rafmacalaba/datause-encoder-data huggingface dataset:
**Discussion of perception data as opposed to objective measures** . Perception data have the strength and weakness of building on perceptions of the respondent. For example, the answers may incorporate some information that would otherwise be lost, if the data were of a more objective character. By contrast, a drawback of perception data is that respondents might provide inconsistent information. Introducing a new product may have different importance for different firms or even different individual respondents, and it may not be clear whether the respondent is answering from the viewpoint of the firm or of the individual.

Even though there are some concerns about the data it is still possible to implement the models. It is however important to remember that there are these inconsistencies when reading and interpreting the results.

# **Colombian survey**

The quality of the data of the Colombian survey seems to be very good. The consistencies across the different variables were checked and no major errors or inconsistencies were revealed. One example of the control for consistency is to control whether firms that claim that they have innovation activities or R&D activities also have expenditures on innovation and R&D respectively. This analysis did not reveal any problems.

In the description of the data 7 percent of the Colombian firms reported having R&D related expenses, but 10 percent have an R&D department. This inconsistency can be difficult to explain. The straightforward answer is that there are some Colombian firms who do not have activities in their R&D departments. A more reasonable explanation is that the investments related to the activities in the R&D department are not regarded as R&D activities, but more broadly as innovation activities. This seems to be a reasonable answer to this potential inconsistency.

An additional potential inconsistency was the sum of PhDs employed in the private sector, which was reported to be 4680. It is a high number and compared to the total of people with a master degree (678) seems greatly exaggerated. A closer look at the data revealed four firms reported 99 PhDs in each department but were identified 99 PhDs in total. To deal with this obvious inconsistency we identify the 11 different departments and reduced the number of PhDs to zero for each individual department, whilst leaving the number of PhDs in the firm as a whole unchanged. As a result the total of PhDs was reduced by 4,356 (4 times 99 times 11) leaving a total of 324 PhDs employed in the Colombian private sector. The following table provides a view of the problem with the number of PhDs. The “calculated total” is the total computed manually on basis of the information of the 11 different departments.

**Table A.5.2 Number of PhDs in Each Department**

|**Department**|**Mean**|**Max**|**Min**|**Sum**|
|---|---|---|---|---|
|Administration|0.09|99|0|611|
|Sales|0.07|99|0|436|
|Design|0.06|99|0|402|
|<br>Engineering|0.07|99|0|407|

37
calls for a total ban on private tutoring, while the latter supports the (controlled) development of tutoring.<sup>21</sup> Table 1 lists the reasons that students take private tutoring classes, according to data from the VHTS. Tutoring classes are divided into two categories: tutoring classes organized by the student’s own school, and other tutoring classes. Across the two types of tutoring, the most important reason for taking tutoring is to prepare for examinations, which accounts for almost half of all responses (42-47 percent). Other commonly cited reasons given include to catch up with the class (13-14 percent), to acquire better skills for future employment (13 percent), and to pursue a subject that the student enjoys (6-11 percent). Other reasons, such as to get childcare, to compensate for poor-quality lessons in school, or to study subjects not taught in mainstream classes, account for a smaller proportion of all responses (3-6 percent each). The preeminence of exam preparation over other reasons for taking tutoring classes reflects the importance of examinations in the school system in Vietnam.<sup>22</sup> Richer households in Vietnam spend more on tutoring classes than do poorer households, as shown in Table 2. Currently about 40 percent (= 100 - 60.4) of households in Vietnam send their children to private lessons, and the majority of them (90 percent) spend between 1 percent and 5 percent of household expenditure on tutoring classes. The percentage of households with positive expenditures on tutoring classes is only 21 percent in the poorest (1<sup>st</sup> ) consumption quintile, but nearly doubles to 38 percent in the next richer quintile (2<sup>nd</sup> ) and hovers around 35 percent in the top three quintiles (3<sup>rd</sup> to 5<sup>th</sup> ). In terms of

> 21 See also Dang (2011 and forthcoming) for more detailed discussions of the private tutoring phenomenon in Vietnam.

