rafmacalaba/datause-encoder-data huggingface dataset:
even further, as they decrease the maximum waves that can propagate towards the embankment without breaking.

Although the previous functions result in sheltering of the coast from extreme events, applying mangroves to reduce the wave loads on dikes requires considering several limiting factors. Firstly, mangroves have specific habitat requirements, and they may not grow at every location. Mangroves grow at intertidal areas with low wave action, and sufficient freshwater and sediment input (Alongi, 2002), and different mangrove species have specific requirements in terms of temperature, inundation, and salinity levels that they can tolerate. Potential sites for mangrove establishment have been mapped in Bangladesh (Gijón Mancheño et al., 2021), indicating locations where mangroves could grow naturally, and those where erosion mitigation would be necessary for mangrove colonization. However, the potential wave load reduction by mangroves at such sites has not been investigated, which hinders accounting for the effect of mangroves on embankment designs in future CEIP programs.

2.2. Mangrove geometry Wave reduction by mangroves is highly dependent on the location of the trees on the tidal profile and on their geometry. Mangrove species often grow in distinct bands between mean sea level (MSL) and mean high water (MHW), depending on their relative tolerance to inundation (Snedaker, 1982). Some species, denoted as pioneers, have a relatively higher tolerance to salinity and are the first to colonize at locations near MSL. Within the salttolerant species found in Bangladesh, _Sonneratia apetala_ (locally known as Keora) and _Avicennia officinalis_ (locally known as Baen) are the pioneers with highest planting survival (Saenger and Siddiqi, 1993). Since _S. apetala_ is the most planted species, for which there is also most available information about its geometrical and structural properties, this species is chosen for further analysis.

At sites where mangroves were not present before, such as on newly accreted land, mangrove trees will need time to colonize and grow. Ecologic studies suggest that mangroves can reach their full height in 10 years (Wang et al., 2021), and their wave attenuation capacity will increase as the surface area of the tree enlarges by growth of the trunk diameter, and by increasing the size and number of branches (Maza et al., 2021).

5
|**Table 2: Program Implementat**|**ion**<br>(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Mean<br>[SD] in|Differ<br>|ence betwe<br>l|en [...]<br>||P‐valu|e for differ|ence...|P‐values<br>joint tests|
||Control|an|Controgr|oup||Group 1|Group 1|Group 2|G1=G2=G3=|
||Group|Group1|Group2|Group3|N|and 2|and 3|and 3|0|
|**PANEL A: Administrative data from**|**CGAs**|||||||||
|Step 1:<br> First visit done|0|0.991***|0.973***|0.976***|3,596|0.064*|0.085*|0.594|0.000***|
|successfully|[0]|(0.009)|(0.006)|(0.005)||||||
|Step 2:<br> Second visit done|0|‐0.008|0.415***|0.466***|3,596|0.000***|0.000***|0.003***|0.000***|
|successfully|[0]|(0.027)|(0.017)|(0.015)||||||
|Step 3:<br> Business is formal<br>|0|0.002|0.146***|0.171***|3,596|0.000***|0.000***|0.051*|0.000***|
|acording to the CGA<sup>β</sup>|[0]|(0.02)|(0.013)|(0.011)||||||
|Step 4:<br> Additional services:||||||||||
|Business registered to CGAs|0<br>[0]|0.009<br>(0.02)|0.129*** <br>(0.012)|0.154*** <br>(0.011)|3,596|0.000***|0.000***|0.041**|0.000***|
|Business attended to at least|0|0.006|0.113***|0.141***|3,596|0.000***|0.000***|0.016**|0.000***|
|one group training at CGAs|[0]|(0.019)|(0.012)|(0.01)||||||
|**PANEL B: Endline survey data**||||||||||
|_Formalization process: (only formal_|_businesse_|_s)_||||||||
|Number of days it took to||||||||||
|formalize<sup>η</sup>|22.9|‐14.6|‐21.6**|‐15.6**|329|0.557|0.929|0.322|0.11|
||[27.3]|(11.4)|(8.6)|(7.5)||||||
|Amount paid for formalization|66,931 <br>[57,036]|‐73,289***<br> (19,449)|‐57,145***<br>(15,343)|‐56,552***<br>(13,302)|332|0.421|0.333|0.957|0.001***|
|Share of business who paid<br>something to formalize|1|‐0.788***|‐0.693***|‐0.773***|332|0.706|0.945|0.563|0.001***|
||[0]|(0.246)|(0.194)|(0.168)||||||
|_Program Knowledge:_||||||||||
|Ever heard of the Entreprenant|0.131|0.187***|0.207***|0.252***|2,582|0.668|0.114|0.083*|0.000***|
|status|[0.338]|(0.041)|(0.026)|(0.023)||||||
|Was able to explain what is the|0.055|0.174***|0.148***|0.198***|2,582|0.508|0.476|0.023**|0.000***|
|Entreprenant status|[0.228]|(0.034)|(0.022)|(0.02)||||||

Notes: Column 1: Standard deviations presented in brackets. Columns 2‐4: coefficients and standard errors (in parentheses) from an OLS regression of the firm owner/firm characteristic on treatment dummies, controlling for strata dummies (dummies for each triplet). ***, **, * indicate statistical significance at 1, 5 and 10%. β: For the control group and group 1, CGA did not have any information as they are not following up with these businesses. η: Top‐coded at the 99th percentile.

