rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Concluding remarks**

This study documented for the first-time patterns in export specialization from an activity perspective and showed how it can generate additional insights beyond those based on the product perspective. First, export incomes from production activities decline and engineering, managerial, and services support activities grow as countries develop. Second, countries initially specialize along the extensive margin (shifting activities across industries) but later along the intensive margin (shifting activities across occupational classes). Third, new activity specialization is strongly related to the proximity of this activity to the initial export basket, in particular for specializations along the extensive margin and in routine intensive occupations. Fourth, countries that defy proximity and diversify quicker appear to grow faster in GDP per capita. This is correlation however and no claim for causation is made.

We see at least two promising avenues for further research. One avenue is in the modeling of structural change and the role of international trade. The canonical macro-structural change framework focuses on the sectoral composition of the economy in terms of employment and valueadded. Trade can shape the sectoral composition in various ways (Alessandria, Yi and Johnson 2021). Lower trade barriers facilitate specialization, for example through shifting comparative advantage and promoting economies of scale. Sectoral specialization will consequently affect the sectoral composition of the economy. And given a set of trade barriers, policy changes or technology shocks to the economy may also affect specialization patterns and consequently sector composition. Trade barriers are typically related to products whereas technological change such as automation affects particular activities rather than products or sectors. Modeling the composition of the economy in terms of activities in addition to sectors appears therefore to be a promising way forward as for example in Bárány and Siegel (2018) and Duernecker and Herrendorf (2022).

A second avenue for research is in further developing an integrated product and activity data set. Our data tracks exports at a rather aggregate industry level compared to the detailed product level export data that is available. Suppose that production technologies for a particular detailed product are the same around the world. In that case, cross-country differences in the occupational composition of the exporting industry (as documented in this study) are due to a different set of products being exported. In that case, activity specialization across the extensive margin can also 28
interval of 27 percent to 31 percent empty miles. Figure 3 presents the individual study estimates, their weights, and the mean estimate in a forest plot. The _I_<sup>2</sup> index is 99.27 percent, suggesting a very high degree of true heterogeneity across the studies.

Given the substantial heterogeneity across the studies, I conduct a meta regression of effect sizes on the statistic estimated, the data source, the year of the study, and an indicator for whether the country is High Income. I present the regression results in Column 1 of Table 1. Studies of High Income countries find significantly lower rates of empty miles compared to studies of Non-High Income countries. In addition, vehicle-level studies find lower levels of empty miles, suggesting that trip- or site-level studies may be more likely to sample empty trips relative to vehicle-level studies.

In Column 2, I extend the meta regression to include countries as moderators. Given the country moderators, High Income status has a stronger negative effect on empty trips. In addition, both site-based and vehicle-based studies find lower levels of empty miles than trip-based studies. Finally, backhaul studies tend to find lower levels of empty miles than empty-mile or empty-trip studies, which is consistent with a non-zero number of fronthauls biasing backhaul study estimates downward.

**Regional Patterns** Looking at the regional level, Eurostat has collected surveys of truck trips since 1999, for both European Union and European Free Trade Association members. In Figure 4, I report average empty trip shares for countries in the Eurostat dataset. In Panel A, which aggregates over all types of trips, there is significant variation in empty mile share within region, with island states such as Cyprus and Ireland having empty trip shares which are 15 to 20 percentage points higher than states like Belgium and Denmark. Panel B compares the empty share of domestic versus international trips for each country. The vast majority of countries have more empty trips when their truckers are traveling domestically compared to international trips.

**Country-Specific Trends** So far in this section, I have aggregated over many decades of studies. Over the past decades, different countries have experience divergent trends in the share of empty trips. While most studies are short-term and unable to capture time trends, Gonzalez-Calderon et al. (2012b) and McKinnon and Ge (2006) both cover several decades of consistent measurement by governmental agencies. I plot the time estimates in Figure 5. Up to 1975, both the United Kingdom and Colombia saw similar levels of empty miles between 30 and 35 percent. Both countries saw declines between 1975 and 2000, with Colombia seeing a rapid decline from 1975 to 1980 and a gradual decline afterwards, and the United Kingdom seeing consistent declines. After 2000, the United Kingdom saw a reversal

9
# **Who migrates overseas and is it worth their while? An assessment of household survey data from Bangladesh**

### **Manohar Sharma and Hassan Zaman**

**Key words:  Bangladesh, migration costs, remittance impact, propensity score matching**

## **Acknowledgments**

Manohar Sharma is a Senior Poverty Specialist in the East Asia region and Hassan Zaman a Lead Economist in the Poverty Reduction Group of the World Bank. We are grateful for helpful comments received from Syed Ahmed, Louise Cord, Ana Revenga, Marcin Sasin and Iffath Sharif. All errors are our own. The views expressed in this paper are the authors and they should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
**ONLINE   APPENDIX**