> 22 For examining our hypothesis of the quantity-quality tradeoff, we are in fact assuming that sending children to tutoring classes are completely determined by parents.  If corrupt teachers force tutoring on their own students beyond parental control (see, e.g., Bray, 2009; Jayachandran, 2012), household investment in tutoring would not provide valid evidence for this tradeoff. However, the results in Table 1 suggest this concern is a minor one in the context of Vietnam.

13
economy. Most of the NIM spread in Brazil is due to I&R factors, and changes in I&R are highly correlated with changes in NIM changes. China on the other hand, appears to be different and I&R factors are less significant in the determination of the NIM spreads.

<!-- Start of picture text --> Figure 13a. BRICs - I&R and macro (2009)<br><!-- End of picture text --> <!-- Start of picture text --> Figure 13b. BRIC - I&R and macro (2008)<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>10<br>8 8<br>6 6<br>4 4<br>2<br>2<br>0<br>0<br>I&R Macro<br>I&R Macro -2<br>-2<br>-4<br>-4<br>-6<br>-6<br>India Russia Brazil China India Russia Brazil China<br>Percentage points<br>Percentage points<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data India is an interesting case, where both in 2008 and 2009 it experiences negative micro spreads. It seems that banks in India have underestimated micro-factors and have overestimated macro-factors. This is exactly the opposite of what happened in Brazil in 1995, 1997 and 2002. In India the macroeconomic situation in both 2008 and 2009 turned out to be better than the banks had anticipated. Banks in India were too pessimistic about the macroeconomic situation, while at the same time banks were too concerned with the microeconomic situation in their industry when making lending and borrowing decisions.

In the case of China and Russia we observe almost no movement in the spreads both in terms of micro and macro-factors in both 2008 and 2009. This might be the case because the banking sectors are tightly controlled and a large part of the banking sector is controlled by the state. A further reason for the stability of the spreads in China and Russia might be the presence of large state-sponsored corporations, which borrow a significant amount of money from the domestic banking sector.

The amount of borrowing by these large state-sponsored corporations is generally stable because they have a high degree of bargaining power with the banks. Hence, the interest rates these large corporations borrow are generally fairly stable and primarily based on inflation. Inflation in both China and Russia was generally stable or decreasing because of the collapse in commodity prices and the global economic activity.

Overall, BRIC countries seem to be doing better than other developing countries. They seem to have weathered the financial crisis without major volatility in both micro and macro-factors. Compared to all developing countries, BRIC countries saw less volatility in terms of NIM in 2008 and 2009 (Annex 1 Figures 1a & 1b). The fact that Russia’s spreads have remained constant is surprising given the sharp devaluation of the Ruble and the large drop in financial assets’ prices in 2009. Perhaps the considerable 26
# **APPENDIX II: ECOWAS: EPA trade and fiscal effects**

|||Our Study<br>(WorldBank/ <br>|IMFdata,2004)<br>||Busse, B<br>|orrmann and G<br>(ECOWAS <br>|rossmann<br> data,2001)<br>||
|---|---|---|---|---|---|---|---|---|
|**Trade Effects**|Nigeria|Ghana|Senegal|Cape Verde|Nigeria|Ghana|Senegal|Cape Verde|
|Collected tariff rate|8.22%|7.88%|9.5%|10.3%|15.90%|4.70%|8.50%|12.10%|
|EU import in % of total import|37.3%|39.2%|60.0%|74.1%|47.9%|43.1%|51.8%|74.3%|
|Change in prices after elimination|-7.6%|-7.3%|-8.7%|-9.4%|13.7%|4.5%|7.8%|10.8%|
|of tariff on EU Import|||||||||
|I. Trade creation|||||||||
|million US $|417.8|93.7|95.4|20.8|348.3|45.6|71.2|16.9|
|% of preferred imports|5.3%|5.1%|6.1%|6.6%|12.5%|3.7%|8.0%|9.2%|
|II. Trade diversion|||||||||
|million US $|486.1|105.8|70.9|15.4|229.1|40.2|31.4|4.5|
|% of non-preferred imports (Non EU)|3.7%|3.7%|6.8%|9.0%|7.6%|2.4%|3.8%|7.1%|
|III. Total Trade effects|||||||||
|million US $|903.9|199.5|166.3|36.2|577.4|85.9|102.7|21.5|
|% of preferred imports|11.5%|10.9%|10.6%|11.4%|20.8%|6.9%|11.5%|11.7%|
|**Tariff Revenue Loss**|||||||||
|million US $|682.0|150.6|154.7|34.3|487.8|90.8|87.9|24.0|
|% of Total Tariff Revenue|41.48%|47.4%|69.6%|78.0%|52.7%|66.4%|60.0%|79.9%|
|% of Total Government Revenue|2.4%|7.1%|10.4%|15.8%|2.5%|10.3%|10.7%|19.8%|
|% ofGDP|1.0%|1.7%|2.0%|3.6%|1.2%|1.8%|1.9%|4.1%|

