rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

Foreign direct investment (FDI) has been increasing at an extraordinary speed during the past 20 years. In the second half of the last decade, world inflows grew at an annual rate of almost 40 percent, reaching $648 billion in 2004.<sup>1</sup> Foreign capital stocks (FCS) were multiplied by a factor of 5 between 1990 and 2004, rising from $1,770 billion in 1990 to almost $9 trillion in 2004.<sup>2</sup> An even larger increase was reported in developing countries, where stocks went from $364 billion to over $2,230 billion over the same period.

In particular, foreign direct investment inflows to Latin America and the Caribbean (LAC) continuously grew during the 1990s, up to almost half of total inflows into developing economies in 1999. In that year, FDI accounted for 25 percent of Latin America’s gross fixed capital formation.<sup>3</sup> Although there was a slowdown in these inflows at the beginning of the 21<sup>st</sup> century, by 2004 aggregate stocks in Latin America reached $600 billion dollars, about six times more than in 1990.<sup>4</sup> There is a growing concern that the growth in China and India may present a challenge to other developing countries. The low wages and the large populations of these countries may entice multinational enterprises to relocate their production facilities there. In fact, FCS in China grew at an amazing speed, from $20 billion in 1990 to $245 billion in 2004, the largest FCS in the developing world. At the same time, stocks in India increased from $1.6 to almost $40 billion over the same period.

> 1 UNCTAD 2005

> 2 Throughout the paper we refer to stocks of foreign direct investment (FDI) as foreign capital stocks (FCS).

> 3 UNCTAD 2004 and UNCTAD 2005

> 4 These figures were taken from UNCTAD foreign direct investment database. We do not include Bermuda, Cayman Islands and Virgin Islands in the Latin American figures as part of LAC.

2
explodes later this century, who is going to provide goods and services for all the retirees? There are many elements to a solution, not least allowing expanded immigration from the developing world, with its much younger population. Regardless, one desirable element has to be for the industrialised countries to save abroad by running large current-account surpluses vis-à-vis the developing world. These cumulated surpluses, while facilitating much-needed investment in poorer countries right now, could later be drawn down as the baby-boomers stop working.” (Rogoff, 2002). But repeated financial crises (both when capital has moved to poor countries or to rich countries) have made this an inefficient and even infeasible option, quite apart from the fact that this is a very indirect way of addressing what is at heart a demographic problem as Rogoff himself acknowledged.

<!-- Start of picture text --> Dependency Ratio, OECD vs. South Asia, 1950-2050<br>1950 1970 1990 2010 2030 2050<br>Year<br>OECD_ratio SAsia_ratio<br>total_ratio<br>80<br>70<br>60<br>Total Dependency Ratio 50<br>40<br><!-- End of picture text --> Source: Ozden and Parsons (2012) There is, of course, significant heterogeneity among industrial and developing countries. But the contrasted noted above will prevail for a sufficiently large groups of countries in the foreseeable future. The figure above presents a dynamic picture of the dependency ratio for the rich OECD nations as well as for the starkly contrasting situation in the countries of South Asia.<sup>15</sup> Whereas in 1990 the dependency ratio for the OECD was under 50, in 2050 this number is projected to be over 70. The corresponding ratio for South Asia has been falling rapidly since the late 1960s, is expected to continue to fall until 2040 and then gradually increase.

There are significant benefits to rich countries from getting more young immigrants. For example, the United States Labor force is currently about 140 million, and annual immigration of workers of 1.4 million would represent a direct increase of one percent in the labor force growth, which would translate into higher GDP growth of 0.6 percent. Moreover, Kremer and Watt

> 15 Differences are also evident within South Asia, since Sri Lanka has completed its transformation while Afghanistan is yet to start its transition.

