rafmacalaba/datause-encoder-data huggingface dataset:
Under this definition also, the textile and apparel industries are integrated to GVCs across all external comparators. The set of countries in the region that are in the same position is also unchanged, as textile and apparel manufacturers are integrated to GVCs in Kenya and Uganda among the non-resource rich group in the region, in Ghana, South Africa, and Zambia among the non-oil resource rich group, though none of the countries in the oil exporting group.

The use of the alternative definition significantly alters our assessment of the degree of integration of specific countries to other categories of manufacturing GVCs. Thus, Indonesia and Vietnam are assessed to be significant players in the manufacturing GVCs in transport equipment among the external comparators as are Malawi and Côte d’Ivoire among non-resource rich countries of the region, and the Democratic Republic of Congo, Ghana, and Zambia among the non-oil resource rich group. The group of significant players along GVCs of electrical goods and machinery also changes with only Bangladesh among the external comparators, only Côte d’Ivoire among the non-resource rich group of countries of the region, with South Africa and Zambia among the non-oil resource rich group and none among oil exporters.

Three of the four external comparators, namely, Bangladesh, Cambodia, and Vietnam are all integrated to manufacturing GVCs in metal products under the alternative definition along with Kenya among non-resource rich countries of the region, South Africa and Zambia among the non-oil resource rich group, and none among the oil exporting group. The corresponding lineup of participants in GVCs in the manufacturing of chemicals and non-metal mineral products now includes only Indonesia among the external comparators; Malawi and Kenya, among the nonresource rich group of countries in the region; the Democratic Republic of Congo, South Africa, and Zambia, among the non-oil resource rich group, and Cameroon among the oil exporting group.

# **7. Conclusion**

Based on the EORA database (UNCTAD 2013), this paper shows that African economies have reasonably high rates of linkages compared with those of China, the rest of East Asia, Germany, India, the United States, and the rest of Western Europe across broad categories of 21
about convergence in incomes per capita and are a key driver for the reduction of the betweencountry component of global inequality. The increasing of the working age population shares in many developing countries, vis-à-vis its reduction in high income countries also contributes towards this convergence (Cruz and Ahmed, 2016).

Figure 5: Projected economic growth by geographical region <!-- Start of picture text --> Annual average growth rates of income per capita (2012-2030)<br>8.7<br>9.0<br>8.0<br>7.0<br>6.0<br>5.0 4.4<br>4.0<br>3.3<br>3.1<br>3.0 2.6 2.7 2.5<br>2.0<br>1.0<br>0.0<br>East Asia & Europe & Latin America Middle East & South Asia Sub-Saharan High Income<br>Pacific Central Asia & Caribbean North Africa Africa<br>Percent<br><!-- End of picture text --> Source: CGE and GIDD simulations results.

Is the education wave contributing to the reduction of global inequality from the perspective of this between-country convergence? The answer is yes, but it does not seem much in the aggregate as shown in Figure 6. The contribution of the education wave – vis-à-vis a scenario where skilled and unskilled workers grow at the same rate – comes exclusively from the higher productivity of skilled workers who are becoming more abundant in the wave scenario. However, other drivers of growth and convergence, namely the catching up of productivity and supplies of other factors (land and capital), do not change across simulations. For these reasons, the education wave boosts convergence by, at best, about one percentage point in East Asia and the Pacific and Latin America and the Caribbean regions, but its impact is more limited in other regions. The convergence differentials should be considered a lower bound, given that more abundant skilled workers are likely to generate externalities and an additional productivity uptick that is not simulated here. In addition, we have very conservative education projections and we a looking at a relatively short period of time to fully capture the effects of the education wave.

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# **ACRONYMS**

ADB Asian Development Bank DIT Dili Institute of Technology EP Equal Probability FLFP Female Labor Force Participation GDP Gross Domestic Product ILO International Labor Organization L & S Labor and Solidarity LM Labor Markets MoECYS Ministry of Education, Culture, Youth, and Sports MW Minimum Wage SLTA Sekolah Lanjutan Tingkat Atas (Senior High School) STM Sekolah Teknik Menengah (Technical School) TLSS Timor Living Standards Survey TVET Technical and Vocational Training UES Urban Enterprises Survey UNMISET United Nations Mission of Support in East Timor UNTAET UN Transitional Administration in East Timor This report was prepared by Maitreyi Bordia Das and Philip O’Keefe, with inputs from Rui Manual Hanjan. Annex 1 was prepared by Jakob Wichmann. It is primarily based primarily on analysis of a survey conducted by Dili Institute of Technology under leadership of Joao Cancio Freitas, with support from Jakob Wichmann and Rui Manuel Hanjan. The team is grateful to the then Secretariat of Labor and Solidarity (now Ministry for Labor and Community Reinsertion) for its support in survey design and implementation, and to members of the Labor Board who provided suggestions. It is also grateful to Elisabeth Huybens for guidance and support and to Jan Rutkowski, Adrian Fozzard, Alan Abrahart, and Johanna Stenstrom Johansson for comments on an earlier draft. The team also acknowledges the financial support from Government of Denmark which financed the survey on which much of the analysis is based. The survey instruments are available in English and Bahasa from the World Bank Office in Dili, TimorLeste.