**Online   Appendix   Table   III:   Test   Scores   across   Subjects   -­‐   Impact   on   Market   Outcomes**

|||**Test Scores(Year 2)**||
|---|---|---|---|
||**English**|**Urdu**|**Math**|
||(1)|(2)|(3)|
|**_Panel A: No Controls_**<br>||||
|Report Card|0.129**|0.105*|0.149*|
||(0.0648)|(0.0578)|(0.0765)|
|Observations<br>|112|112|112|
|R-Squared|0.338|0.369|0.377|
|**_Panel B: Baseline Control Only_**<br>||||
|Report Card|0.0952**|0.0931**|0.138**|
||(0.0467)|(0.0404)|(0.0585)|
|Baseline|0.630***|0.677***|0.679***|
||(0.0617)|(0.0575)|(0.0752)|
|Observations<br>|112|112|112|
|R-Squared|0.660|0.698|0.64|
|**_Panel C: Baseline and Village Controls_**<br>||||
|Report Card|0.100**|0.101**|0.147**|
||(0.0482)|(0.0406)|(0.0588)|
|Baseline|0.624***|0.675***|0.678***|
||(0.0640)|(0.0590)|(0.0733)|
|Observations|112|112|112|
|R-Squared|0.662|0.707|0.644|
|Baseline Test Score(Mean)|-0.044|-0.015|-0.037|

# _Notes:_

*   significant   at   10   percent   level   **significant   at   5   percent   level   ***   significant   at   1   percent   level This   table   presents   impact   of   the   intervention   on   village   average   test   scores   for   particular   subjects   (English,   Urdu and   Math).   We   observe   gains   between   0.09   -­‐   0.15   standard   deviations   in   treatment   villages   relative   to   control. The   outcome   variables   are:   Round   2   village   level   test   scores   in   English   (column   1),   in   Urdu   (column   2),   in   Math (column   3).   All   regressions   include   district-­‐fixed   effects   and   robust   standard   errors.   Panel   A   has   no   controls;   Panel B   includes   baseline   test   scores   as   a   control;   and   Panel   C   includes   baseline   test   score   as   well   as   the   same   village controls   as   previous   tables.   Baseline   Test   Score   (Mean)   displays   the   baseline   test   score   mean   for   the   sample   for   all outcome   variables.   Columns   1-­‐3   are   run   on   all   112   sample   villages.
# **4. THE IMPACT OF INTANGIBLE INVESTMENT: EVIDENCE FROM EXPORT GROWTH AND TFP PATTERNS**

Ultimately, the case for better measurement of intangibles rests on its potential to quantify knowledgebased sources of growth which are largely consigned to the black-box of TFP in current growth analytics. Although CHS (2009) and similar studies have shown that growth accounting calculations which include the intangible capital stock as an input indicate a sizable role of intangibles as a source of growth, these are still essentially accounting exercises which take the asset nature of intangibles as a given. There is, of course, micro-economic research which supports this assumption for specific types of intangibles, such as the research measuring the impact of R&D expenditures, or the relatively recent work on the impact of management consulting on Indian textile firms (Bloom et al., 2012). But the impact of aggregate intangible expenditures on future output is yet to be systematically tested, particularly in the context of developing countries.

## **4.1 Evidence from export growth patterns**

**Figure 14: Intangible investment and export growth across manufacturing industries in Brazil**

<!-- Start of picture text --> 12 13 14 15 16 17<br>Log Intangible Investment<br>Change in Revealed Comp. Advantage OLS Fitted Line<br>Intangible Investment is total intangible investment during 2000-08 in logs<br>.5<br>0<br>-.5<br>-1<br>-1.5<br><!-- End of picture text --> Note: Revealed Comparative Advantage (RCA) of an industry _i_ in year _t_ is calculated as the share of industry _i_ in Brazil‘s total exports in year _t_ divided by the share of industry _i_ in total world exports in year _t_ . This graph plots the change in RCA between 2000 and 2008 against intangible investment in 32 2-digit manufacturing industries. The fitted line is the linear prediction from an OLS regression of change in RCA on log intangible investment. The coefficient on intangible investment is statistically significant at the 10 percent level.