12
**<u>Table 1: Summary of actual NREGS participation by household poverty status</u>**

|||**Phase 1**||**Ph**|**ase 2**|**Phase 3**|
|---|---|---|---|---|---|---|
||**2006**|**2007**|**2008**|**2007**|**2008**|**2008**|
|**Having a job card**|||||||
|All households|0.438|0.492|0.503|0.322|0.356|0.399|
|Poorest of poor households|0.492|0.537|0.547|0.295|0.338|0.429|
|<br>Poor households|0.493|0.540|0.554|0.422|0.463|0.470|
|Not-so-poor households|0.350|0.427|0.436|0.252|0.273|0.293|
|<br>Nonpoor households|0.233|0.288|0.302|0.266|0.269|0.146|
|**Participation in NREGS work**|||||||
|All households|0.049|0.328|0.414|0.035|0.298|0.188|
|Poorest of poor households|0.056|0.381|0.457|0.038|0.286|0.211|
|Poor households|0.045|0.355|0.460|0.047|0.382|0.226|
|Not-so-poor households|0.052|0.260|0.356|0.021|0.230|0.128|
|Nonpoor households|0.060|0.056|0.102|0.051|0.077|0.000|
|<br>Number of observations|2,397|2,397|2,397|838|838|751|
|**Female participation (% of time)**|||||||
|<br>All households|0.536|0.590|0.631|--|0.599|0.503|
|Poorest of poor households|0.446|0.607|0.650|--|0.632|0.520|
|<br>Poor households|0.597|0.580|0.606|--|0.586|0.415|
|Not-so-poor households|0.655|0.595|0.633|--|0.639|0.650|
|<br>Nonpoor households|0.403|0.456|0.633|--|0.411|0.444|
|**Female wages received (Rs/day)**|||||||
|All households|84|79|81|--|52|84|
|Poorest of poor households|78|80|82|--|53|87|
|Poor households|82|79|81|--|54|78|
|Not-so-poor households|99|79|82|--|48|84|
|Nonpoor households|36|78|76|--|47|91|
|**Male wages received (Rs/day)**|||||||
|All households|80|79|81|--|43|83|
|Poorest of poor households|85|82|82|--|46|83|
|<br>Poor households|77|76|81|--|47|83|
|Not-so-poor households|77|78|80|--|34|80|
|<br>Nonpoor households|67|79|79|--|32|75|
|<br>**Total amount received per household**|**(Rs)**||||||
|All households|796|2,623|4,103|1,907|1,540|995|
|Poorest of poor households|710|2,674|4,335|2,045|1,558|1,065|
|<br>Poor households|717|<br>2,665|<br>4,182|<br>1,728|<br>1,703|<br>962|
|Not-so-poor households|973|2,360|3,687|1,887|1,314|925|
|<br>Nonpoor households|1,342|2,849|3,027|2,480|1,147|639|
|Number of observations|117|787|993|29|250|141|

_Source:_ Authors’ computation from household survey and administrative data. _Note:_ --: not reported due to small number of observations.

16
## _12_ **_Polih Fiscal Cris_**

somewhat tentatively, that there is not much of a direct role for char'ges in the cost of credit in explaining the 1991 profit collapse.

# F. Taxes and subsidies

In Poland as in other transition countries, subsidies have been cut drastically in recent years. However, the bulk of the Polish subsidy cuts took place before January 1990. Net indirect taxes from the enterprise sector (turnover taxes minus subsidies) fell further in 1991, but the change was relatively small; net taxes as a percentage of sales went from 1.1% the previous year to 2.9% (Table 1). In any case the net effect on profits of subsidy cuts/net tax increases would depend on what happened to prices. For example, subsidy cuts could lead to higher profits if price controls are lifted and furms exercise monopoly power.

# _G._ 'Extraordinary profits and losses'

This is a mystery item in the enterprise financial accounts. It includes, for example, capital gains/losses on hard currency holdings, interest on overdue credits, capital gains on explicit inventoiy revaluations, etc. In 1989 it was very large (8% as a percent of sales), apparently largely because of capital gains on hard currency holdings. In 1990 it was much smaller but still positive, and in 1991 it became significantly negative. It is not clear how to interpret this, however, and more information is needed here.'" Table 4 summarizes the above results: Table 4: The 1991 Collapse in Proritability - Sources

|Fall in HCA profitability|_-16.5_|
|---|---|
|Estimated effects of:||
|Increase in amortization allowances<br>Increase in ULC|-3.5<br>-4.7|
|Decrease ir. inflation bias|-8 to -10|
|Changes in relative import/export prices|-1 to -2|
|Total:|-17 to -20|