34
Venables (eds), _Spatial Inequality_ (Oxford University Press).

- Ravallion, M., S. Chen (1999), “When Economic Reform is Faster than Statistical Reform: Measuring and Explaining Inequality in Rural China”, _Oxford Bulletin of Economics and Statistics_ , 61(1), 33-56.

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- Rodriguez, E. (1998), “International Migration and Income Distribution in the Philippines”, _Economic Development and Cultural Change_ , 46(2), pp. 329-350.

- Rural Economic Research Center, The Ministry of Agriculture of China (2003), _Chinese Rural Research Report 2001_ (Beijing, China Financial & Economic Publishing).

- Schiff, M. (1996), “South-North Migration and Trade”, The World Bank Working Paper No. 1696, The World Bank.

Schultz, T. W. (1964). _Transforming Traditional Agriculture_ (New Haven, Yale University Press).

- Shand, R.T. (1987), “Income Distribution in a Dynamic Rural Sector: Some Evidence from Malaysia”, _Economic Development and Cultural Change_ , 36(1), 35-50.

- Stark, O. (1980), “Urban-to-Rural Remittances in Rural Development”, _Journal of Development Studies_ , 16(3), 369-374.

Stark, O. (1991), _The Migration of Labor_ (Oxford, Basil Blackwell).

- Stark, O., J.E. Taylor, S. Yitzhaki (1986), “Remittances and Inequality”, _Economic Journal_ , 96(383), 722740.

- Stark, O., J.E. Taylor, S. Yitzhaki (1988), “Migration Remittances and Inequality: A Sensitivity Analysis using the Extended Gini Index”, _Journal of Development Economics_ , 28(3), 309-322.

- Straubhaar, T., F. P. Vadean (2005), “Introduction: International Migrant Remittances and their Role in Development”, in OECD, _Migration, Remittances and Development_ (Paris, OECD Publishing).

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- Taylor, J.E., T.J. Wyatt (1996), “The Shadow Value of Migrant Remittances, Income and Inequality in a

29
more likely to formalize, be it registering their businesses or acquiring a tax identification number.

These results on practices are consistent with previous findings in the literature. As indicated above, Berge et al. (2014) test the effectiveness of a training program relying on group sessions only (similar to our basic training), and find no impact on the implementation of new business practices among female participants. Valdivia (2012) found that a program combining group and individual sessions and covering both technical and managerial skills successfully helped female entrepreneurs adopt better business practices.

Table 4 reports estimates of the impact of the basic training and the enhanced training on the business outcomes of the average participants. We consider six measures of revenues, profits, and earnings and do not observe any significant impact of either version of the treatment. These results are consistent with previous findings in the literature.

As we rely on self-reported measures of revenues, profits, and earnings, we need to consider the possibility that the training program may have affected the participants’ numeracy and accounting skills and therefore their accuracy in reporting these measures. For instance, trained entrepreneurs who might have overstated their profits prior to the intervention may be better able to calculate their profits after the training and report lower profits even if their actual profits have remained unchanged (or even increased). This would lead us to underestimate the impact of the training. (The bias may also go the other way, in which case we would overestimate the impact of the training.) Our end-line survey instrument included a digit-span recall test and we do not observe any significant difference in the average performance of the three groups. We also do not observe a stronger correlation between the different measures of business outcomes in the treatment groups than in the control group. For these reasons, we conclude that our estimates of the impact of the intervention are not biased by the intervention’s effect on participants’ numeracy skills.

In addition to collecting data on the entrepreneur’s main business activities, we asked questions about secondary businesses. Participants in both training programs are not more likely to have launched new activities after the intervention (results available upon request). This is consistent with the fact that we do not observe any impact on our measure of aggregate monthly income from all sources and on the average value of household assets.

Following the possibility that more “seasoned” entrepreneurs may have benefited differently from the intervention, we test for heterogeneity of the treatment effect for entrepreneurs with different levels of experience and report the results in Table 5. We define experience as the entrepreneur’s ‘tenure’, measured by the number of years since the entrepreneur launched her main business activity. We estimate a positive and significant coefficient for the interaction of 12
Table C.11: National Estimates of Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference

||||Sector of W<br>|ork<br>||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.010|0.033|0.020|-0.034|-0.923|
||(0.042)|(0.017)|(0.025)|(0.034)|(0.658)|
||[0.809]|[0.053]|[0.433]|[0.319]|[0.160]|
|Mean of Dependent|0.342|0.072|0.126|0.279|7.623|
|(Pre-Reform Cohorts)||||||
|First Stage F-Statistic|6.93|7.91|7.91|7.91|6.60|
|N|24,882|24,607|24,607|24,607|24,649|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical responses � are assigned the maximum value. Years of Schooling _izy_ is the predicted level of schooling, instrumented with the free primary education (FPE) intensity measure _Izy_<sup>FPE</sup> , two mother tongue instruction (MTI) intensity measures _Izy_<sup>MTI-T</sup> and _Izy_<sup>MTI</sup> , which denote the measures for MTI regions without and with script change, respectively, and the interactions for regions in which two interventions occurred. All samples include women in birth cohorts from 1970 to 1988. All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression, and the second-stage estimate in column 5 is generated using a tobit model. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.27
# **Table 4 - Comparison of income distribution with and without migration**

||Income in the presence<br>of migration<br>(observedincome)|sence of<br>Income in the ab<br>migration<br>(simulatedincome)|
|---|---|---|
|Gini coefficient|0.454|0.530|
|Average per capita income (_yuan_)|3111|2580|
|FGT index (%)|||
|_P_0- poverty incidence|14.3|27.5|
|_P_1- poverty depth|5.6|12.0|
|_P_2- poverty severity|3.1|7.1|
|Nu<br>mberofobservations|1208|1208|
|Note: Poverty line is equal to 850_yuan_.|||

Figure 1 presents the growth incidence curve calculated from the difference between observed income and predicted income, which show the changes in per capita income resulted from migration and remittances for each segment of population. As the GIC is above the zero axis at all points, income growth was positive for the entire population. A strictly negative-sloped GIC, which displays changes in household per capita income of each percentile ranked from poor to rich, indicates that the poorer households experienced a higher rate of growth due to migration. In other words, the poorer the households, the larger their proportionate income gains. For the poorest group (below the 25<sup>th</sup> percentile), household per capita income increased more than 80% (with the poorest poor gaining even more from migration); while that of the richest group (above 75<sup>th</sup> percentile), increased less than 25% (with the richest rich gaining even less).

21
The other prospect is to expand other sources of export demand. South-South trade is often cited as a potential source of growth in developing countries. This deep embedment in global value chains also appears in the structure of developing countries’ imports. _Figure 15_ below shows low- and middle-income country exports to other low- and middle-income countries (i.e. South-South trade) by broad economic category as percentage of total exports of BEC. During the past two decades, the export shares to other developing countries have been continuously growing for capital goods, consumption goods, and intermediates. This reflects a growing importance of South-South trade. By definition, high-income countries have absorbed a declining percentage of exports from developing countries. Preliminary data for 2009 indicate a strong increase of South-South trade, indicating that this is a potentially promising source of demand growth in the future.

**Figure 15: South-South Trade by Product Category (% of Total Exports by Product Category), 1970-2009**

<!-- Start of picture text --> 50%<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Capital goods Consumption goods Intermediate goods<br>1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text --> Source: Own calculations. Data: UN Comtrade. NB: Figures are exports from Low- and Middle-Income Countries to other Low- and Middle-income countries by Broad Economic Category.

Regarding the composition of South-South trade, more than a third of developing countries exports of intermediates went to other developing countries in 2008. The preliminary 2009 data even indicate a jump to almost 50%. Capital and consumption goods, however, are increasingly exported to developing countries as well, reaching pre-crisis shares of 30% and 20%, respectively. But here again, the structure of world trade according to GVCs may create an obstacle in the short run to South-South trade growth. Figure 15 showed that the greatest growth potential of developing countries’ exports over the past decade has been in intermediates. This indicates that South-South trade also is to some extent molded by global value chains and the processing of intermediates to serve these chains. In this sense, the expansion of South-South trade depends still on the functioning of GVCs.

(3) A final policy conclusion is about trade politics in industrialized countries during a time of severe economic downturn. Because of the extent of global value chains, developed country firms – generally the lead firms in the global value chains – depend on imports for their inputs and profitability<sup>32</sup> and thus these firms are less inclined to support trade protection than they were in

> 32 For some econometric-based evidence, see Milberg and Winkler (2009b).