31
# **Social Funds**

“Social funds are multisectoral programs that provide financing (usually grants) for small-scale public investments targeted at meeting the needs of the poor and vulnerable communities, and at contributing to social capital and development at the local level. They serve as innovators and demonstrators of new methods of decentralized participatory decision-making, management, and accountability that may be adopted for broader application by public sector organizations ...”<sup>116</sup> Further information: <u>www.worldbank.org/socialfunds</u> Reading: Garnier and Imschoot 2003; Rawlings, et al. 2004; Tendler 2000

# **Sustainable Investing (SI)**

Sustainable investing (or ethical or socially responsible investing) blends for-profit investing with social or environmental criteria for investing. Typically, investment decisions are based on social responsibility ratings or on benchmarks that exclude corporate securities from investments. Sometimes, the term SRI includes investments that actively seek having a social impact (see impact investing). In the USA, assets in socially screened portfolios are estimated to have equaled $2.71 trillion in 2007, an increase over the $2.16 trillion counted in 2003.<sup>117</sup> The World Bank’s and other multilateral development bank’s bond issuances offer sustainable and socially responsible investments. World Bank debt issues are the funding source for development loans and have provided financing for more than 4,000 development projects in more than 130 countries through more than US$400 billion in lending.<sup>118</sup> The World Bank’s bond offerings that are geared towards sustainable investors include COOL bonds, Eco notes, and Green bonds. By the end of 2008, the World Bank has sold bonds that are based on the developmental purpose of the use of funds of US$ 2.146,47 million[[accuracy of number]].

Further information: <u>http://www.socialinvest.org http://treasury.worldbank.org/Services/Capital+Markets/About+Debt+Se curities/SRI.html</u> Reading: Cumming and Johan 2007; Hill, et al. 2007; Sparkes and Cowton 2004; Zakri Y. Bello 2005 See also: Blended Value Investments Impact Investing Cool bonds Eco notes Green bonds

# **Solidarity Levy on Airline Tickets**

The solidarity tax on airline tickets is an example of a financing instrument that has many elements of a global tax, but has been established as a nationally implemented, globally coordinated tax. The solidarity tax on airline tickets has been in effect in France in since mid2006, and implemented since then in Chile, Côte d’Ivoire, Congo, Republic of Korea,

> 116 www.worldbank.org/socialfunds.

> 117 Social Investment Forum 2008.

> 118 http://treasury.worldbank.org/Services/Capital+Markets/About+Debt+Securities/SRI.html.

86
growth is productivity growth, the central question is whether the Moldovan economy could reach an annual TFP growth of 3.2-3.3 percent? And, if so, what types of policy steps would the government need to take in order to bring this about? In fact, during the period 1996-2009, TFP contributed 3-4 percentage points to GDP growth (IMF 2010, p. 65), suggesting that the simulated results are within the range of what is feasible.

Table 15 shows that, when our positive shocks are combined, agriculture and services would contract, while industry would expand. These changes are accompanied by changes in the relative well-being of different household types, as we will see below.

**Table 14. Combined scenarios: Annual macro growth (%)**

|||**Finalyear**<br>|||
|---|---|---|---|---|
|**Indicator**|**2012**|**base**|**allgrw**|**allgrw-tr**|
|Absorption|99,595|3.9|6.7|6.7|
|Consumption - private|65,095|4.2|7.0|7.0|
|Consumption - government|13,307|1.5|1.5|1.5|
|Fixed investment - private|19,224|4.5|8.5|8.3|
|Fixed investment - government|1,969|4.1|7.7|7.6|
|Exports|35,280|4.4|10.8|10.6|
|Imports|63,874|3.8|9.2|9.1|
|GDP at factor cost|59,315|4.3|6.6|6.5|
|Total factor employment (index)|n.a.|2.4|3.3|3.2|
|Total factor productivity (index)|n.a.|1.9|3.3|3.3|
|Real exchange rate (index)|n.a.|0.2|-3.9|-3.8|
|Headcountpovertyrate(%)|21.4|9.5|4.3|3.7|

Note: Columns other than 2012 show results for 2020.

**Figure 21. Combined scenarios: GDP growth by year 2013-2020 (%)**

<!-- Start of picture text --> 4.5<br>4.0<br>3.5<br>3.0<br>2.5 base<br>2.0 allgrw<br>1.5 allgrw-tr<br>1.0<br>0.5<br>0.0<br>2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> 33
Table A.7: Baseline balance on key variables

