rafmacalaba/datause-encoder-data huggingface dataset:
- 35 -

# The Kerry proposal (1986) 36/

This plan offers a more complex form of interest-rate relief. Senator Kerry advocates the creation of an International Debt Program (IDP), to be managed by the UN's International Trade and Finance Development Council. IDP would relate debt service to a given debtor's trade balance and combine interest forgiveness, debt service capping and debt rescheduling.

Debt-service capping would make payments a function of trade surpluses. If the surplus is 20 percent or less of a country's exports, debtservice payments would be limited to 20 percent of exports and increase proportionally, in line with the surplus. For countries with trade deficits, debt would automatically be rescheduled according to a progressive scale, based on the ratio of the trade deficit to total exports. A deficit equal to 20 percent of exports would entitle the country to automatic rescheduling of 20 percent of its debt, etc., subject to a ceiling of 40 percent. All rescheduled amounts would be capitalized and entitle the debtor to interest relief. For the first five years all interest on rescheduled debt would be paid by the IDP. For the next ten years, interest would be capped at 3 percent, with the remain'er paid by the IDP. Finally eligible debtors would establish a reserve against future default, equivalent to 10 percent of the relief they have received.

A major difference between the Rohatyn and Bradley proposals and the Kerry plan is that, under the latter, commercial lenders would not suffer a loss of revenue. Industrial nations would fund the IDP's disbursments in a manner proportional to the trade benefits they enjoy as a consequence of the debtors' relief. The difficulty of gaining public support for that cost feature is a major stumbling block.

# The Bank of **Nova** Scotia proposal (1987) 37/

This is one of the few debt plans made by a commercial bank that is also a creditor to HICs. It central feature is that the amount of interestrate relief would be set so that debt service could be a "reasonable burden". In each case, the amount of relief would be determined in light of the debtor's exports and GDP. Relief would be granted equally by all existing creditors, but would exclude trade credit.

Rather than being forgiven, the amounts by which original interest charges are reduced would accrue to lenders in the debtor's local currency at a pre-agreed exchange rate and would be available for conversion into equity investment in the debtor country. Finally, relief would be subject to a

# 10/17/86

36/ Wertman, Patricia, op.cit., CRS 10/17/86, pp 24-26.

37/ Bank of Nova Scotia, A proposal regarding developing-country debt. Mimeo,
holdings in the US. _Journal of Financial Economics_ , 87(3):535–555, 2008. URL `https:// EconPapers` _._ `repec` _._ `org/RePEc:eee:jfinec:v:87:y:2008:i:3:p:535-555` .

- Jarrad Harford, Sandy Klasa, and William F. Maxwell. Refinancing risk and cash holdings. _Journal of Finance_ , 69(3):975–1012, 2014. URL `https://EconPapers` _._ `repec` _._ `org/RePEc:bla:jfinan:v: 69:y:2014:i:3:p:975-1012` .

- Mark Hirschey. Intangible capital aspects of advertising and r&d expenditures. _Journal of Industrial Economics_ , 30(4):375–90, 1982. URL `https://EconPapers` _._ `repec` _._ `org/RePEc:bla:jindec:v: 30:y:1982:i:4:p:375-90` .

- Michael Jensen. Agency costs of free cash flow, corporate finance, and takeovers. _American Economic Review_ , 76(2):323–29, 1986. URL `https://EconPapers` _._ `repec` _._ `org/RePEc:aea:aecrev: v:76:y:1986:i:2:p:323-29` .

- Kathleen M. Kahle and Ren M. Stulz. Is the US public corporation in trouble? _Journal of Economic Perspectives_ , 31(3):67–88, 2017. URL `https://EconPapers` _._ `repec` _._ `org/RePEc:aea:jecper:v: 31:y:2017:i:3:p:67-88` .

- Wolfgang Keller and Hˆale Utar. International trade and job polarization: Evidence at the workerlevel. (22315), 2016.

- Lina M Khan. Amazon’s antitrust paradox. _Yale LJ_ , 126:710, 2016.

- Tim Koller. What is value-based management? _The McKinsey Quarterly_ , (3), 1994.

- Tim Koller, Marc Goedhart, and David Wessels. _Valuation: Measuring and Managing the Value of Companies_ . JohnWiley & Sons, 6 edition, 2017.

- Mordecai Kurz. On the formation of capital and wealth. _Stanford Institute for Economic Policy Research (SIEPR) Working Paper_ , 2017.

- Vojislav Maksimovic, Gordon Phillips, and Liu Yang. Do public firms respond to investment opportunities more than private firms? the impact of initial firm quality. NBER Working Papers 24104, National Bureau of Economic Research, Inc, 2017. URL `https://EconPapers` _._ `repec` _._ `org/ RePEc:nbr:nberwo:24104` .