2
have been implemented in a number of developed countries around the world, including Italy, Germany, Netherlands, USA, and they have been shown to be strong predictors of improved financial outcomes. They have likewise been used in many emerging markets, such as Indonesia, India, Sri Lanka, and Mexico.

Despite the ubiquity of the “Big Three” questions, they are not necessarily comprehensive nor appropriate in many settings. Indeed, Lusardi and Mitchell (2009) themselves write that it is “imperative to expand the range of measures of financial literacy, so as to better evaluate the types of problems that people find most difficult” (p. 6). Developing countries in particular possess many characteristics — among others, high poverty, low access to finance, and lack of consumer finance protections — that are necessary to consider when measuring financial knowledge (Holzmann, 2010). For instance, if most households are uneducated and hold informal savings, it may be more important to evaluate understanding of bank account opening requirements than savings returns computations. Thus, the notion of financial literacy should be extended, especially in the developing country context.

To this end, we propose a broader approach to assessing financial literacy. Our conceptualization of financial knowledge focuses not on specific topics as the “Big Three” does, but rather on three general and distinct _dimensions_ . The first dimension we measure is financial _numeracy_ , which concerns the ability to add income and expenses, determine interest rates, and similar calculations. In particular, as part of our endline survey, we asked respondents the following two questions.

- (1) Let’s assume that you deposited Rs. 10,000 in a bank account at an 8% monthly interest rate.  How much money will you have in your account in one year if you do not withdraw from or add to this account any money? (a) More than 10,800; (b) Less than 10,800; (c) Exactly 10,800.

- (2) Suppose you had Rs. 50 to save. You could either save this for 1 month in an account which earns 14% interest per month or save it for 1 month in an account that earns 2% interest per week. Which would you choose? (a) 14% per month; (b) 2% per week.

Such quantitative skills play an important role not only in selecting optimal financial products, but likewise in navigating one’s day-to-day personal financial situation. As evidenced in the above two questions, financial _numeracy_ encompasses whether the respondent can reason with numbers and whether they can use elementary arithmetic to solve financial problems.

> would have in the account if you left the money to grow: more than $102, exactly $102, or less than $102?, (2) Imagine that the interest rate on your savings account was 1 percent per year and inflation was 2 percent per year. After 1 year, would you be able to buy more than, exactly the same as, or less than today with the money in this account?, (3) Do you think that the following statement is true or false? “Buying a single company stock usually provides a safer return than a stock mutual fund.”

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of private plot produce to relying on self-valuation may explain partially why this result was obtained.

**Table 10: Poverty and Ownership of a Private Plot**

|_Whether the household has a_<br>_private plot_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
|No plot|26.6|.|18.5|34.4|48.2|38.1|
|Has plot|25.6|...|26.5|27.7|39.1|43.5|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||**Poverty G**|**ap (in pe**|**rcent)_**|**1/**|||
|No plot|21.6|...|13.6|21.4|28.1|29.1|
|Hasplot|17.2|...|13.2|19.2|22.2|31.8|
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Motes:_ I/ The povertv gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty line (this measure is also known as the expenditure gap ratio).

Education of the household head has a strong influence on the household's poverty status, with the lowest headcounts achieved by those with university education (Table 6). In all three countries, households headed by those with primary education or less than complete primary education had poverty rates higher than average, and higher than those with secondary education. The depth of poverty paralleled the poverty rates, with the highest poverty gaps for those with primary or less education, followed by secondary and vocational-technical education. The poverty gaps for those poor with higher education **were** below **average.** 53
(4) _ITi,j,t_ = _β_ 0 + _β_ 1 _Competj,t_ + _γxi,t_ + _αi_ + _uit_ Where _ITi,j,t_ =IT use of firm _i_ , from sector _j_ , at time _t xi,t_ =Vector of control variables such as age, firm’s size and share of skills _αi_ =firm fixed effects As Table 2 shows, there is indeed a strong correlation between IT use and Chinese competition as the coefficients for our main two IT-use variables (computers-per-worker and share of labor with Internet) are positive and statistically significant at the 1% level in most of the specifications. In the case of the share of labor with computers, the results are slightly lower than the ones for computers-per-worker.