||(|1)|(|2)||(3)|T-|test|
|---|---|---|---|---|---|---|---|---|
||Co|ntrol|In-|Kind|Reco|gnition|Dife|rence|
|Variable|N|Mean|N|Mean|N|Mean|(1)-(2)|(1)-(3)|
|_Student-level (grade 2)_|||||||||
|Math|6466<br>[112]|0.027<br>(0.075)|6643<br>[109]|0.078<br>(0.067)|6512<br>[115]|-0.089<br>(0.078)|-0.051|0.117|
|Language|6466<br>[112]|0.032<br>(0.069)|6643<br>[109]|0.057<br>(0.067)|6512<br>[115]|-0.013<br>(0.064)|-0.025|0.045|
|Female|6454<br>[112]|0.474<br>(0.008)|6639<br>[109]|0.477<br>(0.009)|6495<br>[115]|0.471<br>(0.008)|-0.003|0.003|
|Age|6262<br>[112]|9.411<br>(0.067)|6398<br>[109]|9.561<br>(0.092)|6299<br>[115]|9.585<br>(0.079)|-0.150**|-0.175**|
|_Student-level (grade 3)_|||||||||
|Math|6089<br>[119]|-0.004<br>(0.077)|6280<br>[120]|-0.055<br>(0.067)|6294<br>[117]|-0.115<br>(0.077)|0.051|0.112|
|Language|6089<br>[119]|0.035<br>(0.077)|6280<br>[120]|-0.007<br>(0.063)|6294<br>[117]|-0.091<br>(0.084)|0.041|0.126|
|Female|6083<br>[119]|0.473<br>(0.009)|6267<br>[120]|0.472<br>(0.009)|6290<br>[117]|0.480<br>(0.008)|0.001|-0.007|
|Age|5964<br>[118]|10.949<br>(0.071)|6143<br>[120]|11.071<br>(0.078)|6173<br>[117]|11.037<br>(0.067)|-0.122|-0.088|
|_School-level_|||||||||
|No. grade 2 students|112|57.732<br>(3.545)|109|60.945<br>(4.444)|115|56.626<br>(3.600)|-3.213|1.106|
|No. grade 3 students|119|51.168<br>(3.130)|120|52.333<br>(4.200)|117|53.795<br>(3.812)|-1.165|-2.627|
|Headteacher female|138|0.181<br>(0.033)|139|0.165<br>(0.032)|139|0.230<br>(0.036)|0.016|-0.049|
|Headteacher age|132|48.455<br>(0.782)|136|47.882<br>(0.846)|135|46.281<br>(0.913)|0.572|2.173**|

_Notes_ : The value displayed for t-tests are the differences in the means across the groups. Standard errors, in parenthesis are clustered at the school level. Observations with square brackets indicate number of clusters. Strata fixed effects included in all estimation regressions. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

8
# **Table A - 10 :  Ranking The Most Costly Procedural Requirements for**

# **Import Activities in PAFTA Countries**

|||**Respondents**|**Percent total**|**(2008 Survey)**|**(2001 Suvey)**|
|---|---|---|---|---|---|
|||**(Count)**|**respondents**|**(Rank)**|**(Rank)**|
|**- **<br>|**Road Transport & Transit / Maritime**<br>**Transport / Air Transport**|115|(57%)|1|6|
|**- **<br>|**Domestic Taxes on Imports (e.g. VAT, Sales**<br>**taxes, etc)**|114|(59%)|2|2|
|**-**<br> <br>|**Procedural Requirements (Customs**<br>**clearance, Inspection & other administrative**<br>**procedures)**|109|(49%)|3|3|
|**- **|**Tariffs on Imports**|99|(45%)|4|1|
|**- **<br>|**Technical Requirements & Compliance to**<br>**Standards**|88|(42%)|5|5|
|**- **|**Public Sector Corruption**|84|(44%)|6|4|

28
households which attrit to those who do not.

These results suggest that while attrition is more common amongst households which experience greater income mobility, the magnitude of the bias is likely to be rather small. However, a concern might be that households which experience the largest absolute changes in income move houses and attrit out of the survey before the next quarter’s survey can be completed. Since these income movements are by assumption unobserved, we can not directly examine them. Instead, in Table 4 we examine how much our pseudo-panel estimates of absolute and conditional mobility differ when we consider only households which don’t attrit. We classify households according to whether they participate in all five quarters of the ENEU survey or not, and restrict our analysis to the cohort-quarter observations where we have at least 100 observations per cohort in each group. Column 1 repeats the quarterly pseudo-panel estimate of β in the absolute mobility regression for the full sample. Column 2 creates a pseudo-panel of non-attritors by taking the first wave observations for households which are observed in all five waves. Column 3 creates a pseudo-panel of attritors, by taking the first wave observations for households which are not observed for at least one of the four remaining waves. The estimate of β for the non-attritors pseudo-panel of 0.987 is very close in magnitude and not statistically different from the 0.991 coefficient for the full sample. The attritors pseudo-panel estimate of 0.977 suggests slightly greater absolute mobility among the attriting households<sup>11</sup> , but one can not reject equality of the coefficients in the non-attritors and attritors samples. These results therefore suggest that there is very little bias from attrition in estimating mobility with a balanced panel.