10. For example, **as** inflation declined, inventory revaluations carried out by enterprises would hav resulted (c.p.) in smaller (paper) capital gains snd smaler extraordinary profits. But this would not affect HCA profit over a longer period, since the effect of the revaluation would be to bring forward the inflation bias that would be realized anyway when the revalued inventories were later sold.
# **Table 2. General government spending, 2016**

||Administrati|ve data<br>f|Included<br>analys|in the<br>is<br>f|
|---|---|---|---|---|
||mil RSD|% o<br>GDP|mil RSD|% o<br>GDP|
|Total expenditures|1,899,719.70|47.3|||
|**Social spending**|991,743.00|24.7|815,132.00|20.22|
|**Contributory social insurance benefits**|517,018.58|12.8|440,122.90|10.90|
|Pensions|494,209.89||456,101.47||
|Old-age pensions|325,582.75|8.1|304,997.43|7.50|
|Family pensions|82,693.56|2.0|63,679.24|1.58|
|Disability pensions|85,933.57|2.1|67,591.23|1.67|
|Agricultural pensions|||19,166.52|0.46|
|Survivors pensions for children|||667.05|0.016|
|Pension from abroad|||45,774.39|1.13|
|Unemployment benefit|12,545.28|0.3|3,958.44|0.09|
|Sickness leave|10,263.42|0.3|3,544.66|0.08|
|**Non-contributory benefits**|97,076.91|2.3|51,363.20|1.30|
|Financial social assistance|14,276.85|0.3|12,356.35|0.30|
|Foster care benefits|3,408.62|0.1|895.38|0.02|
|Caregivers allowance|9,840.18|0.2|11,679.35|0.28|
|War Veterans benefits|13,557.44|0.3|1,305.09|0.03|
|Parental allowance|7,221.66|0.2|3,025.86|0.12|
|Child allowance|12,446.15|0.3|8,100.46|0.19|
|Wage compensation during the maternity<br>leave|30,435.53|0.7|10,012.08|0.23|
|Heating allowance|||316.15|0.01|
|Utility allowance|||324.35|0.007|
|Education allowance|||2,387.43|0.05|
|One-off assistance|||378.41|0.01|
|Other|5,890.49|0.1|582.30|0.01|
|Compensation for the loss of employment|||4,228.26|0.09|
|Severance payment|||403.79|0.008|
|**Education**|111,138.26|3.0|111,138.26|3.03|
|Pre-school education|2,037.64|0.1|2,037.64|0.05|
|Primary education|69,890.00|1.7|69,880.00|1.74|
|Secondary education|25,636.21|0.6|25,636.21|0.63|
|Tertiary education|25,189.33|0.6|25,189.33|0.61|
|**Health**|212,508.95|5.9|212,508.95|5.90|
|Health Insurance Fund|212,508.95|5.9|212,508.95|5.90|

9
Table 13A. (Part 1) 9-sector compensation of employees by skill and gender available in the ILFS (in current billions of Shillings)