34
|**Variable**|**Coefcient**|**Variable**|**Coefcient**|
|---|---|---|---|
|ln(Employment)|0.11**|Insurance|1.06**|
|FDI share [0,1]|0.92**|Auxiliary fn. activities|0.40**|
|Exporter, Yes=1|1.12**|Real estate activities|0.19**|
|**Sub-Sector. Base = Electricity, gas, and steam**||Renting of equipment|0.43**|
|Water|-1.03**|IT|0.50**|
|Sale of motor vehicles|1.59**|R & D|0.14**|
|Retail trade|1.13**|Other services|0.42**|
|Hotels and restaurants|-0.39**|**Year, Base = 2001**||
|Land transport|0.10**|2002|0.12**|
|Water transport|1.03**|2003|0.23**|
|Air transport|0.57**|2004|0.38**|
|Auxiliary transp. activities|0.78**|2005|0.52**|
|Post & telecom|1.12**|2006|0.62**|
|Financial intermediation|0.83**|2007|0.72**|
|N=501797||||

* _p <_ 0 _._ 05, ** _p <_ 0 _._ 01 Notes: Table reports labor productivity premium conditional on size, foreign ownership, sub-sector, and year. Regional fixed effects are included but not reported. Results are based on OLS regression with robust standard errors. The dependent variable is log of value added per worker deflated by sub-sector specific price deflator.

Table 1: Labor productivity premium depending on firm ownership, export status, subsector, and year

# **4 Data, methodology, and results**

## **4.1 Sample**

The data for the study come from several statistical statements annually submitted to the National Statistics Office (Derzhkomstat) by all commercial firms in the country. The data are restricted and not available for public use. The sample covers seven years from 2001 to 2007. The total number of firms in the data set exceeds 400,000 per year and covers all sectors except budgetary organizations (public schools, public hospitals, museums, etc.) and banks. We start with the sample of manufacturing firms (NACE Section “D”) which never switched to another sector over the period of study. Since the Sectoral Expenditures Statement, required to construct the firm-specific service liberalization index, is submitted by only relatively large firms, our sample is restricted to the firms with more than 150 employees on average. We further excluded observations with zero or negative output, capital stock or employment assuming that they indicated non-operational firms in a year. Based 13
outcomes. Finally, biases may arise as a result of interviewing certain individuals and not others.

The literature provides some empirical evidence on the impact of having one household member provide information about others. Fisher et al. (2010) find that when husbands alone are interviewed in Southern Malawi, the information can be used to accurately calculate aggregate poverty measures, including the poverty headcount and poverty gap indexes. However, the reported determinants of poverty differ depending on whether the wife’s estimate of her own income is used to calculate household income. In this context, husbands underestimate their wife’s income, on average, and do not accurately estimate the total household income in most households (Fisher et al., 2010). Relatedly, Chen and Collins (2014) use cross-reports of spouses’ incomes and expenditures to assess the source of information asymmetries and their impact on farm production and efficiency in Ghana. Spouses have very poor estimates of each other’s income and expenditures, both in total and by type of good.

On labor, Bardasi et al. (2011) find that female labor statistics do not differ by self/proxy reporting, but that proxy responses produce much lower male employment rates than do self-reports. Similarly, Dammert and Galdo (2013), and Janzen (2015) report significant effects of proxy respondents on child labor estimations in Peru and Tanzania, respectively, Analyzing LSMS-ISA data from Malawi and Nigeria, Palacios-Lopez et al. (2017) find opposite effects of respondent gender on reported female labor share in crop production – 7 percentage points higher if the respondent is female in Malawi, lower but non-significant in Nigeria.

These studies suggest that proxy respondents may not provide adequate information on the income and labor inputs of others, including on that of their spouse. Subsequently for asset ownership and control, the key methodological question is whether it is sufficient to interview one household member regarding the assets owned by all household members. Or perhaps a better formulation of this question is, under what circumstances is it sufficient to interview one household member versus multiple individuals? And if multiple, which household members should be interviewed? The answers depend on the analytical objective, as detailed below.

