rafmacalaba/datause-encoder-data huggingface dataset:
the five-year averages in Barro and Lee (2000). There are several countries included in the sample that are not included in the Barro and Lee dataset. We matched each of these countries with other countries included in Barro and Lee, using real GDP per capita and government expenditure as a share of GDP per capita.

Observations for a typical country consist of five five-year periods. With 50 developing countries and no missing observations, that would give a sample size _n_ = 250. However, we do have some missing observations (with _n_ = 230) for production and trade data, and the sample is unbalanced.

# 4. Empirical Findings

Given that changes in openness, foreign R&D and education are unlikely to have an immediate impact on productivity growth, we specify the estimated equations in terms of five-year changes in the log of _TFP_ , the log of _NRD_ , and in _YE_ , where the letter “D” before the variable indicates a five-year change. In other words, the estimated equations are specified in terms of the growth rate of _TFP_ and _NRD_ , and in terms of the change in _YE_ . We estimate nine equations, all variants of equation (3) above. The results are presented in Table 3.

Table 3 shows that the coefficient β _N_ of Dlog _NRD_ is positive and significant in all nine regressions. Denote the coefficient β _N_ for small states by β _NS_ (equation (3)). The value of β _N_ ranges from .269 to .615, and falls to a range of .269 to .397 when the variable Dlog _NRD*S_ 3 is included in the regression. For instance, in equation (1), β _N_ = .490 (significant at the 1% level). It falls to .269 (significant at the 10% level) in equation (2). On the other hand, β _NS_ = .964 (significant at the 1% level) in the same equation. The 10
**Table 2:** Female labor force participation and Gini index (fixed effects panel analysis, w/o high income countries).

|Dep. var.: Gini index (1=100)|||||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Female LFP (% of female population ages 15+)|0.317**<br>[0.145]|0.586***<br>[0.178]|0.747**<br>[0.328]|0.652**<br>[0.291]|
|Female LFP squared|-0.003**<br>[0.001]|-0.005***<br>[0.002]|-0.008***<br>[0.003]|-0.008***<br>[0.002]|
|Services value added (% of GDP)||0.212***<br>[0.077]|0.188***<br>[0.064]|0.197***<br>[0.055]|
|Exports of goods and services (% of GDP)||0.078*<br>[0.043]|0.133**<br>[0.062]|0.133**<br>[0.058]|
|Imports of goods and services (% of GDP)||-0.031<br>[0.026]|-0.038<br>[0.061]|-0.076<br>[0.061]|
|Foreign direct investments (% of GDP)||0.018<br>[0.019]|0.032<br>[0.026]|0.078*<br>[0.040]|
|Age dependency ratio (% workforce over total<br>population)||0.168**<br>[0.072]|0.209**<br>[0.103]|0.225**<br>[0.094]|
|Labor Force with secondary education (% of total)|||0.059<br>[0.060]|0.016<br>[0.046]|
|Government expenditure (% of GDP)|||0.029<br>[0.072]|-0.074<br>[0.087]|
|International migrant stock (% of population)||||0.620<br>[0.716]|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Year dummies|Yes (2002-<br>2018)|Yes (2002-<br>2018)|Yes (2002-<br>2018)|Yes (2002-<br>2018)|
|Observations|533|512|247|187|
|Within R-squared|0.345|0.444|0.561|0.606|
|Between R-squared|0.088|0.224|0.322|0.120|
|Overall R-squared|0.096|0.280|0.352|0.179|

_Notes:_ Robust standard errors in brackets;<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

_Source:_ Authors’ own elaboration using World Bank’s data.

27
The last step consists of estimating market income using the individual net income that was calculated above, YijD̃ + αijπj . Specifically, net income is a function of market income, direct taxes and social insurance contributions and is estimated as follows for each type of worker: _For public sector employees:_ where YijM is the labor income component of market income, s is the social security contribution rate (NPPF in the case of public workers), GIS is the payment to the Group Savings and Insurance Scheme, and TDS(. ) is a function of tax deducted at source<sup>19</sup> which depends on gross income, social security contributions, GIS payments, and the tax rate.

_For formal private sector employees:_ where s is the social security contribution rate (provident fund in the private workers’ case), GSLI is the Group Savings Link Insurance rate, and TDS(. ) is the tax deducted at source.