||Young<br>||Females|(withouty<br>Thi|oungwork<br>Pf|ers)<br>All 4||Males(wi|thoutyoun<br>Thi|gworker<br>Pf|s)<br>All 4|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||workers|Unskilled|Laborers|ecn-<br>cians|roe-<br>ssionals|occu-<br>pations|Unskilled|Laborers|ecn-<br>cians|roe-<br>ssionals|occu-<br>pations||
|Agriculture/Forestry/Fishing|86.654|5.387|435.371|0.452|0.000|441.211|42.055|732.564|1.591|0.627|776.837|1304.702|
|Mining&quarrying|0.459|0.037|1.427|0|0.116|1.579|1.767|5.479|0.409|0.000|7.655|9.694|
|Manufacture|0.691|1.474|12.977|0.752|0.418|15.621|14.401|72.441|7.213|1.379|95.434|111.745|
|Electricity. Gas and water|0|0|0.634|0|0.044|0.678|0.286|10.267|1.048|2.383|13.984|14.662|
|Construction|0.331|0.369|1.741|0.034|0.000|2.144|7.167|76.318|1.805|0.522|85.813|88.288|
|Trade|5.395|71.068|92.754|5.758|0.516|170.096|60.252|249.482|30.183|4.258|344.175|519.666|
|Transport & Communication|0.048|0.896|8.577|1.144|0.963|11.580|4.935|79.301|6.905|6.504|97.645|109.274|
|Rest of services|1.090|21.795|33.987|41.711|5.904|103.397|28.017|134.223|149.645|37.988|349.872|454.358|
|Finance. Insurance & Business|0.000|0.872|4.175|0.000|1.107|6.154|0.886|15.240|7.727|15.126|38.978|45.133|
|Personal service|1.090|20.923|29.811|41.711|4.797|97.242|27.131|118.983|141.918|22.862|310.893|409.225|
|ALL SECTORS|94.668|101.025|587.467|49.852|7.962|746.306|158.880|1360.075|198.798|53.662|1771.415|2612.389|

Table 13A. (Part 2) 9-sector compensation of employees by skill and gender available in the ILFS (in percentage)

||Young<br>||Female|s (without<br>|young wor<br>|kers)<br>||Males (|without y<br>|oung work<br>|ers)<br>|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||workers|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|All 4 occu-<br>pations|Unskilled|Laborers|Techni-<br>cians|Profe-<br>ssionals|All 4 occu-<br>pations||
|Agriculture/Forestry/Fishing|3.32|0.21|16.67|0.02|0.00|16.89|1.61|28.04|0.06|0.02|29.74|49.94|
|Mining&quarrying|0.02|0.00|0.05|0.00|0.00|0.06|0.07|0.21|0.02|0.00|0.29|0.37|
|Manufacture|0.03|0.06|0.50|0.03|0.02|0.60|0.55|2.77|0.28|0.05|3.65|4.28|
|Electricity. Gas and water|0.00|0.00|0.02|0.00|0.00|0.03|0.01|0.39|0.04|0.09|0.54|0.56|
|Construction|0.01|0.01|0.07|0.00|0.00|0.08|0.27|2.92|0.07|0.02|3.28|3.38|
|Trade|0.21|2.72|3.55|0.22|0.02|6.51|2.31|9.55|1.16|0.16|13.17|19.89|
|Transport & Communication|0.00|0.03|0.33|0.04|0.04|0.44|0.19|3.04|0.26|0.25|3.74|4.18|
|Rest of services|0.04|0.83|1.30|1.60|0.23|3.96|1.07|5.14|5.73|1.45|13.39|17.39|
|Finance. Insurance & Business|0.00|0.03|0.16|0.00|0.04|0.24|0.03|0.58|0.30|0.58|1.49|1.73|
|Personal service|0.04|0.80|1.14|1.60|0.18|3.72|1.04|4.55|5.43|0.88|11.90|15.66|
|ALL SECTORS|3.62|3.87|22.49|1.91|0.30|28.57|6.08|52.06|7.61|2.05|67.81|100.00|

Sources: Author's own elaboration based on the ILFS (NBS, 2002a). The information merges all the data used in Table 6 in this Appendix with the occupations by gender and sector (Table 10) and the mean monthly wage of paid employees by occupation and gender (Table 9).

44
Finally, Alami estimated a dynamic error correction model for the dollarization ratio. The results showed that demand for foreign currency deposits varies with changes in the interest rate differential and not the exchange rate, which implies that the reason behind holding foreign currency denominated deposits is related to portfolio shifts and not to currency substitution, i.e., Egyptian residents substitute the currency in an attempt to preserve the value of money rather than using it as a medium of exchange. The empirical evidence in this paper suggested that foreign currency denominated deposits will have no significance when studying money demand as a monetary instrument.