Columns 4 through 8 examine conditional mobility of the non-attriting and

> 11 Note that households in this pseudo-panel are by definition households that would attrit in the next 4 quarters whenever you sample them. This is a subset of the group of households which happen to attrit in an observed five-quarter period. They are thus households which are likely to have even greater geographic and income mobility than the average attriting household.

21
decreased significantly from 5.6 years in 1995 to 4.5 in 2002.<sup>5</sup> There has been a slight reversion of this trend between 2000 and 2002, but not enough to offset the losses observed during the second half of the 1990s. Emigration to the United States, Canada and the United Kingdom is likely to have played an important role in this decline in the average education level of the labor force, since more schooled and skilled workers the ones more likely to emigrate.<sup>6</sup> Increasing returns to education are also not likely to be behind the growth in real wages. The rate of return of formal education has been high in Jamaica, leaving very little room to increase further. I rely on pooled regression to investigate the return of education on wage earnings using the LFS data from 1995 to 2002. Table 1 reports the result of the pooled Mincer regression and the regressions by gender.<sup>7</sup> The coefficient estimates for Years of Education are positive and statistically significant for all three regressions and the returns to education are bigger for women. The returns to education in Jamaica are substantially high in the international standard. The average returns to education in Jamaica (11.7%) are among the countries with highest returns to education in LAC and returns are higher than most OECD countries.<sup>8</sup>

**-** **<u>Table 1 Log Real Wage Mincer Regression (1995 2002)</u>**

||Total|Female|Male|
|---|---|---|---|
|Years of Education|0.117|0.143|0.100|
||(29.86)**|(24.34)**|(19.53)**|
|Years of Potential Experience|0.009|0.008|0.011|
||(13.38)**|(7.32)**|(12.63)**|
|Urban|0.161|0.166|0.186|
||(9.43)**|(6.84)**|(8.03)**|
|Constant|4.388|4.037|4.616|
||(101.69)**|(59.91)**|(84.46)**|
|Observations|9614|4634|4980|
|R-squared|0.11|0.14|0.10|

Note: Absolute value of t-statistics in parentheses * significant at 5% level; ** significant at 1% level While returns to education appear to have been increasing in most countries in the LAC region, the same is not happening in Jamaica. While returns to formal schooling

> 5 The average years of education of all 20 years olds who are in the labor market in each year. World bank(2006c)

> 6 Mishra (2006)

> 7 The time trend and location effects are controlled out.

> 8 See World Bank (2006c) for comparison.

5
Table 1: Sample sizes and average test scores

||Spanish|Num|ber of ca|ses|
|---|---|---|---|---|
||test score|Students|Classes|Schools|
|All|61.6|574,322|||
|Sample with no missing|62.9|467,304|26,077|13,795|
|Schools with _>_ 1 class|63.1|389,516|19,662|7,439|

Source: ONE 2000, Argentina Table 2: Students’ characteristics, according to own family background.

|||Own|backgrou|nd (no. bo|oks)|
|---|---|---|---|---|---|
||Total|Lower|Lower<br>Middle|Upper<br>Middle|Upper|
|||(0-10)|(11-50)|(51-100)|(_>_100)|
|No. students|399,378|128,321|156,673|60,557|53,827|
||100.0|32.1|39.2|15.2|13.5|
|Spanish test score|63.0|55.9|64.2|69.5|69.3|
|Spanish test score (sd)|19.1|18.2|18.3|18.1|19.0|
|Maths test scores|59.4|51.9|60.5|65.9|66.1|
|Parents education (yrs)|10.8|8.8|10.6|12.4|13.5|
|Assets index (prices)|26.4|20.6|27.2|31.1|32.3|
|Grade repeated|0.17|0.28|0.14|0.08|0.09|
|Private school|0.23|0.09|0.24|0.37|0.41|
|Class size|26.8|25.7|27.0|27.6|27.6|
|Age|11.6|11.7|11.5|11.4|11.4|

Source: ONE 2000, Argentina a third of the students belong to the lower group and another third to the lower-middle group.

The proxy chosen for family background performs relatively well; on average, parental education and assets increase with the number of books held at home. As expected, wealthier students perform on average better in both Spanish and Math, are less likely to repeat any grade and the attend private institutions significantly more. The gap in test scores between the lower group and the upper group is 14 points, statistically different from zero and close to one standard deviation in test scores observed in the overall distribution.

Students tend to share their classes with disproportionately more peers 8