_For formal self-employed workers in the farming sector:_ where RI is the payment to the rural insurance scheme.

_For formal self-employed workers in the non-farming sector:_ _For all informal workers (wage workers or self-employed workers):_ Self-employed workers are assumed to pay income taxes but not social security. We assume that informal workers do not pay any income tax or contributions as they are not covered by labor regulations. D̃ Importantly, equations (7)-(11) are defined in terms of the observed variable (Yij + αijπj), the policy

> 19 According to the guidelines described in the Rules of Income Tax 2017 and the Tax Deduction at Source, all income from salary income, rental income, dividend income, interest income and income from other sources is liable to pay the tax deduction at source (TDS) which is a provisional tax to the annual personal income tax. The taxable base is the prefiscal income after deducting social security contributions, and the provisional tax amount is calculated using the TDS contribution table. The last section shows how TDS is implemented in the construction of the prefiscal income.

13
# **2. Validation and Robustness Check**

Figure 30. External Validation, NUTS1 Level $5/day PPP– Observed (SILC) & Imputed (LFS), 2007 <!-- Start of picture text --> LFS<br>SILC<br>Source : LFS, SILC, imputed OLS, regionally deflated, authors’’ calculations<br><!-- End of picture text --> 61
The results in columns (1) to (5) suggest that, even though statistically significant, Open Skies Agreements imply only a small decline in air transport costs of around 2.3%. This is the average effect of open skies agreement on freight rates independent of the number of years the agreement has been standing. Open Skies Agreements may reduce freight rates over time, in which case the total effect of these agreements would be larger than 2.3%. We want to test the hypothesis that air carriers take time to adapt to the new rules in the market and need to go through an underlying “learning by doing” process. A similar hypothesis, which cannot be tested separately with the available data, would be that those firms that survive after Open Skies Agreements are signed, are more efficient and, given the existence of more contestable markets, they set lower tariffs. The process of fighting to survive is not a one-period game and that would explain why freight rates decrease over time. To test the first hypothesis, columns (6) to (8) compute the previous regressions using only the years 1990 and 2001.<sup>39</sup> Using this “type of” first difference regression,<sup>40</sup> the dummy for Open Sky Agreements captures the fall in freight rates for the whole period since the agreement was signed in each country. For example, Spain signed an Open Skies Agreements with the US in 1995, therefore, in the year 2001 the agreement had been standing for 6 years. In column (6) the agreement dummy implies a large drop in freight rates of around 9 percent. When we include our infrastructure measure in column (7), the fall in freight rates increases to 13 percent. In this specification our infrastructure variable is significant at 10 percent levels and it has a magnitude similar to that obtained in column (2) of Table 1. Finally, to confirm that the effect of agreements increases over time, in columns (8) and (9), besides our Open Sky dummy, we include the number of years since the agreement was signed. Reassuring our previous results, the interaction term is negative and significant at conventional levels. These results, even though they are significant at conventional levels, are estimated imprecisely.

To check the results of the previous paragraph, Table 4 presents country-product fixed-effects regressions for the whole sample period (as columns 1-5 in Table 3) but allowing the effect of Open Skies Agreements to differ over time. The dummy “Year of

> 39  We drop countries that are not in the whole sample to avoid any composition effect (2 countries). Results hold with the whole sample.

18
behavior may also be due to the social marketing that accompanies such programs (World Bank, 2011).

CCTs go beyond labeling or social marketing to the degree that they strictly enforce compliance with conditions. Schady and Araujo (2008) report on a randomized transfer in Ecuador in which beneficiaries were told there was a schooling requirement but the condition was not strictly enforced. Those that believed there were co-responsibilities had higher enrollments than the other cash recipients. De Brauw and Hoddinott (2011) also investigated the additional impact of conditions over the transfer by looking at a subset of beneficiaries of Progresa that did not receive the forms necessary for monitoring program compliance. This group had lower enrollments than other CCT beneficiaries.