24
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 1.2  Progress and challenges for regional integration in ECOWAS**

|Sector|Achievements|Challenges|Promise of regional integration|
|---|---|---|---|
|**Road**<br>**transport**|Several major international<br>gateways in West Africa that<br>facilitate trade.|High trucking charges and lengthy<br>delays due to trade facilitation issues.<br>Coastal countries appear to neglect<br>maintenance of regional corridors.||
|**Railways**|Two relatively successful<br>binational concessions|Low levels of passenger and freight<br>traffic, poor operational performance of<br>railways. Railways facing stiff<br>competition from other modes of<br>transport. Incompatible rail gauges.|Reducing costs and delays associated with<br>surface transport of goods in the region.|
|**Ports**|Burgeoning container and<br>general cargo traffic.|Poor operational performance and<br>absence of a transshipment hub.||
|**Air**<br>**transport**|Reasonable levels of<br>interregional connectivity.<br>WAEMU and BAG are most<br>liberalized markets in Africa.|Low levels of connectivity within<br>ECOWAS.<br>Lack of a strong regional hub.<br>Aging fleet and poor record with<br>respect to air traffic safety.|Collaborating on improvement of safety record.<br>Improving efficiency of regional air transport<br>through better hub system.|
|**Power**|High electrification rates.<br>Cost recovery is better than<br>in other regions.<br>Principle of regional trade<br>already well established.|Lack of generation capacity leads to<br>unreliable service, with only 70 percent<br>of demand being satisfied.<br>Utilities highly inefficient with regard to<br>distribution losses and revenue<br>collection.|Deepening regional integration would save the<br>WAPP area $435 million in annual energy<br>costs, and annual savings in carbon emissions<br>of some five million tonnes of carbon. Long-run<br>marginal cost of power in the WAPP would fall<br>by $0.01 per kilowatt-hour or 5 percent. Overall<br>rate of return on regional integration<br>investments is 33 percent.|
|**ICT**|Access to ICT services<br>among highest in Africa.<br>Significantly cheaper to call<br>on landline within ECOWAS<br>than outside the region.<br>Roaming arrangements<br>relatively advanced.<br>Associated regional telecom<br>regulators have been active<br>in promoting harmonization.<br>Well-endowed with<br>submarine cable<br>infrastructure.|Relatively high prices for ICT services.<br>Many countries not connected to the<br>submarine cable.<br>Even those connected face high costs<br>due to lack of competition on<br>international gateways.|Achieving regional integration of ICT will cost<br>only $5.1 million annually, and bring benefits of<br>$ 115 million annually, a return of more than<br>400 percent. Benefits derive primarily from<br>lower prices inducing higher rates of<br>subscription to broadband services. The overall<br>rate of return on regional integration<br>investments (existing greenfield and filling of<br>gaps) is 52 percent.|

Source: AICD.

Note: BAG = Banjul Access Group; WAEMU = West African Economic and Monetary Union; WAPP = West African Power Pool.

15
failure to upgrade exports and integrate into global production chains (Behar and Freund, 2011). Consistent with these explanations, our results suggest that by deterring efficiency-seeking FDI in non-oil manufacturing and tradable services, political instability discourages quality FDI capable of bringing new technology and creating jobs in the region. By hurting efficiency-seeking, quality investments, shocks to political stability exacerbate underinvestment in labor-intensive sectors and aggravate unemployment, considered a precursor to political instability in countries with youth bulges.<sup>5</sup> Last but not least, to our knowledge this paper is among the first to examine the economic effects of the Arab Spring on FDI and demonstrate that political instability has resulted in a reduction in foreign direct investments, especially in non-oil manufacturing activities, in countries with Arab Spring uprisings, including Egypt, Tunisia, Libya, Syria, and Yemen. Continued political uncertainty will likely entrench MENA’s dependence on natural resources and limit prospects for economic diversification. On the bright side, the very same resource dependence might have shielded the region from yet more dramatic reductions in FDI; had investments been concentrated in manufacturing and commercial tradables, the decline in investment due to Arab Spring events could conceivably have been more dramatic.