Many large-sample household surveys, including the LSMS-ISA, end up collecting individually disaggregated data regarding the ownership of assets of all household members, often through proxy respondents. Table 2 provides, by sex, the module-specific incidence of use of proxy respondents for information on individuals 18 years of age or older in selected LSMS-ISA-supported household surveys. Even for a household survey program that has achieved remarkable success on many fronts since 2009, the level of 21
70

# **Table A1b**

## **Dependent Variable: Growth Rate of Real GDP Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A1b.1 **|**Reg A1b.2 **|**Reg A1b.3 **|**Reg A1b.4 **|**Reg A1b.5 **|**Reg A1b.6 **|**Reg A1b.7 **|**Reg A1b.8 **|**Reg A1b.9 **|**Reg A1b.10 **|**Reg A1b.11**|**Reg A1b.12**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2875*****|**0.2335*****|**0.2035*****|**0.2118*****|**0.2820*****|**0.2381*****|**0.2195*****|**0.2355*****|**0.2047*****|**0.1956*****|**0.2265*****|**0.2194*****|
|**Growth of Labor**|**0.5356*****|**0.6874*****|**0.5648*****|**0.5867*****|**0.2662***|**0.3181****|**0.3043****|**0.6845*****|**0.5751*****|**0.5438*****|**0.7019*****|**0.6799*****|
|**Initial GDP (1960)**|**-2.2136*** **|**-3.8021*** **|**-4.3319*** **|**-3.8384*** **|**-3.6178*** **|**-3.7009*** **|**-4.0798*** **|**-3.6342*** **|**-3.9829*** **|**-4.3185*****|**-3.5668*****|**-3.8537*****|
|**HC25**||**0.4127**|**0.4763**|**3.7916***|||||||||
|**HC25 with**<br>**educational returns**|||||**-0.0648**|**-0.1836**|**1.3407***||||||
|**HC25 with extended**<br>**educational returns**||||||||**0.1915**|**0.0047**|**1.5053***|**0.1238**|**1.6068***|
|**Sq of HC25**||||**-0.9286****|||||||||
|**Sq of (HC25 with**<br>**educational returns)**|||||||**-0.2858****||||||
|**Sq (HC25 with extended**<br>**educational returns)**||||||||||**-0.2950****||**-0.2960***|
|**Log (Educational Quality)**|||**0.5926**|**0.3671**||**1.1302**|**1.1645***||**0.7218**|**0.5857**|||
|**Log (Educational Quality**<br>**extended)**|||||||||||**0.4920**|**0.3574**|
|**Educational Quality (BC)**|||||||||||||
|**Log (Patents)**|**0.1479****|**0.1981*****|**0.1854****|**0.1955*****|**0.1200**|**0.1100**|**0.0864**|**0.2057*****|**0.1987*****|**0.1847****|**0.2027*****|**0.1867****|
|**Phones**|**0.0034*****|**0.0040*****|**0.0033*****|**0.0035*****|**0.0033*****|**0.0023****|**0.0026****|**0.0044*****|**0.0036*****|**0.0037*****|**0.0040*****|**0.0040*****|
|**Constant**|**0.9257***|**0.3989**|**-1.1135**|**-3.2783**|**2.4733*****|**-1.2193**|**-2.7443**|**0.6366**|**-1.0248**|**-1.8668**|**-1.0260**|**-1.8733**|
|**R squared**<br>|**0.6194**<br>|**0.5777**<br>|**0.5952**<br>|**0.6078**<br>|**0.6936**<br>|**0.7116**<br>|**0.7236**<br>|**0.5778**<br>|**0.5928**<br>|**0.6049**<br>|**0.5817**<br>|**0.5933**<br>|
|**Number of Countries**<br>*, **, *** denotes significance at th<br>Robust standard errors obtained u|**107**<br>e 90, 95 and 9<br>sing White's c|**90**<br>9 percent con<br>orrection for h|**84**<br>fidence level r<br>eteroscedasti|**84**<br>espectively<br>city.|**52**|**52**|**52**|**90**|**84**|**84**|**90**|**90**|
# **The Labor Market Effects of Financial Crises:**

# **The Role of Temporary Contracts in Central and Western Europe**<sup>**1**</sup>

Siddharth Sharma, _The World Bank_ Hernan Winkler, _The World Bank_ JEL Classification: J23, G01, J46, J41 Keywords: Financial crises, Labor Markets, Temporary Contracts, Employment Protection, Europe

> 1 We thank David Gould, Martin Melecky and participants of several World Bank workshops, where previous versions of this paper were presented, for their comments. We are also grateful for suggestions received from anonymous reviewers.
Economic Analysis.” _American Economic Review_ 81(5): 1407-18.

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- Park, Albert, Fang Cai and Yaohui Zhao. 2006. “The Informalization of the Chinese Labor Market.” Background Paper for the China Poverty Assessment. Washington: The World Bank.

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- Stock, James H. and Motohiro Yogo. 2005. “Testing for Weak Instruments in Linear IV Regression.” In _Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg_ , ed. James H. Stock, Donald W. K. Andrews, 80-108. Cambridge and New York: Cambridge University Press.