Both these studies verify that the differential impacts are not due to self or community selection. Moreover, their conclusions are bolstered by two recent randomized trials designed to test the impact of conditions over a similar unconditioned transfer. In a study of young women in Malawi Baird, McIntosh and Ozler (2011) found that both UCTs and CCTs reduced school dropout after two years but the reduction among UCT recipients was less than half of the reduced drop out when the students received CCTs. Akresh, de Walque, and Kazianga (2012b) also found that conditional transfers had a different impact on schooling than unconditioned transfers in a randomized trial in Burkina Faso. However, there results were nuanced in that there were no appreciable differences among core students, that is, among most primary school age children as among well higher ability children. In contrast, CCTs were more effective than UCTs in improving the enrollment of younger children and girls as well as children with lower ability. CCTs also performed better than UCTs in improving attendance and achievement scores.

7
**Figure 3. Regional organization of the automotive supply base**

<!-- Start of picture text --> Number of firms (in top 100) Share of sales in home region<br>1999 2001 2003 2005 1999 2001 2003 2005<br>year year<br>North America Europe Asia North America Europe Asia<br>50<br>0.70<br>40<br>0.65<br>30<br>0.60<br>20<br>0.55<br>10<br><!-- End of picture text --> Source: _Automotive News_ top supplier list (various years) However, Figure 3 illustrates that this situation changed in the following years, as global sourcing by lead firms increased extra-regional sales by Tier 1 suppliers. First, global sourcing has caused the list of top suppliers to become more regionally balanced. The number of top suppliers coming from each of the three regions, in the left panel, now reflects the worldwide production share of the lead firms of the respective regions. Second, by 2005 average sales in home regions declined from 68.6% to 61.6% for the 100 largest suppliers, which are shown broken down by region in the right panel. The decline has been particularly pronounced for North American based suppliers. Asian headquartered suppliers’ increased extra-regional sales modestly and European suppliers’ extra-regional sales remained flat during the same period. Nevertheless, examples abound of suppliers from Germany and Japan following their largest customers offshore as extra-regional final assembly has grown.

# **4. The long history of government intervention in the automotive industry**

As we have argued, the automotive industry has long been deeply affected by protectionist measures and other forms of government intervention. Technical necessity, political sensitivities, and market variation have kept final vehicle assembly, and by extension much of parts production, close to end markets. Powerful lead firms and industry associations, largescale employment and relatively high rates of unionization, and the iconic status of motor 10
_68_ _E8atOim 4nd Pwjctim of MOdali by QUe_

# Annex C. Expert Assessments

This annex provides expert assessments of mortality in 1985 from some specific causes by age, sex, and region and explains how they were obtained. The basic procedure was to show the regression estimates based on WHO data and regression models to individual experts on specific diseases, with a request for corrections and supporting evidence, and to calculate alternative rates and numbers of deaths on the basis of plausible recommendations.

Those specific causes of death on which alternative assessments were made are discussed in the same order as in preceding tables. Comments were also received on neoplasms and injury and poison, but no revisions were suggested. For specific causes not covered, no comments were received.

Those causes of death left out are not necessarily less controversial. For instance, West and Sommer (1987:4) estimate 420,000 deaths annually among preschool children from vitamin A deficiency, but the regression estimates for deaths under five due to micronutrient disorders are only 58,000. More generally, a comparison in Table Cl with figures compiled by Walsh (1988:15) shows that some estimates in the literature are considerably higher than the regression estimates here. The comparisons in this table cannot be examined in detail because information on Walsh's procedures is not available. The comparisons do suggest that the regression estimates substantially understate deaths from malaria and schistosomiasis, and these discrepancies are among those examined below.

Table Cl. Comparison of esdmated deaths from certain specific causes in developing countries around 1985 with Walsh assessment

||||_Ratio_<br>_Walshl_|_:_<br> <br>_Pe_|_rcent_|
|---|---|---|---|---|---|
|_Dise_|_iRegrv_<br>_sion_|_-_<br>_Walsh_|_Regres-_<br>_sion_|_Regres-_<br>_sion_|_Walsh_|
|Diarrhea|2978|4300|144|30.6|21.9|
|Tuberculosis|804|900|112|8.3|4.6|
|Acute respiratory infection|5127|10000|195|52.6|51.0|
|Measles|420|2000|476|4.3|102|
|Polio|25|2|8|0.3|0.0|
|Yellow fever, dengue, encephalitis|<br>8|31|388|0.1|**0.2**|
|<br>Malaria|<br>146|1500|1027|<br>1.5|7.6|
|Schistosomnasis, filarlasis|12|375|3125|0.1|1.9|
|Pregnancy|218|500|229|2.2|2.5|
|<br>Malnutrition, micronutrdent|556|2000|360|a|a|

Sources: Estimates reported above and Walsh (1988:15).