# **5.2 IT use and productivity**

First of all we estimate the relation between IT use and productivity using OLS in order to analyze if there is indeed a relation between these two variables without any further consideration regarding endogeneity or the relation with China’s competition. As Table 3 shows, for all IT use variables we observe that there appears to be a positive and significant correlation between IT use and productivity measured by sales-per-worker. These results are indeed consistent with what previous studies have found for developed countries.

As mentioned before, IT use is probably endogenous because there are likely to be unobservable characteristics that are both positively correlated with IT and with productivity. Therefore, we adopt an IV strategy to estimate the same equations, where we instrument our IT use variable with the instruments described in section 3.2. Results are shown in Table 4. Overidentification tests as well as the F-test, evaluating the strength of our IV, do not indicate invalidation of our instruments (See Table 5 for the firststage results). As the table shows, results are in line with the OLS coefficients, but slightly higher which may be the consequence of treatment effect heterogeneity (Imbens and Angrist, 1994).

In order to analyze what previous literature has found regarding the complementarity between IT use and skills, we estimated the equation interacting IT use with the share of white-collar workers in the firm. In all specifications the interaction was not significant.<sup>8</sup> Thus, we don’t find much evidence that firms with a higher share of white-collar workers experience greater effects of IT on productivity as a result of the complementarity between skills and IT. However, our results may be driven by the fact that the proxy we

> 8Results are not shown here but are available upon request.

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**Figure 6:  Phone Faults (reported faults/100 mainlines)**

<!-- Start of picture text --> 60<br>57<br>48<br>40<br>37<br>25<br>20 18<br>11<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

# **Transport**

**Overall assessment.** The transport sector’s overall performance indicators show South Africa lagging behind its comparator country groups and several other regions. This finding must be taken with caution though, reflecting at least two factors at play. First, it may simply reflect a more idiosyncratic territorial distribution of population and economic activity in a large, coastal, resource rich country such as South Africa than is the case for the average country group. Second, it may reflect some peculiarities in the type of road network that is appropriate for a country with semi-arid climate, and with a large proportion of its land surface carrying low population densities. However, it also reflects the legacy of the Apartheid era and wide inequalities in infrastructure service delivery between the privileged group and the majority of the population.  Perceptions of the quality of service delivery are relatively favorable (Table 4 and Figures 7-8).

**_Access. Road_** _density in terms of population (road-kilometers per 1,000 people)_ is twice the average for low-income countries and higher than Middle East & North Africa, and East Asia Pacific. However, at 6.1 road-km per 1000 people it is only a third of the density of high-income OECD countries and two thirds of upper-middleincome countries. Interestingly, Brazil has significantly higher road density in terms of population (10), while Indonesia and Malaysia score lower (1.7 and 2.8).  Road _density in terms of land (road-kilometers per 1,000 sq km)_ is low (227) when compared with many other groups and is only higher than that of SSA (155) and low-income countries (181). It is much lower than in benchmark upper-middle-income countries (328).  At the same time, it is important to recognize that these road density indicators for South Africa may be somewhat misleading in comparative perspective because of the specific territorial configuration of population density that is concentrated in comparatively small parts of an otherwise large national territory. To the extent that South Africa has lower than average population density and greater differences in the concentration of population and economic activity (parts of the country are very poorly inhabited with

24
Reksten and Grown 2017). One drawback of this approach is that it is data intensive. It requires detailed gender disaggregated data, which may not be available in many developing countries. Another problem is that proposing gender-related interventions within each growth constraining area poses the risk that none of the gender related actions will actually be addressed with policy interventions. In developing countries, where policy makers are often preoccupied with immediate macroeconomic concerns, growth concerns usually trump those related to female economic empowerment. Finally, many of the gender-related interventions may not be seen to deliver benefits on a scale that matters at the national level. The result is that there are often two parallel dialogues – one around economic growth and its constraints and another around women’s economic empowerment.

The approach we propose merges the two dialogues for the benefit of both. Once the areas of national priority are identified through growth diagnostics a la HRV (2006) or other types of rigorous growth analysis, the same approach can be deployed to identify the major constraints to women’s economic participation. We then seek to identify overlapping impediments to growth and economic participation. In other words, we are interested in removing the most significant gender-based distortions that impede the achievement of more robust economic growth outcomes.

The remainder of this section illustrates the application of this approach to the case of Turkey. We use the prioritization of the binding constraints to growth and inclusion in Turkey’s Systematic Country Diagnostics (World Bank, 2016) and the gender-gaps database of the World Economic Forum (WEF). The latter provides standardized data on gender gaps in education, health, economic and political participation in Turkey and other countries. According to Turkey’s Systematic Country Diagnostics the top binding constraints to growth, given a score of 4 in column (2) of Table 1, are:

- low quality of regulatory and accountability institutions;

- geopolitical tensions in East and Southeast Turkey;

- underdeveloped financial markets;

- low educational achievements;

- low female labor force participation;

- low performance in technology absorption and innovation;

- weak corporate governance;

- weak competition policy and its enforcement;

- congested cities.