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31
Malawi). This heterogeneity reflects the fact that some countries are more agrarian than others and that some are large net-consumers, others are large net-producers, and yet others either consume and produce little cereals or do so in similar magnitudes so that the net effects tend to cancel out. A similar story can be told about the pro-poor bias of liberalization. There are countries where cereals liberalization would be clearly pro-poor (Tanzania, Bhutan, Guinea Bissau, Ecuador) and countries where it would be clearly pro-rich (Vietnam). There are also cases where the liberalization would be neutral and this can in turn be because of little direct consumption and production of grains (Armenia, Georgia) or because of the offsetting consumption and income effects (Madagascar, Nigeria).

As this analysis illustrates, the HIT data and our framework enable researchers to examine and exploit the extensive household heterogeneity in incomes and expenditures in many dimensions. There are some limitations to the framework that are important to note for accurate interpretation of our results. To make it operational, we need to impose some structure, in particular perfect competition, constant returns to scale, and product homogeneity. Since we work with household surveys and with an agricultural household model `a la Deaton, there are some limitations in the scope of the analysis as well. The household surveys do not collect reliable information on the returns to capital and on profits and often fail to capture very rich households. Also, there are marked differences between consumption and production aggregates from the household surveys and those from the national accounts. As a result, there might be some discrepancies between the household welfare effects from the HIT data and the aggregate welfare effects stemming from more general trade models. In addition, some relevant impacts, such as heterogeneous varieties, two-way trade or labor and investment effects, may require additional modeling assumptions as well as additional data (such as demand and supply elasticities and so on). In any case, most of the discussion about poverty, inequality and household welfare is typically based on household surveys and these surveys and the HIT data set are thus a natural starting point for analyzing the distributional impacts of trade policy.

13
# **Table A19: Second stage: Log Refugees at 100km with decay function (i.e. index generated using 1)distance to closest cluster, 2)square of distance to closest cluster** **<u>and 3) square root of distance to closest cluster) instrumented by distance-based IV.</u>**

|||||Lo|g of welfare|(consumption|aggregate per a|dult equivalen|t)||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (100km) (with distance)|0.05464|0.05431|0.12505*|0.11830*|||||||||
||(0.07485)|(0.07471)|(0.07173)|(0.06503)|||||||||
|Log of refugees (100km) (with sq distance)|||||0.07919|0.07872|0.17786**|0.16863**|||||
||||||(0.10636)|(0.10619)|(0.09069)|(0.08452)|||||
|Log of refugees (100km) (with sqrt distance)|||||||||0.04762|0.04733|0.10911*|0.10304*|
||||||||||(0.06577)|(0.06565)|(0.06608)|(0.05957)|
|HH head age||0.00244|0.01173**|-0.00202||0.00256|0.01198**|-0.00048||0.00240|0.01164**|-0.00267|
|||(0.00511)|(0.00563)|(0.01666)||(0.00511)|(0.00562)|(0.01663)||(0.00512)|(0.00563)|(0.01669)|
|Age squared||-0.00003|-0.00008|0.00001||-0.00003|-0.00009|-0.00001||-0.00003|-0.00008|0.00001|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00407|0.02267|-0.08058||0.00444|0.02438|-0.07114||0.00398|0.02213|-0.08356|
|||(0.03472)|(0.03776)|(0.06331)||(0.03466)|(0.03763)|(0.06380)||(0.03473)|(0.03779)|(0.06331)|
|Household size|||-0.02625***||||-0.02637***||||-0.02622***||
||||(0.00539)||||(0.00540)||||(0.00539)||
|Married monogamously|||-0.42458***||||-0.42368**||||-0.42470***||
||||(0.16461)||||(0.16473)||||(0.16443)||
|Married polygamously|||-0.37064**||||-0.36922**||||-0.37097**||
||||(0.17213)||||(0.17228)||||(0.17196)||
|Separated (divorced/widowed)|||-0.41299**||||-0.41070**||||-0.41350**||
||||(0.17258)||||(0.17253)||||(0.17245)||
|Completed primary sch|||0.22308***||||0.22343***||||0.22286***||
||||(0.03704)||||(0.03703)||||(0.03702)||
|Commercial farming|||0.10497||||0.10622||||0.10509||
||||(0.08958)||||(0.08944)||||(0.08955)||
|Wage employment|||-0.00101||||-0.00059||||-0.00098||
||||(0.04835)||||(0.04831)||||(0.04833)||
|Non-agricultural self-employment|||0.09133**||||0.09126**||||0.09127**||

29
B-Brazil; Group C-other countries in Central/South America not in Groups A or B; Group D-countries in North America; Group E-ex-English colonies in Sub-Saharan Africa; Group F-ex-French and ex-Belgian colonies in Sub-Saharan Africa; Group G-other Sub-Saharan African countries not in Groups E or F; Group H-Western European countries including Turkey; Group I-Eastern European (and ex-Soviet Bloc) countries; