rafmacalaba/datause-encoder-data huggingface dataset:
financial turmoil when capital controls are not in effect.<sup>30</sup> Finally, the control period in Korea is divided into three sub-periods, to reflect the fact that the severity of restrictions lessened during the sample period as explained in the previous section.

Figure 3 shows that during tranquil periods the cross-market premium oscillates around zero. This appears to be case also at times of financial distress, characterized by a larger volatility of the premium. The average premium turns positive in periods when capital outflows are restricted (Argentina, South Africa, and Venezuela) and negative in periods of controls on inflows – except in Indonesia, where the (relatively loose) limits on foreign participation may have been not binding at the time and where domestic investors could still do the arbitrage.<sup>31</sup> By contrast, in Korea, where a similar ceiling is combined with a rule restricting the convertibility of the DRs, arbitrage is impeded regardless of whether the ceiling is binding. The evidence that the discount is much lower in Chile than in Korea, on the other hand, directly reflects the different nature of the restrictions: quantitative limits that prevent arbitrage in Korea, and an implicit tax that weakens arbitrage in Chile. Note that the Chilean “tax” on inflows effectively increases the price of the underlying stock, which should therefore fluctuate around the average value of the tax from the investor’s standpoint. According to the figure, that was roughly two percent.

Table 4 displays summary statistics of the average cross-market premium during tranquil, crisis, and control times.<sup>32</sup> The table shows that the presence of controls is associated with sizeable premia of the expected sign, ranging from 49 percent under controls of outflows in Venezuela, to –31 during the period of most restrictive controls on inflows in Korea. The only exception is, again, Indonesia, where the small positive premium is associated with the presence of controls on inflows, consistent with accounts that the latter were not binding at the time.

In addition, a comparison with tranquil times shows that the volatility of the premium increases significantly both during crisis and under controls, as expected. In particular, the volatility and the mean of the average premium are positively correlated.

> 30 Although the crisis in Argentina started before the controls were introduced, the sample period without controls (light-shaded in Figure 3) is so short that we do not consider it as a crisis period for the purpose of the empirical tests below.

> 31 A ceiling on foreign investment will not affect arbitrage by foreign investors as long as foreign participation is well below the limit.

> 32 For Korea, the statistics are derived from the average premium of the unrestricted stocks (tranquil period) and the average premium of the restricted stocks (control periods).

19
[2000]). Furthermore we estimate that this extra weight depends positively on an industry’s share of employment and regional concentration.

The paper is organized as follows. We start by providing background information on the EU’s trade policy that guides the theoretical and empirical modelling. In section 3 we model the interaction between PTAs and MTL and derive the main results. In section 4 we fi rst discuss the predictions and our strategy for empirical identi fi cation and then analyze and quantify the estimation results. In the fi nal section we summarize the main results and discuss their implications. All proofs are in the appendix.

# 2 The European Union’s Trade Policy

Until the most recent expansion, the EU’s membership was composed of 15 countries that accounted for one-third of the world output and more than 20 percent of world trade. The EU succeeded the European Communities that started in the 1950s as a customs union. Currently the EU members form a single market with free movement of goods, services, capital, and labor and also cooperation on foreign and security policy as well as justice and police matters.

The main actors in the formation of EU trade policy are the Commission, the Council, and the European Parliament. The Commission negotiates and enforces trade policy on behalf of the member states. The Council, where each member state is represented at the ministerial level, is the decision maker. It determines a mandate on the basis of a Commission proposal. The Commission negotiates on the basis of this mandate and the Council must then decide whether to approve the outcome or not. The European Parliament is regularly informed on trade policy by the Commission but it is also involved by giving “assent” on major treaty rati fi cations that cover more than trade.<sup>7</sup> The 1966 Luxembourg Compromise required unanimity in decision-making in the Council for crucial issues. As a matter of fact, until 1987 the interest groups predominantly concentrated on lobbying their own governments for protection given this veto power. After the 1987 Single European Act, the Commission gained greater control and the veto power has been replaced by a quali fi ed majority voting and consequently, some interest groups have also started lobbying the Commission in order to be able to affect the trade policy determination process, not only the voting outcome. (Tavares [2001]) However, even after 1987, industry associations continued to favor lobbying their own government as opposed to the Commission (Hayes [1993]).<sup>8</sup> Thus, our assumption in section 4.6

> 7 Based on information from the Directorate General Trade of the European Commission. Accessed on July 30, 2004 at <http://europa.eu.int/comm/trade/index_en.htm>

> 8 There are some exceptions, such as the pan-European organization EUROFER that represents the steel sector but,

4
# **7. Conclusion**

This paper has examined the labor productivity growth and employment effects of BRICS’ versus G7 countries’ direct investment in selected African countries over the period 2001-2012. The results show that while BRICS’ FDI increases productivity growth across different model specifications, G7 countries’ FDI has varying effects under different conditions. For example, in the models where G7 effects are analyzed together with BRICS’ FDI and without control variables (Table 5), G7 FDI has a significant and negative impact on labor productivity growth and employment. In models that exclude BRICS’ FDI (Table 6), G7 countries’ direct investment exerts labor productivity growth enhancing effects, which are significant only when a measure of human capital (proxied by primary school enrollment), is included in the model. The results point to the importance of an educated workforce in Africa in augmenting the impact of BRICS’ and G7 countries’ direct investment on labor productivity growth.

With regard to employment, FDI from BRICS contributes to the employment rate among the African countries included in this study relatively more compared to FDI from G7 countries; this result holds across different model specifications. In contrast, the impact of G7 countries’ FDI on employment varies depending on the model specification. For instance, in the models with BRICS’ FDI, it reduces employment, while in those where BRICS’ investment is excluded and where the impact of infrastructure development is accounted for, it enhances the employment rate in Africa. Moreover, the magnitude of the effect of BRICS’ FDI on employment is 10 times higher than that of G7 countries.

An issue that African policy makers can glean from the forgoing analysis is that BRICS’ FDI appears to be a substitute for G7 countries’ FDI. For example, in models where BRICS’ FDI is included alongside that of G7 countries, the effects of G7 countries’ FDI on labor productivity 27
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64
# **Table 7: Communication Skills of Local Graduates in Developing Countries**

Dependent Variable: Quality of Communication Skills of Local MBA Graduates _(Numbers in brackets are z-statistics)_

|Variable|||||
|---|---|---|---|---|
|Size of Firm (No. of Employees)|-0.0000242<br>(-0.37)|-0.000012<br>(-0.18)|-0.00002<br>(-0.03)|-0.00007<br>(-0.10)|
|Number of MBAs Hired by Firm<br>Yearly|**0.311 ****<br>**(1.76)**|0.262<br>(1.42)|0.266<br>(1.45)|0.25<br>(1.46)|
|GDP per capita in Country|0.0001<br>(1.17)|0.00011<br>(1.16)|||
|No. of Commercial & Business<br>Administration Students in<br>Country (all levels and both male<br>& female)|79.48<br>(0.83)|167.2<br>(1.45)|**222.87 ****<br>**(1.88)**|**218.12 ****<br>**(1.85)**|
|Finance & Insurance Sector<br>Dummy|0.318<br>(-0.08)|-0.111<br>(-0.33)|-0.07<br>(-0.20)|-0.073<br>(-0.22)|
|African Regional Dummy||-0.223<br>(-0.42)|-0.248<br>(-0.48)|-0.274<br>(-0.53)|
|East Asian Regional Dummy||**-1.40 ***<br>**(-2.52)**|**-1.49 ***<br>**(-2.72)**|**-1.49 ***<br>**(-2.72)**|
|Latin American Regional Dummy||**-1.31 ***<br>**(-2.64)**|**-1.24 ***<br>**(-2.53)**|**-1.22 ***<br>**(-2.49)**|
|Middle Eastern Regional Dummy||**-1.64 ***<br>**(-2.55)**|**-1.75 ***<br>**(-2.72)**|**-1.73 ***<br>**(-2.71)**|
|South Asian Regional Dummy||0.079<br>(0.13)|-0.0006<br>(-0.001)|-0.44<br>(-0.07)|
|Dummy for Firm Hiring MBAs<br>with Specific Skills||-0.40<br>(-1.30)||-0.388<br>(-1.30)|
|Dummy for Firm Hiring MBAs<br>with Finance Skills|||-0.149<br>(-0.47)||
|Dummy for Firm Hiring MBAs<br>with Marketing Skills|||-0.351<br>(-0.52)||
|Number of Observations|173|173|174|174|
|Likelihood Ratio chi-squared|8.4|**19.38 ****|16.76|**17.97 ****|

- Significant at the 5% level

** Significant at the 10% level

28
94 the pension, if the number of contribution years is at least 40. In the case of retirement between 60 and 62 the pension is reduced by 0.2 percent per month and between 58 and 60 by 0.5 percent per month (given the 40 years of contribution). An overview is given in the following table.

|**Table 39**<br>|**PFSBB:**<br>|**Early R**<br>|**etireme**<br>|**nt and P**<br>|**ension **<br>|**Benefi**<br>|**ts, 200**<br>|**5**<br>|
|---|---|---|---|---|---|---|---|---|
|Retirement Age|65|64|63|62|61|60|59|58|
|Pension in %|||||||||
|of insured wage|100.0%|100.0%|100.0%|100.0%|97.6%|95.2%|89.2%|83.2%|
||Sourc|e: Pensio|nskasse|SBB (20|04), p.1|2|||

The bridging pension equals up to the maximal amount payable under the AHV/AVS. The repayment starts at the ordinary AHV/AVS age and is charged against the annuity payments. For the period of age 60 to age 65 the bridging pension is subsidized by the employer. This does not hold for the bridging pensions before age 60.

Investment: The asset management invests the old–age credit balances with a long– term focus and well diversified. The investment horizon extends over a period of 10 to 15 years. Taking into account their ability to take risks, they assume a return on investment of 4.5 percent. As at 31.03.05, the portfolio comprised a high fraction of bonds issued in CHF (46 percent). Foreign bonds (11 percent), domestic shares (9.9 percent), foreign shares (16.8 percent) and mortgages (7.7 percent) were among other important asset classes apparent in the portfolio. For the period 31.12.04 to 31.03.05, the return on investment was 1.95 percent, whereas the benchmark performed at 2 percent.

# **ABB Pension Fund**<sup>**85**</sup> **:**

_The pension fund that is set up for employees of ABB is organized in the form of a foundation. Economically or financially associated companies can join the pension fund. The ABB pension fund accounts for more than 7 thousand active insured and slightly more than 8 thousand retirees in total. The old–age insurance system is organized in the form of defined contributions, where the member can choose among three different types: Standard (i.e., same contributions of employer and employee), Standard plus (i.e., higher contributions of employee), and Standard minus (i.e., higher contributions of employer). Early and late retirement is possible, but not earlier than at age 57 and not later than at age 70, respectively. Retirement benefits can be drawn in the form of pensions, or in the form of a capital payment, or as a mixture of both. The risk insurance (i.e., invalidity and death) is organized in the form of defined benefits, where the contributions are fully paid by the employer. The asset side of the balance sheet accounts for 3.2 billions CHF, which covers liabilities related to old–age benefits at a rate of 103.2 percent. The liabilities were computed by using a technical interest rate of 3.75 percent._ Early Retirement: At ABB, members may at their request retire from the age of 58 85c.f. ABB Pension Fund (2005)
descriptive results presented in this paper apply the appropriate weights (unless otherwise indicated). The 2002 survey used substantially the same questionnaire and covered 34,785 households.

An interesting feature of these two surveys is that they include a panel of 5,755 rural households. An effort was made in 2004 to re-survey all 6,309 households that had been surveyed in rural areas in rounds 2 and 3 of the 2002 socioeconomic survey. This represents an annual attrition rate of 4.5%, which is relatively low. A comparison between panel households and those who dropped out of the panel found no appreciable differences in the relevant variables (in 2002), allaying concerns about attrition bias.

The summary statistics in Table 2 come from a special module that was included in the 2004 socioeconomic survey and that asked all adult members of households about their experience with the VRF. By 2004, a sixth of all adults had borrowed at least once from the VF, with higher proportions of borrowers among the poor (defined as those in the poorest quintile, as measured by expenditure per capita) and among those in rural areas; in this respect, VRF lending differs sharply from the older “village bank” programs in Northeast Thailand analyzed by Coleman (2002), where the bulk of the loans, and gains, accrued to the wealthier villagers. Adults in 38 percent of households had borrowed from the VRF by 2004.

Of those adults who did _not_ borrow, less than one percent had been refused a VRF loan, although a further 4% thought that they would be turned down. On the other hand, over a quarter of non-borrowing adults said they had no need to borrow, and almost a third said that they did not want to go into debt. Poor households were less likely to indicate that they did not need to borrow, but more likely to be fearful to going into debt.

