rafmacalaba/datause-encoder-data huggingface dataset:
To assess whether access to a bank is a mere reflection of a _Union_ being more accessible to various infrastructure, which facilitates the _Union_ ’s EGPP implementation, we examine the association of access to other facilities with EGPP participation, using the same _Union_ -time fixed effects models. The results show that access to these facilities has little association with EGPP participation, and thus confirms the validity of access to bank as an instrument for EGPP participation (Columns (2) through (4) in Table 6). When we change the definition to incorporate both time and distance, where easy access means within 30 minutes of reach or within 5 kilometers boundary, or use actual time to the nearest bank, the results (See Appendix Table 1) show that the association between the easy access to banks and EGPP participation remains significant.<sup>18</sup> Table 7. Externality Test of the Instrumental Variable

||Log(Household Expenditure)<br>|Log(Per<br>ex|capita household<br>penditure)|
|---|---|---|---|
|Covariates|(1)||<br>(2)|
||base<br>extended|base|extended|
|Access to bank|-0.044<br>-0.025|-0.040|-0.018|
||(0.029)<br>(0.022)|(0.025)|(0.022)|
|Hhld hd'|-0.001**||0.002***|
|ouseo eas age|(0.000)||(0.000)|
|Hhld hd' di|0.013***||0.014***|
|ouseo eas eucaton|(0.002)||(0.002)|
|Mil id=1|-0.318***||-0.095***|
|arta status (not marre)|(0.017)||(0.017)|
|Disability (yes=1)|0.061***<br>(0.011)||0.086***<br>(0.011)|
|Household members average|0.031***||0.019***|
|education|(0.004)||(0.004)|
|Ohi ttl=1|0.078***||0.071***|
|wnersp (cae)|(0.010)||(0.010)|
|Ohi ld=1|0.001***||0.001***|
|wnersp (an)|(0.000)||(0.000)|
|Nb f hild d 5|-0.100***||-0.079***|
|umer o cren uner|(0.008)||(0.008)|
|Number of working age adults|0.044***<br>(0.005)||0.056***<br>(0.005)|
|Union-time fixed effects|Yes||Yes|
|Adjusted R squared<br>Notes: Robust standard errors in paren<br>the externality tests.|0.140<br>0.517<br>theses. *** p<0.01, ** p<0.05, * p<0.1. T|0.088<br>he table present|0.298<br>s estimated results of|

> 18 The average distance to the nearest bank when it is not located in the same _Union_ is about 5 kilometers.

22
# **Contents**

|**1. INTRODUCTION .........................................................................................................1**|
|---|

|**2. CONTRACTUAL SAVINGS INSTITUTIONS AND CORPORATE**|
|---|
|**FINANCING PATTERNS................................................................................................4**|
|**3. A SIMPLE MODEL OF FIRMS’ FINANCING CHOICES.....................................6**|
|3.1 THECORPORATESECTOR...........................................................................................6|
|3.2 LONGTERMDEBT......................................................................................................8|
|3.3 REFINANCINGRISK ANDFINANCIALINSTITUTIONS....................................................9|
|_(1) Short term Debt......................................................................................................9_|
|_(2) Bank runs and refinancing risks..........................................................................10_|
|_(3) Equity Markets.....................................................................................................12_|
|_(4) Predictions of the Model ......................................................................................14_|
|_(5) Contractual Savings Development.......................................................................16_|
|**4. DATA AND EMPIRICAL STRATEGY...................................................................18**|
|4.1 DATA........................................................................................................................18|
|4.2 DEFINITION OFVARIABLES ANDEMPIRICALSTRATEGY...........................................19|
|_(1)_ _Firm-specific Characteristics...............................................................................19_|
|_(2)_ _Macroeconomic Factors ......................................................................................20_|
|_(3)_ _Financial System Characteristics.........................................................................21_|
|_(4)_ _Empirical Strategy................................................................................................23_|
|**5. EMPIRICAL RESULTS.............................................................................................23**|
|5.1 DESCRIPTIVESTATISTICS..........................................................................................23|
|5.2 REGRESSIONRESULTS..............................................................................................26|
|_(1) The strategy..........................................................................................................26_|
|_(2) Institutional Investors and Firms’ Financing Patterns........................................27_|
|_(3)_ _Financial Structure and Financial Channels.......................................................29_|
|**6. CONCLUSION ............................................................................................................31**|
|**REFERENCES ................................................................................................................32**|

iii
# **1 Introduction**

Among the various dimensions of international migration, movements of the highly skilled are arguably the most topical. On the one hand, governments of more developed countries are implementing policies to attract the best and the brightest in an increasingly competitive market for skills. On the other hand, many poorer countries, especially those already suffering from low levels of human capital, are deeply concerned about retaining their most skilled workers, whose absence ultimately impinges upon their long-term economic and political development. Until now, the literature has almost exclusively examined high-skilled movements to OECD nations, often termed the ‘Brain Drain’. Even a casual observation of basic migration patterns, however, indicates that such a focus fails to capture the complete global picture.