The remainder of this paper is organized as follows. Section 2 discusses the econometric framework and the data, providing a bird’s eye view of the sectoral composition and evolution of greenfield FDI and political instability. Section 3 discusses estimation issues and presents our results, while section 4 provides concluding remarks and suggestions for future research.

> 5 Using a time-series, cross-national statistical model for internal armed conflict for the period 1950-2000, and for event data for terrorism and rioting for the years 1984-1995, Urdal (2006) shows that youth bulges increase both opportunities and motives for political violence. The latter may arise as youth bulges are more likely to experience unemployment.

7
unemployment rate is 2-3 times higher than the adult unemployment rate, regardless of the level of aggregate unemployment (figure 3). However, there is considerable cross country variation, not only in the levels of youth unemployment but also in the ratio of the youth to adult unemployment rate. A very high ratio in some countries is a signal of atypical youth difficulties in the transition to work and a source of concern for policymakers.

**Figure 3: Unemployment is higher for youths than adults**

<!-- Start of picture text --> 3 : 1 2 : 1 1 : 1<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4<br>Adult unemployment rate<br>Youth unemployment rate<br><!-- End of picture text --> _Notes:_ The 1:1 line represents cases where the estimated incidences among young and adults are identical. The 2:1 (and 3:1) lines represent cases where the estimated incidences among young is twice (three times) as large as adults.

The unemployment rates vary widely across regions, gender, urban and rural sectors and with different individual and household characteristics. The availability of household survey data allow for disaggregating across these different dimensions and a deeper assessment of unemployment among youth (see figure 2).

Overall unemployment rates for youth and adult age groups are very similar in developed and developing countries. Youth rates are more than double the adult rate regardless of the country’s development level, with youth unemployment rates averaging in the mid-teens. Male and female youth unemployment rates are virtually identical in developed and developing countries, averaging about 15 percent. In developed countries, youth unemployment rates are highest in rural areas. In developing countries, the opposite pattern holds with urban unemployment rates more than double the rural rates.

According to the ILO, the unemployment rate varies widely across regions, from a low of 7 percent in East Asia to 13.4 percent in industrial economies to a high of 25 11
Policy Research Working Paper 8700

# **Abstract**

The main goal of this paper is to document and analyze the long-term evolution of inequality of opportunity and thus extend the recent empirical literature, which is mainly concerned with its measurement at a specific point in time. Using repeated cross-section surveys for five European countries (France, Germany, Great Britain, Italy, and Switzerland), the evolution of inequality of opportunity is measured for a period of about two decades for the whole populations, as well as for different birth cohorts. Relative inequality of opportunity represents an important portion of total income inequality, with values ranging from 30 to 50 percent according to the standard deviation of logs (and reaching a lower share in case of mean log deviation) and, for all the countries, it shows a stable or declining time trend. When the birth cohorts are followed across time, inequality of opportunity decreases with age: the effect of circumstances seems to weaken over the life cycle. This is a quite different age profile from that of inequality of outcomes (income or consumption), which generally increases with age. A decomposition of the relative inequality of opportunity allows highlighting some key drivers of its time evolution. In all the countries, there has been a clear enhancement of equality of educational opportunity (as captured by a downward trending intergenerational education persistence) and a reduction of the returns to education. However, for some countries, notably Italy, these trends have failed to translate into decreasing inequality of opportunity in the income distribution because of the increasing role of parental networking (an additional channel through which parental background affects the incomes of offspring).