The average amount borrowed in the most recent VRF loan was 16,183 baht (about $518), and this was only slightly less than the amount requested on average. The mean interest rate charged on VRF loans was 6.0 percent per year, but there was considerable variation, as Figure 1 shows: substantial numbers of Village Funds charged annual interest rates of 5, 3, or 12 percent. The interest rate paid by poor, or rural, borrowers was essentially the same, or perhaps slightly lower, than that paid by other adults.

Although the rhetoric surrounding the Village Revolving Fund program emphasized the importance of providing finance for processing and packaging, over half of all VRF borrowers said that they planned to use the money for relatively traditional agricultural purposes. This effect was even more marked among poor and rural borrowers. Borrowing is fungible, so this does not necessarily imply that spending on agricultural activities actually rose as a result of the implementation of the Village Fund program, but there is a dissonance between the reported uses of the borrowed funds and the original aspirations for the Fund.

_Village Fund, draft of June 29, 2009_ _Page 7 of 34_
2 investments in human and physical capital at the margin, relative to other forms of household income. For instance, in a recent study of remittances and education in El Salvador, Edwards and Ureta (2003) find that international remittances (mainly from the US) have a large positive impact on student retention rates in school. In a similar study of remittances and education in the Philippines, Yang (2005) reports that positive exchange rate shocks lead to a significant increase in remittance expenditures on education. Osili (2004) finds that a large proportion of remittance income in Nigeria is spent on housing. At the mean, a 10 percent increase in remittance income raises the probability of investing in housing in Nigeria by 3 percentage points. Choosing among these three quite distinct views of the role of remittances in development is largely a question of creating a body of careful empirical work based on statistically representative household surveys.

This paper provides new, statistically robust evidence on how remittances are used in one low-income, Sub-Saharan African country, Ghana. The results of the nationally-representative, 2005/06 Ghana household survey are used to compare the marginal spending behavior of three groups of households: those receiving no remittances, those receiving internal remittances (from within Ghana) and those receiving international remittances (from African or other countries). Because all surveyed households can be separated into one of these three groups, it is possible to compare the marginal spending patterns of remittance- and non-remittance receiving households across a broad range of consumption and investment goods, including food, education and housing.

The results, which are of interest for economic policy making in Ghana, may have broader relevance to the remittances and development debate as well. Since household incomes in Ghana are a fraction of those in many other developing countries that receive international remittances (e.g. El Salvador, Mexico, and the Philippines), remittances may be used differently
Figure 5: Income Shares: Routine/Non-Routine Labor <!-- Start of picture text --> (A.1) Non-Routine Share 2011 (A.2) Routine Share 2011<br>-7 -6 -5 -4 -3 -2 -7 -6 -5 -4 -3 -2<br>Log Real GDP Per Capita Log Real GDP Per Capita<br>70 25<br>60<br>20<br>50<br>15<br>40<br>Income Share (%) Income Share (%)<br>30 10<br>20<br>5<br><!-- End of picture text --> _Notes:_ The figures illustrate measures of the routine and non-routine income shares. Earnings shares are taken from the I2D2 database and the aggregate labor share is taken from the PWT 8.1. Self-employed workers are included and the patterns are robust to their exclusion.

# 4. ICT Abundance and Industrial Composition

Inspired by the discussion in the previous section we present a simple quantifiable model to illustrate the relationship between industrial composition and the relative values of ICT and NICT capital stocks. Based on the predictions of this model we then evaluate the degree to which the cross-country differences in ICT abundance are accounted for by variation in industrial composition.

To motivate the analysis, Figure 6 documents that low-income countries produce disproportionately in sectors that utilize ICT less intensively. Panel A is based on country and industry specific value added as reported in the Groningen Growth and Development Center’s 10 sector database (GGDC10, Vries, Vries and Timmer, 2014). While this measure provides a relatively high level of disaggregation, it is available only for a relatively small number of countries. As an alternative, we consider a more coarse measure of industrial composition by using data on the value added in agriculture, industry excluding manufacturing, manufacturing and services, based on the World Bank’s World Development Indicators (WDI) for a wider set of countries. Panel B of Figure 6 illustrates a similar distribution of industrial composition based on this alternative data source. To highlight differences between high and low income countries we illustrate the value 20