The absence of detailed and high quality data is the main obstacle that prevents us from properly quantifying the extent of skill mobility across the world. These data shortcomings not only impede many important avenues of research, but in light of the paucity of immigration and emigration flow data by skill level, also militate against countries’ ability to assess their net human capital situation and thus the effectiveness of their immigration, education and labour market policies.<sup>1</sup> This paper is the first to seriously address this issue, by first developing a global overview of human capital mobility and then subsequently by introduc-

> 1In the absence of immigration and emigration flow data by skill level, the best nations are able to do in terms of assessing their net human capital situation and thus the effectiveness of their policies, is to compare the total level of human capital at a single point in time (i.e. at the time of census) with the total number of domestic nationals abroad. To be able to do this accurately, bilateral data are required for all potential destinations worldwide.

2
-6-

and Bhagrava (1983) (CRDW); and (b) the Dickey-Fuller test (DF) or Augmented Dickey-Fuller test (ADF) (Dickey and Fuller 1979, 1981). All test the null hypothesis that the series are I(1) : Ho : Xt I(1). The three statistics employed are calculable by least squares regression 1/ as follows: CRDW: Xt a + et, Ho : Xt I() if CRDW < 0.511 at 99%.

DF :Aet a - Bet 1 + vt9 Ho : Xt I I(1) if s is negative and has 2/ a t-statistic lower than -3.37 (95%) or -4.07 (99%).

n ADF AX a - Be i - y Ae t v , Ho: X - I(1) if B is negative and has a t-statistic lower than -3.17 (95%) or -3.77 (99%) where et are the residuals from the Xt regression and n is selected to be large enough to ensure that the residuals vt are white noise. A statistically significant, negative coefficient B signifies that changes in Xt or et can be reversed over time and that their levels are stable over the long term.

7. After establishing that commodity prices and interest rates are integrated, the next step is to see if they are also co-integrated. Two variables are said to be co-integrated if there exists a constant K such that

- 1/ Their critical values with one, two and three variables are provided by Engle and Granger (1987) and Granger and Newbold (1989).

- 2/ The test statistic is the t-statistic for Beta, but the t-distribution is not appropriate.
22 foregoes revenue, WTO panels tend to find benefit without much analysis.<sup>43</sup> But a panel has a more difficult challenge in determining whether there is a benefit conferred through other fiscal measures because there may be a question of the adequacy of a comparison to a market-based benchmark.

To ascertain a benefit, WTO judges have borrowed from the SCM guidelines for countervailing duties which explain how to calculate the amount of the benefit. These guidelines, in SCM Article 14, provide: (a) equity capital shall not be considered as conferring a benefit, unless the investment decision can be regarded as inconsistent with the usual investment practice (including for the provision of risk capital) of private investors in the territory of that Member; (b) a loan by a government shall not be considered as conferring a benefit, unless there is a difference between the amount that the firm receiving the loan pays on the government loan and the amount the firm would pay on a comparable commercial loan which the firm could actually obtain on the market; (c) a loan guarantee by a government shall not be considered as conferring a benefit, unless there is a difference between the amount that the firm receiving the guarantee pays on a loan guaranteed by the government and the amount that the firm would pay on a comparable commercial loan absent the government guarantee; (d) the provision of goods or services or purchase of goods by a government shall not be considered as conferring a benefit unless the provision is made for less than adequate remuneration, or the purchase is made for more than adequate remuneration. The adequacy of remuneration shall be determined in relation to prevailing market conditions for the good or service in question in the country of provision or purchase.

These guidelines are initially applied with respect to the marketplace of the defendant country, but if in Article 14(d), a government's predominant role in providing goods and services distorts the market so that in-country prices are unreliable, then an out-of-country market can be used as a substitute benchmark.