Handy (1999) estimated a standard money demand function to measure the impact of inflation on money demand in Egypt using annual data during the period 1970/71–1996/97. Nominal monetary aggregate was regressed on the Consumer Price Index (CPI), real gross domestic product index, inflation rate, average interest rate measured in local currency, and an equivalent offshore interest rate using the augmented ARDL procedure. To check the stability of the money demand function, the monetary aggregate was identified first as currency in circulation, then as domestic liquidity, excluding foreign currency deposits, and finally, as total liquidity. The results indicated that: (1) money demand in Egypt is strongly related to price level, real output, and inflation; (2) in the second half of 1980s, there was a tendency towards dollarization that took the form of foreign currency deposits; and (3) the two forms of money defined as currency in circulation and total liquidity were robust over the period. Thus, targeting money demand should not be regarded when setting monetary measures in Egypt.

El-Sheikh (2002) estimated demand function of M1 using quarterly data covering the period 1960– 1973 using the maximum likelihood technique. M1 was regressed on the expected real income, seasonal income variations, a set of relevant yields, and a set of institutional, distributional, and war variables. The following variables were found to be significant in determining M1 demand; saving deposit yields, post-office deposit yields. The study concluded with four important findings related to the estimation of the money demand function: First, the estimate of the elasticity of M1 with respect to seasonal income was far below unity, while the corresponding estimate with respect to expected income was far higher than unity. The estimate of the elasticity of M1 with respect to long run seasonal income is in line with those estimated by Baumol (1952) and Tobin (1956). ElSheikh was able to differentiate between holding the currency to preserve the value of the money 14
The authors tested the proposed model using FX reserve and external debt data from seven countries—Albania, Ghana, the former Yugoslav Republic of Macedonia, South Africa, the Republic of Korea, Tunisia and Uruguay. Among the ones disclosing currency composition of international reserves and external debt portfolio, the sample countries were selected based on three main criteria, including broad regional representation; issuance of foreign currency debt; and minimum reserves benchmark of three months of imports and short-term foreign currency debt service.<sup>6</sup> The objective of the paper was to explore the impact of an overarching ALM strategy and integrated approach on the efficient management of FX risk. The outcome of this process is several policy recommendations on ways to minimize the risk of FX mismatches and increase the return on public FX reserves.

This paper is comprised of six sections. Section II provides a brief overview of the sovereign asset and liability management approach. Section III discusses the challenges of managing FX risk and opportunities under a joint ALM approach. Section IV assesses the status of FX risk in the selected country sample. Section V measures the impact of a joint ALM approach on portfolio optimization. Finally, section VI offers policy recommendations that account for governance and institutional constraints.

# **II. Sovereign Asset and Liability Management**

The Revised Guidelines for Public Debt Management recommends establishing and executing a debt management strategy that enables a government to raise required amount of funds at the lowest possible cost over the medium to long term with a prudent level of risk. Key elements of this approach include the identification of the preferred composition of a debt portfolio, including the share of debt denominated in foreign currency. The structure of preferred debt portfolio and the degree of risk exposure may differ based on the size and types of debt portfolios and the level of risk aversion of the decision makers.

Identifying the appropriate share of debt denominated in foreign currency is a multi-step process. First, a debt manager must define the share of foreign currency debt given the inability of issuance of desired amount of debt with local currency (Melecky, 2007). Having identified the level of local currency debt, the debt manager decides the currency composition of the FX debt portfolio, ideally, by finding a basket of currencies that has the lowest variance with local currency.

Emerging markets hold a much larger share of debt denominated in foreign currency than advanced economies, largely because the smaller size of domestic capital markets constrains their ability to issue debt in local currency (Figure 1).

> 6 Countries with floating exchange rate regimes, such as Uruguay, South Africa and Albania, are less likely to use reserve assets to execute exchange rate interventions. Others, such as Tunisia, with its crawling peg, or FYR Macedonia, with its currency stabilized against the EUR, may require additional FX reserves to maintain this flexibility .