'Walsh lists malnutrition as an associated cause, with deaths presumed to be also counted elsewhere. Therefore, this cause is not included in the calculation of percentages. These percentages are over causes included In the table only, not over all causes.
year span rather than the two quarter panels the ENH offers. This limitation may not be as serious as appears at first for measuring the impact on wages since higher inflation rates erode more quickly any mandated increase in the MW than in the US. It has also been argued (Brown et al. 1982) that the high turnover in low skilled workers may imply that employment adjustment in the most critical ranges around the minimum wage may be relatively rapid. Further, Maloney (1998) shows that average manufacturing tenure in Latin America is roughly 70% of the OECD so the quantity adjustment might occur more rapidly. Nonetheless, we put less weight in our analysis on the lags and do not follow Neumark in generating "representative" worker responses to lagged minimum wages.

# _Effects on wages_

Table 3a reports the effects on real wages of a change in the real minimum wage. on salaried workers. The results are broadly consistent with Neumark's. Around the minimum wage, the effect is high for salaried workers, .87 for those earning .7-.9 minimum wages. Moving up the income scale, the effect remains significant up to 4 minimum wages, although with decreasing coefficients reaching .16 for those earning more than 4 minimum wages. What is remarkable is that the effect dies off much more slowly than in the US. Between 2-3 minimum wages, Neumark found an impact of only .06 % whereas Colombia shows .38 up to 4 minimum wages. This suggests a far greater numeraire effect and hence substantially greater impact of, the minimum wage. As

> '" Neumark argues that generally speaking, the lag has the usual interpretation as long as long as the individual history is not relevant, that is, that the contemporary effect of a change in the MW does not depend on past wages. The problem that he highlights is that a rise in the minimum wage in the previous period may have "swept up" a worker into a different category. Therefore, the correct total effects (contemporaneous plus lagged) needs somehow to compensate for individuals changing classification that involves generating a set of representative workers in each cell.

14
# **1. Introduction**

Youth unemployment is a global problem, and is particularly high among educated youth in the Middle East and North Africa (World Bank, 2014). Youth often face a catch-22 whereby firms are reluctant to hire them without job experience unless they have the right connections, but without being hired they cannot get this experience. The returns to this experience are believed to be high because initial jobs provide on-the-job training for youth in both job-specific skills, as well as in soft skills. But since many young people may leave a firm once they have received this experience, there is a concern that firms underprovide job experience from a public policy perspective. Firms may be particularly reluctant to invest in training workers in fragile and conflict-affected states, where political and economic uncertainty makes many types of investments additionally risky (Blattman and Ralston, 2015).

This paper reports on a randomized experiment testing an internship program in the Republic of Yemen that was intended to help overcome these constraints and provide employment experience to educated youth. The program provided firms with a 50 percent subsidy towards the cost of employing an intern for six months, and a matchmaking service to link firms seeking intern positions to graduates looking for employment opportunities. The first year of this program took place in 2014, with a second year of the program canceled due to civil conflict breaking out in the country in 2015.

Evaluating the first year, we have four main findings. First, there appears to be a mismatch between the supply of skills of interns (heavily concentrated in STEM fields like engineering and IT) and the demand for these skills from firms (which required relatively more marketing and business graduates). Second, the internship program did generate new job experience, rather than just substituting for the employment applicants would have received any way. We find recipients of the internship had a 42 percentage point increase in the likelihood of working during the internship period, a 3.4 month increase in the amount worked, and a 73 percent increase in earnings during 2015. Third, we were able to field a short-term post-treatment follow-up survey just as conflict was starting, providing evidence on impacts up to five months after the internship. The results 2
strategy. Building on successful products and targeting markets that have been successfully reached or are reachable by virtue of implementable policy measures should be a guiding principle. Figure 5 presents the expansion potential of some products in four regional markets where Tajik products have had some success: the CIS, the EU, the Middle East and North Africa and East Asia.