28
# **4 Heterogeneity**

Aside from evidence on the causal impact of mobile phone penetration on GDP growth, an important policy question is the distribution of the impacts across heterogeneous groups. For instance, how does the impact of mobile vary between developed and emerging markets. In addition, how did the arrival of mobile phones spur economic growth in countries that hitherto had low access to communication technologies such as the fixed line telephone network? In this section, I provide evidence on these questions by re-estimating the main IV regression while interacting measures of mobile penetration with measures of countries’ income group and fixed telephone penetration at the baseline.

First, I explore the economic impact of mobile phones between developed and developing economies. Using the World Bank’s income classification at the baseline, I split countries into two main groups: developing vs developed.<sup>31</sup> I then interact the mobile penetration measures with a dummy variable equal to 1 for developing countries and 0 otherwise. Results are shown in Table 5. The results show that while mobile phones have a positive impact on local economic growth across all income groups, the effect is higher among developing countries. For instance, the results in column 2 suggest that a 10 pp increase in mobile penetration is associated with a 0.5 pp growth in nightlights in developed (high and upper middle income) countries, compared to a 0.9 pp increase in developing (low income and lower middle income) countries. The effect of 2G connectivity is however only present in developing markets (columns 3-4). These findings are plausibly indicative of the economic importance of mobile voice and SMS messaging which incidentally is the backbone of digital finance platforms like mobile money in most developing countries (Suri and Jack, 2016; Suri, 2017). However, the findings in relation to 3G/4G are interesting: in column 5, the results indicate that effects of 3G/4G connectivity are about 0.97 pp higher in developing markets relative to developed markets. However, the coefficient of the interaction losses statistical significance in column 6, where we include controls for economic shocks at the region level. Nonetheless, the results provide suggestive evidence that mobile broadband internet induce economic impacts in both developed and developing countries.

In addition, I explore how the relationship between mobile phone penetration and economic activities varies according to pre-existing access to digital communication, specifically fixed line telephony. For instance, in countries with relatively high fixed line telephony prior to the arrival of mobile phones, the additional impact of the mobile phone particularly 2G connectivity could be low compared to countries that hitherto had low access to any form of telephone

> 31Countries in the World Bank’s low income (LIC)and lower middle income (LMIC) groups are classified as developing, while countries in the upper-middle income (UMIC) and high income (HIC) groups are classified as developed.

18
populations under review, metrics and model specifications used. Most early research examining cognitive skills in the context of labor market outcomes is based in the United States and uses data from the National Longitudinal Survey of Youth (NLSY), which includes a measure of cognitive and vocational ability—the Armed Services Vocational Aptitude Battery (ASVAB) (Cawley, et al., 1996; Cawley, Heckman, and Vytlacil, 2001). Findings from these studies show cognitive ability having modest effects on wages.

Subsequent research on the role of cognitive skills moved toward using comparable data from large-scale international reading literacy tests, such as the International Adult Literacy Survey (IALS) and the Adult Literacy and Life Skills Survey (ALLS). It is argued these reduce the heterogeneity of cognitive skills metrics and are also better measures of functional literacy and reading proficiency (Barrett, 2012; Barone and van de Werfhorst, 2011; Fasih, Patrinos, and Sakellariou, 2013; Green and Riddell, 2003; Hanushek and Zhang, 2006). Separate studies using the Canadian IALS (Green and Riddell, 2003) and the Australian ALLS (Barrett, 2012) find that cognitive skills significantly predicted higher earnings. Similarly, IALS data have been used in cross-country comparisons.

More recent research examining the effect of cognitive skills on earnings has used reading assessment data from the Program for the International Assessment of Adult Competencies (PIAAC) survey, which is sponsored by the OECD and is designed to measure key cognitive and workplace skills. The PIACC survey measures cognitive skills in three domains: literacy, numeracy, and problem solving in technology-rich environments. It addresses some of the measurement issues noted with the IALS and ALLS.<sup>6</sup> Hanushek et al. (2013) used the PIAAC data to estimate the returns to skills in 22 countries. Their findings indicate that higher cognitive skills (proxied using the numeracy and literacy skills components of the PIAAC assessment) lead to higher wages across all countries, with prime-age workers (ages 35 to 54) showing higher returns than recent entrants to the labor market. When they added years of schooling to the model, along with numeracy skills, their results showed that the estimated effect of cognitive skills went down by about 43 percent, but coefficients remained positive and significant.

In contrast to the literature on returns to cognitive skills, which demonstrates strong links between cognitive skills and labor market options, similar evidence on _noncognitive_ skill payoffs is relatively sparse—and it is more difficult to consolidate findings. This is largely due to the variety of ways in which noncognitive skills are defined, measured, and interpreted. In general, however, available evidence shows that noncognitive skills have small positive effects on earnings, and the

> 6 See Hanushek et al. (2013) for a brief discussion of these issues.

5