6
PRE WorkinaPager Series

||\|~~~~~~~~~ALhor|12Lforpae|Contact|
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|WPS730|Wage and Employment Policies in<br>Czechoslovakia|Luis A. Riveros|July 1991|V. Charles<br>33651|
|WPS731|Efficiency Wage Theory, Labor<br>Markets, and Adjustment|Luis A. Riveros<br>Lawrence Bouton|July 1991|V. Charles<br>33651|
|WPS732|Stabilization Programs in Eastern<br>Europe: A Comparative Analysis of<br>the Polish and Yugoslav Programs<br>of 1990|Fabrizio Coricelli<br>Roberlo de Rezende Roch|July 1991<br>a|R. Martin<br>39065|
|WPS733|The Consulting Profession in<br>Developing Countries:AStrategy for<br>Development|Syed S. Kirmani<br>Warren C. Baum|July 1991|INUDR<br>33758|
|WPS734|Curricular Content. Educational<br>Expansion, and Economic Growth|Aaron Benavot|July 1991|C. Cristobal<br>33640|
|WPS735|Traditional Medicine in Sub-Saharan<br>Africa: Its Importance and Potential<br>Policy Options|Jocelyn DeJong<br>|July 1991|0. Nadora<br>31091|
|WPS736|Wages and Employment in the<br>Transition to a Market Economy|Simon Commander<br>Fabrizio Coricelli<br>Karsten Staehr|August 1991|0. Del Cid<br>39050|
|WPS737|External Shocks, Adjustment<br>Policies, and Investment in a<br>Developing Economy: Illustrations<br>from a Forward-looking<br>CGE Model<br>of the Philippines|Delfin S. Go|August 1991||
|WPS738|Tax Competition and Tax<br>Coordination: When Countries<br>Differ in Size|Ravi Kanbur<br>Michael Keen|August 1991||
|WPS739|Managing Financial Risks in Papua<br>New Guinea: An Optimal External<br>Debt Portfolio|Jonathan R. Coleman<br>Ying Qian|August 1991|J. Carroll<br>33715|
|WPS740|The Onchocerciasis Control Program<br>in West Africa: A Long-term<br>Commitmen**t**o Success|Bernhard H. Liese<br>John Wilson<br>Bruce Benton<br>Douglas Marr|August 1991||
|WPS741|When Does Rent-Seeking Augment<br>the Benefits of rDrice and Trade Refor<br>on Rationed Commodities? Estimate<br>for Automobiles and Color Television<br>in Poland|David Tarr<br>m<br>s<br>s|August 1991|D. Ballantyne<br>37947|
# **6.3 Heterogeneity across groups**

The effect of life expectancy at birth might differ across groups. We investigate this separately by male, female, urban and rural groups. Table 3 presents the group-specific results. The estimates are similar to those from the pooled sample. An additional year of life expectancy at birth adds 0.15 year of schooling for women, 0.11 year of schooling for men, and 0.13 additional year for both urban and rural residents. An additional year of parental life expectancy translates to a small increase in schooling for daughters and for children in both urban and rural markets.

Life expectancy at birth might affect the schooling decision of birth-cohorts differently across regions. Therefore, we extend the empirical exercise by seven regions based on the World Bank classification of economies based on income and region.<sup>17</sup> The results in table 4 reveal that although life expectancy effects are consistently positive and statistically significant, they vary in magnitude across regions. The coefficient of life expectancy at birth shrinks for all regions once we control for birth-cohort fixed effects. The estimates suggest that, compared to other regions, cohorts in the Middle East and North Africa spent more time in school in response to a rise in life expectancy at birth. Consistent with what we observe in survey specific estimates, the life expectancy effect is smaller for Eastern Europe and Central Asia. However, the life expectancy effect for South Asia is not significant after including birth-year specific fixed effect. The effect of parents’ life expectancy generally remains positive but the estimates lose significance in the smaller samples.

> 17 The World Bank classifies the developing economies into six regions: “East Asia and Pacific”, Eastern Europe and Central Asia”, “Latin America and Caribbean”, “Middle East and North Africa”, “South Asia” and “SubSaharan Africa”. We added to this the pool of industrialized economies into “High Income Economies”. For the World Bank classification please see http://data.worldbank.org/about/country-and-lending-groups.

17