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at mbussolo@worldbank.org, daniele.checchi@gmail.com, peragine.vito@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 11

# New Loans to Personal Sector (billion yen)

||1965|1970|1975|1980|1984|1985|
|---|---|---|---|---|---|---|
|Housing loans|64|1010|5137|9673|10402|11886|
|Consumer loans|1016|2929|7261|20529|31610|34706|
|Loans|243|777|1890|9429|14706|17866|
|Installment<br>Credit|88|600|1446|3548|6840||
|Finance Co.<br>PFIs|1<br>87|a<br>592|342<br>1104|2118<br>1430|2788<br>4052|2486|
|Non-installment<br>Credit|155|177|444|5881|7866||
|Pawn Shop<br>Post Office|155|177|204<br>240||||
|Sales Credit|773|2152|5371|11100|16904|16840|
|Installment||2020|4751|10083|14435|9769|
|Non-installment||132|620|1017|2469|7071|

Source: _Shohisha Shinyou Toukei_ (Consumer Credit Statistics), 1978 (for the data of 1965), 1981 (for the data of 1970 and 1975), and 1983/1985/1986 (for 1980 and 1984), and 1988 (for 1985).

42
25

## in their Index of Political Cohesion and the one coded for the DPI.

# Table 2: Divided government and government debt

|Dependent variable:<br>change inpublic<br>debt/GDP|OriginalIndexof<br>Political Cohesion<br>(IPC)<br>(Roubini/Sachs) <br>1|= IPCfrom<br>|<br>Database of<br>Political<br>_Institutions<br>2|CHECKS!<br>fromDatabase<br>ofPolitical<br>Institutions<br>3|
|---|---|---|---|
|constant|-0.84|0.31|0.18|
||(0.53)|(0.54)|(0.96)|
|laggedchange in|0.867|0.84|0.84|
|public debt (¢-1)|(0.09)|(0.09)|(0.09)|
|change in|0.12|0.057|0.06|
|unemployment rate|(0.17)|(0.12)|(0.12)|
|change indebt|-0.20|-0.15|-0.17|
|service costs|(0.<br>18)|(0.19)|(0.18)|
|change ingrowth|-0.32|-0.27|-0.32|
|rate|(0.10)|(0.12)|(0.11)|
|Japan (0-1)|2.48<br>|0.67<br>|0.74<br>|
||(1.55)|(1.47)|(1.62)|
|IPCor CHECKS1|0.74|-0.053|0.02|
||0.30|0.28|0.23|
|N|83|79|83|
|AdjustedR?|0.54|0.5|0.5|

Note: This table reproduces the specification from Table 7, column 2, Roubini and Sachs, 1989, for the years 1975 - 85 (years of overlap with our dataset). Standard errors are in parentheses. Debt service costs are the real interest rate less the growth rate, multiplied by lagged debt/GDP. Column 2 excludes four observations (Italy and the US, 1975-76) for which [PC in the DPI is unavailable.

The coding differences noted in Table 2 have two effects. One is to raise the sample mean substantially, from 1.18 to 1.51. The other is to reduce the dispersion in the DPI measure of IPC among the fourteen countries; the IPC compiled by Roubini and Sachs exhibits a coefficient of variation of 83.5, while that of DPI is 72.4. These changes are not surprising. One would expect that fourteen wealthy, democratic OECD countries would exhibit more significant checks on government decision makers that higher values of the IPC imply, and that they would not exhibit large and sustained differences in such a fundamental political variable as the extent of divided government. The effects of divided government are
Chart 3 **Brazil: Domestic and Foreign Debt** Ratios to GDP <!-- Start of picture text --> 0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05 "---<br>0.00<br>85 86 87 88 89 90 91 92 93 94 95<br>- BRFDP ---- BRDDP<br><!-- End of picture text --> Broken line: Domestic Debt Solid line: Foreign Debt Sources: Foreign Debt, World Bank debt tables Domestic Debt, Central Bank of Brazil As the chart indicates, as of 1992 there has been a critical shift in the strategy of domestic and foreign financing of the primary deficit: domestic debt increased dramatically from about 5 percent of GDP in 1991 to 26 percent in 1995. This direction is traced to the policy of capital inflows sterilization (to avoid exchange rate appreciation), and to tight monetary policy associated with the "Real Plan." The switch in debt management is exhibited in a more striking fashion in the next chart, presenting the ratio of domestic debt to foreign debt.

21