All these constraints, except the last four, are also considered crucial for inclusion and given the rank of 4 in column (3) of Table 1. As noted in HRV (2006) λY’s vary in size so at any given point in time only one of them is binding growth. In practice it is difficult to assess the size of the λ’s so most SCDs list several potential binding constraints whose λY’s are likely to be sizable.

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Boys’ enrollment in public schools Unadjusted difference between treatment and comparison districts <!-- Start of picture text --> co<br>ft 7<br>@<br><2)<br>i)<br>5 LO ce Rees ene ee eminem ca,<br>oS Se ee ee ee<br>S ee eS es<br>ra] cece ee! pee ee<br>Ta): ” ETaee 2 eee eee eee eee eee eee<br>Cc eran aee<br>Ww _eeeeneere<br>to | -----el<br>2003 2004 2005 2006 2007 2008<br>Year<br>--------- Lower bound eee Comparison District<br>oe Treaiment District --------- Upper Bound<br><!-- End of picture text -->

Source: Author's calculations using EMIS School Census Data
**Table 5. Determinants of private entrepreneurs’ social status**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|VARIABLES|Economic status|Political status|Social<br>status|sum<br>of three<br>status|Principal<br>component|
|Share of state economy|-0.367*|-0.211|-0.269|-0.308|-0.370*|
||(0.218)|(0.219)|(0.220)|(0.215)|(0.218)|
|Ln GDP percapita|0.226|0.354|0.328|0.396*|0.245|
||(0.233)|(0.233)|(0.233)|(0.231)|(0.235)|
|FDI/Total investment|1.693**|0.456|1.218|1.336|1.671*|
||(0.863)|(0.860)|(0.860)|(0.850)|(0.865)|
|Ln asset|0.222***|0.126***|0.176***|0.192***|0.222***|
||(0.010)|(0.009)|(0.009)|(0.009)|(0.010)|
|Firm age|0.030***|0.033***|0.032***|0.035***|0.030***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Female|-0.121**|-0.074|-0.078|-0.101**|-0.125**|
||(0.050)|(0.050)|(0.050)|(0.049)|(0.050)|
|Year of schooling|0.022***|0.010*|0.020***|0.018***|0.022***|
||(0.006)|(0.006)|(0.006)|(0.006)|(0.006)|
|Party member|0.097***|0.430***|0.174***|0.281***|0.099***|
||(0.036)|(0.036)|(0.036)|(0.036)|(0.036)|
|Membership in Congress or|0.612***|1.323***|0.821***|1.051***|0.616***|
|Consultative Conference|(0.039)|(0.040)|(0.039)|(0.039)|(0.039)|
|Prov, Sector, year|Yes|Yes|Yes|Yes|Sector|
|Observations|11,142|11,081|11,125|11,076|11,076|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

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**Figure 7: Real GDP growth in baseline, low and high growth scenarios**

<!-- Start of picture text --> Average annual GDP growth 2005-2030 (%)<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Low Baseline High<br>EUR USA XHY CHN XEA SAS ECA MNA SSA ARG BRA CHL COL MEX PER CAC XLC<br><!-- End of picture text --> Source: World Bank staff calculations.

Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America The impact on skill premiums is symmetric around the baseline: we observe the highest drop in the skill premium in the low growth scenario; and the smallest drop of the skill premium in the high growth scenario (with an absolute size of deviation from the baseline being similar in the high and low growth scenarios). In the high growth scenario, the shift towards services is strongest, therefore the demand for skilled workers grows at the faster pace than in the baseline, while the supply of skilled workers (determined by demographics) remains the same. As internal migration from rural to urban areas applies only to unskilled workers and is relatively low, the growth of skilled wages is therefore very high in this scenario, and hence results in the smallest drop in the skill premium. In high income countries, the two alternative growth scenarios result in almost unchanged skill premiums given that the relative growth deviations between the scenarios are rather small. On the other hand, the differences between low and high growth scenarios in developing countries are quite significant, for example around 1 percentage point per annum in Argentina, Chile and the Rest of Latin America. This is associated with significant differences in the growth of wages across scenarios, e.g. in Peru in the low growth scenario wages of skilled workers are expected to increase by 40 percent over 2005-2030 period, while in the high growth scenario their growth is 200 percent or over five times faster.

The urban premium is related to the developments in sectoral composition of output. As an example consider Peru, where in the low growth scenario agricultural sector expands, but it contracts in the baseline and high growth scenarios. In the low growth scenario, urban premium 19