**Figure 5. Tajikistan Export Potential in Four Markets**

(Bubbles represent Tajikistan export value of specific products) <!-- Start of picture text --> 45<br>45<br>40<br>40<br>Aluminum wire<br>35 35 Aluminum wire<br>30<br>30<br>25<br>Other parts and 25<br>20 accesoires of Other parts and<br>motor vehicles 20 accessories of<br>15 motor vehicles<br>15<br>10 Apricots,<br>5 cherries, peaches Aluminum, not 10 Onions and<br>alloyed 5 shallots<br>0 Aluminum<br>-10 -5 0 10 20 30 40 0 alloys<br>-1.5 -1 -0.5 -5 0 0.5 1 1.5 2 2.5<br>-10<br>Import growth rate in CIS, 2005-10 -10<br>EU import growth rate, 2005-10<br>45 45<br>40 40<br>35 Aluminum wire 35<br>Aluminum wire<br>30 30<br>25 25 Other parts and<br>Other parts and accesories of<br>20 accessories of 20 motor vehicles<br>motor vehicles<br>15 15<br>Apricots,<br>105 Apricots, cherries Onions and shallots Aluminum waste and scrap Copper waste Aluminum, not 105 Dried fruits Onions and shallots cherries and peaches<br>peaches and scrap alloyed Aluminum<br>0 0 waste and scrap<br>-0.5 -5 0Aluminum, not 0.5 1 1.5 2 2.5 3 -1 -0.5 -5 0 0.5 1 1.5 2<br>alloyed<br>-10 East Asia import growth rate, 2005-10 -10<br>MNA import growth rates, 2005-10<br>Export growth rate in Tajikistan, 2005-10<br>Tajikistan export growth rate, 2005-10<br>Tajikistan export growth rate, 2005-10<br>Tajikistan export growth rate, 2005-10<br><!-- End of picture text --> Source: COMTRADE data and author’s calculations.

Three types of products can be derived from Figure 5: products experiencing a limited supply-demand growth which Tajikistan is exporting a lot (big bubbles in the bottom left corner, e.g. aluminum alloys or aluminum not alloyed); products experiencing a moderate supply-demand growth which Tajikistan is exporting moderately, e.g. dried fruits, onions or apricots; and products with a strong supply-demand growth which Tajikistan is exporting too little (small bubbles in the top right , e.g. aluminum wire).

The first type of products does not need a particular action from the government to sustain its expansion: traditional supply chains can keep exports high as long as commodities importers are 7
BOX 1.5 TEXT REFERENCE FOR THIS BOX How widespread are Internet access and online payments?

Access to the Internet varies widely around the world. While 83 percent of households in high-income OECD economies have access to the Internet within their home, only a third of households in developing economies do (figure B1.5.1). Among developing regions the share of households with Internet access ranges from around 50 percent in East Asia and the Pacific and Europe and Central Asia to less than 10 percent in South Asia.

So it is no surprise that the largest share of adults making payments online—whether paying a bill or making a purchase—can be found in high-income OECD economies: on average in 2014, 54 percent of adults in these economies reported having made a payment online in the past 12 months. Online payments are most common in Scandinavian countries, where more than 75 percent of adults reported having made such a payment in the past 12 months. By contrast, in developing economies less than 10 percent of adults reported having done so. But there are exceptions to this overall pattern: in China, Malaysia, and Turkey almost 20 percent of adults reported making online payments.

FIGURE B1.5.1 Internet access and use for payments Adults reporting household access to Internet and online payments (%), 2014 <!-- Start of picture text --> Internet access Online<br>payments<br>East Asia<br>& Pacific<br>Europe &<br>Central Asia<br>High-income<br>OECD economies<br>Latin America<br>& Caribbean<br>Middle East<br>South Asia<br>Sub-Saharan<br>Africa<br>0 20 40 60 80 100<br><!-- End of picture text --> _Note:_ Data on online payments refer to the share of adults who reported using the Internet to pay bills or make purchases in the past 12 months. _Source:_ Global Findex database.

> THE GLOBAL FINDEX DATABASE **27**