43Recipients actively seek grants and tax benefits so one might presume that they would not do so unless that made them better off. Yet few if any government grants or tax benefits are totally unrequited. The recipient typically provides consideration in the form of requested behavior, and so there is a question of whether to offset this cost to the recipient against the financial benefit to the recipient. For example, suppose that the government invites a firm to reduce carbon emissions and then grants the firm a marketable emission credit for doing so. The firm is better off receiving the credit than not receiving it, but there is a legitimate question of whether there is a real benefit to the firm. The Appellate Body has posited that some financial contributions "may involve reciprocal rights and obligations" but inexplicably has proclaimed that grants "will not involve a reciprocal obligation on the part of the recipient." Appellate Body Report, _US - Large Civil Aircraft (Second Complaint),_ para. & and n. 1294.
> - 10 -

the production costs and competitiveness of Indian industries. The results are summarized in Table 3 below which presents the subsectoral CNPRs along with the actual Nominal Protection Rates (NPRs) and the Net nominal protection rates (NNPRs). An important conclusion emerges from the table. Surcharges on capital and investment costs related to the CEPR (first column of the table) increase domestic prices on average by 12% of output's international prices. Extra price and cost of inputs increase domestic prices on average by 31%. The overall impact is that the sample projects require normally a Corrected Nominal Protection rate (CNPR) of 43% on average, s:y about 45%, two-thirds of which to offset the impact of high input costs and une-third to offset the impact of high capital and investment costs.

TABLE 3: CORRECTED AND ACTUAL NOMINAL PROTECTION BY SUBSECTOR/a (In % of Output Value at International Prices)

|Subsector|VA/CEPR.<br>Impact|Input Cost<br>Impact|CNPR|Actual<br>NPR|<br>NNPR|Distributn(%)<br>of Projects<br>with +/- NNPR|
|---|---|---|---|---|---|---|
|Heavy Chemicals<br>|10<br>|46<br>|56|59|3|60/40|
|Light Chemicals<br>|15<br>|19<br>|34|16|-18|0/100|
|Synthetic Yarns|17|46|63|65|3|50/50|
|Basic Steel Goods<br>|<br>10<br>|50|60|62|2|57/43|
|Electronics<br>|3<br> <br>|31<br>|34|32|-2|60/40|
|Other Engineering|<br>19|24|44|19|-25|0/100|
|Food Industries<br>|1<br>|23<br>|24|26|2|50/50|
|Miscellaneous|6|31|37|46|9|80/20|
|Overall|12|31|43|36|-7|42/58|

/a CNPRs, NNPRs and Actual NPRs in this table are averages weighted by Output in world prices.

# Source: Annex 4

25. The concordance between actual and compensatory NPRs is higher than for effective protection. The actual NPR is close to CNPR for Heavy Chemicals, Synthetic Yarns, Basic steel products, Electronics and Food industries. Positive NNPR is negligible (about 9%) in the Miscellaneous industries (dominated in the sample by Tyres). On the other hand, negative NNPRs are significant in engineering industries (25%) and in Light Chemicals (18%). Finally, the intra-sectoral discrepancies observed within each subsector for effective protection (cf. para. 23) are equally applicable to the nominal protection, as summarized in the last column of the table above. Except for light chemicals and engineering industries where all projects are subject to negative NNPR, the projects in each other subsector are distributed in approximately equal shares between positive and negative NNPRs. On a productwise basis (cf. Annex 4), it is important to remark that subsectors producit., intermediates and inputs for other subsectors (heavy chemicals, synthetic yarns, basic steel products) show quite higher NPRs and CNPRs (averaging about
# Conversion Method for Former Soviet Republics

2. Obviously, the above assumptions do not hold for many economies, including former Soviet Republics. Particularly, the official exchange rate does not link closely the average domestic prices to the corresponding international prices even for tradables in many market economies. Thus, Equation 3 in para 1 above may have to be modified as follows: E = Pt(d)/Pt(w) = (1 +A) Eo ....... (3'), where A is the average net indirect taxes on traded goods. In this case, the conversion of GNP at Eo would result in

[QtPt(d) + QnKPt(d)]/Eo from (4), pam 1 above = (1+A)[QtPt(w) + QnKPt(w)] = (1+A)GNP(w) .... _(5)_ 3. The Bank's Atlas method assumes that A in Equation (5), pam 2 above, is "small" for most economies and thus (1 +A)GNP(w) is still broadly comparable across countries. If A is considered to be "too large" for an economy, then an alternative conversion rate, which approximates E, is used for the economy.

4. Most of domestic prices of tradables in Soviet Republics before 1992 were. administratively determined independently of their foreign prices. Thus, the official exchange rate did not link foreign currency prices of tradables to their domestic prices. Further, foreign prices of prducts traded with for-e.r CMEA countries were negotiated between trading parties and included substantial implicit sL.,sidy and tax elements. They diverge significantly from the international prices. For products "traded" between Republics, their foreign prices need to be imputed. All these suggest that the official ruble-dollar exchange rate can not be used to derive Soviet Republic's GNP in dollars, which would be internationally comparable.

5. National accounts data of former Soviet Union (FSU) on external trade suggest that FSU's official exchange rate deviated, by exceptionally large margin, from the implicit exchange rate linking the domestic prices of traded goods and the corresponding international prices. Therefore, an alternative conversion rate needs to be determined. Further, because of the seriously distorted domestic price structures, particularly highly subsidized service prices, in FSU Republics, the ratio of the average price of non-traded goods to those of traded goods may be downward-biased considerably.

