cs and F is equal to Cv. The long-run optimal stock of capital is K. At point '1', the capital stock is too small and a move from point '1' towards point '2' would cause total cost to fall. (Fixed costs would rise, but this rise would be more than offset by a fall in variable costs.) Hence at point '1' 6Cv/6K<-wK. At point '3', by contrast, the capital stock is too large and a reduction in capital towards K would reduce total cost. (Variable costs would rise, but this would be more than offset by the reduction in fixed costs.) Hence at point '3' 6Cv/<sup>6</sup> K>-wK. Only at point '2', where the cap:tal stock is at its long-run equilibrium value, is 6Cv/5K equal to -wK.

2. That the approach used by Feldstein is itt-suited to the analysis of economies of scale was argued long ago by Davis (1968) and Mann and Yett (1968). That his approach is better suited to the analysis of over-capitaLization hasnot, to our knowledge, been argued before.
choice of being a wage earner as preferable to entering self-employment in the informal sector (often characterized by ‘defensive entrepreneurship’, see Sections 2 and 3<sup>25</sup> ).

On the other hand, evidence concerning the relationship between education and the postentry performance of new businesses in DCs is uncontroversial and consistent with what has been found using data from richer countries. For instance, Van der Sluis, Van Praag and Vijverberg (2005) conclude that an additional year of schooling raises entrepreneurial income by an average of 5.5%; by the same token, McPherson (1996) found that in Botswana and Zimbabwe business owners who have completed secondary school run faster-growing firms than those proprietors with no schooling; finally, Goedhuys and Sleuwaegen (2000 and 2010), using data respectively from Côte d’Ivoire and from eleven Sub-Saharan African countries, found unequivocal evidence that formal education of the entrepreneur positively affect a firm’s growth performance, respectively measured in terms of the growth rates of sales and employment (in both studies, the greatest effect on growth is found for entrepreneurs holding a university degree)<sup>26</sup> .

# **<u>4.2.4 Previous job</u>**

As pointed out in Section 3.1, entrepreneurship is characterized by sectoral and geographical inertia; far from being a disadvantage, persistence in entrepreneurship generates above-the-average post-entry performance, since past experience in the same sector and in the same area is often a signal of better skills and informational advantages (see, for instance, Roberts, Klepper and Hayward, 2011 **)** .

Indeed, Michelacci and Silva (2007) found that the fraction of entrepreneurs who set up their businesses in the area where they were born was significantly higher than the corresponding share for dependent workers and, more importantly, that firms created by locals were bigger, more valuable, more capital-intensive and better financed than their counterparts created by non-locals. The authors interpreted their findings by arguing that local entrepreneurs can on average better exploit the economic and financial opportunities

> 25 By the same token, Nafziger and Terrell (1996), using evidence from India, found that higher education of the founding entrepreneur reduces firm survival, indicating the importance of outside opportunities in paid wage employment within the formal sector.

> 26 By the same token, Ligthelm (2011) found that business management skills were one of the strongest predictor of survival among small informal firms in South Africa.

23
**Table 3 ­ Sales Growth Rates (in %) comparisons: before and after the crisis**

||**P**|**recrisis**<br>||**P**|**ostcrisis**<br>||
|---|---|---|---|---|---|---|
||**Average**|**95% Con **|**f. Interv. **|**Average**|**95% Con **|**f. Interv. **|
|**Innovative**|||||||
|(introduce new product/process)|36.38|_27.05_|_45.71_|‐26.48|_­29.68_|_­23.29_|
|**Noninnovative**|||||||
|(did not introduce new product/process)|23.39|_16.70_|_30.07_|‐25.34|_­28.46_|_­22.22_|
|**Innovative**|||||||
|(invest in R&D)|45.29|_27.87_|_62.71_|‐27.02|_­31.82_|_­22.21_|
|**Noninnovative**|||||||
|(didnotinvestin R&D)|24.61|_19.76_|_29.45_|‐25.55|_­28.08_|_­23.02 _|
|**Young**<br>(< = 5 years old)|45.52|_28.10_|_62.94_|‐30.13|_­36.77_|_­23.48_|
|**Older**<br>(>=6 years old)|26.90|_20.97_|_32.84_|‐25.42|_­27.78_|_­23.06_|

Source: EFCS 2009‐ ES 2008 Data shows that before the crisis, growth rates for innovative (young) firms showed to be significantly higher than non‐innovative (older) companies, at 5% level of significance.<sup>10</sup> After the crisis, however, the reduction in growth rate was higher for innovative (young) firms, though not statistically significant. These results seem to suggest that the crisis impact was higher for innovative and young companies. However, besides lacking of statistical significance for the comparisons after the crisis, these results reflect only a partial analysis. One can argue whether the difference of sales growth performance between the refereed groups of firms persist when controlling for other firm characteristics that are likely to affect sales performance.

In order to address this, we apply an empirical approach to discuss to what extent innovative (young) firms were more affected by the global downturn once firm idiosyncratic characteristics are taken into account. Two empirical methods are used.

> values for both 2004 and 2007. The criterion was to consider as an outlier the observations lying outside the interval defined as the average � three standard deviations of the refereed variable expressed in logarithm form.

> 8 June/July 2008 to June/July 2009. As the 2008/2009 growth rate ranges from ‐100% to 100%, we didn’t control for outliers.

> 9 Some World Bank reports (like the EU10 Regular Economic Report) state that the acute phase of the crisis in these countries was in the second half of 2008 (specifically September 2008), but the severity of the impact was perceived only in 2009 statistics, even though the economic activity has stabilized in the second half of 2009. As we only have data for June/July 2009, we just use this point as a postcrisis reference.

> 10 For the precrisis comparison between innovative and noninnovative companies, the results are valid regardless the criteria used to classify innovative companies.

6