6. If some average relationships between domestic prices of traded goods and their international prices (proxy E in (3) above) and between resource costs for tradables and nontradables (proxy K in (2) above) are known, GNPs in international prices could still be estimated for these economies. The assumption that price elasticities of demands in these economies are very low simplifies the procedure. That is, when international prices are applied to tradables and service prices are adjusted for FSU Republics, there would be no need to impute quantity changes possibly responding to the hypothetical price changes.

34
<!-- Start of picture text --> oS<br>ar<br>teal ie<br>on eile iat ee<br>7 Si<br>rf<br>ar<br>1-2 3-4 5-6 1-8 9-10 11-12 13-14 15-16 17-18 19-20<br>Years since arrival in Switzerland<br>—-e-—- Male, baseline ——*-—- Female, baseline<br>—s— Male, education FE —s— Female, education FE<br><!-- End of picture text -->
than 50% for the majority who didn't even finish secondary school. In Mexico, too, the gender ratio of experience is less than 50%. In all cases, the experience disparity is smaller for young women, before they marry. Also in all cases the gender gap narrows substantially for the minority with higher education but it never completely disappears. By the age of 65, the average woman without a university degree in all these countries has accumulated 18-27 years of experience, while the average man has accumulated 3844.

In all three countries younger women who work earn almost as much as men (Table 4). However, earnings diverge as they age-the age-earnings profile is much steeper for men-perhaps because of the returns to experience. Prime age male earnings profiles rise 2-3% per year while female profiles rise 1-2%per year. Thus, by the time they reach age 50, women earn barely 60% as much as men per month worked, in most educational categories. For example, a Chilean man with a secondary degree age 46-50 earns US$535 monthly, while a comparable woman earns $326. The pattern is similar in Mexico, although absolute amounts are lower. In Argentina a male secondary school graduate in his late 40's earns US$1105 while a comparable female earns $666. (Our Argentinean data are from 1996-97 and use 1997 exchange rates. At 2002 exchange rates these amounts would be only 25-30% as high-much closer to Mexico. The choice of exchange rates should not affect gender ratios). While highly educated women work almost as much as men, their monthly earnings do not converge and, in fact, they diverge further in the highest educational category in Chile.'<sup>0</sup>

**Gender ratios in pension accumulations and monthly own-pensions**

> We now proceed to estimate the gender ratio of retirement savings and annuities under the new system (Table 5). In this section we discuss the pure DC plan, based on

> contributions by workers and employers. (In Mexico we exclude the government's contribution, the social quota). We would expect women's simulated retirement accumulations to be far lower than those of men, as a result of lower labor force participation and lower earnings while working. Converting these accumulations into an

> annuity, women's benefits will be further depressed by their greater longevity--but this is

> offset by the fact that married men who annuitize must purchase a joint annuity that

> covers their wife's life as well as their own. We would further expect women's annuities

16
<!-- Start of picture text --> the fourth income group. Meanwhile the per capita use of modern energy is quite modest until the<br>highest income group.<br>1 1<br>2 2<br>3 3<br>4 4<br>5 5<br>6<br>6<br>7<br>7<br>8<br>8<br>9<br>9<br>10<br>10<br>0 2 4 6<br>0 10 20 30 Energy consumption (kgOE/capita/month)<br>Energy consumption (kgOE/capita/month)<br>Fuelwood Non-fuelwood biomass<br>Fuelwood Non-fuelwood biomass Kerosene Electricity<br>Kerosene Electricity<br>Source: BIDS Survey (2004) Source: BIDS Survey (2004)<br>Figure 5: Total energy consumption by income Figure 6: End-use energy consumption by income<br>For end use or useful energy, the patterns reflect more the actual energy service that is received by<br>consumers. The role of electricity gains prominence due to its higher efficiency. Since electricity is not just<br>the most efficient form of energy sources available to the households, it also provides the widest range of<br>energy services. However, the level of end-use remains fairly constant until the fifth or sixth income<br>categories. The patterns for biomass energy also increase with income, but for useful energy it, for obvious<br>reasons, constitutes a lower percentage of aggregate energy consumption as income goes up.<br>These differences in patterns of energy consumption for useful versus total energy use are<br>very useful. These comparisons show that total energy use may not be the best measure of energy<br>poverty. Instead, it is the actual energy service that a household receives that matters. As is well<br>known, energy is not purchased as an end in itself, but to provide some kind of service such as<br>21<br>Income decile<br>Income decile<br><!-- End of picture text -->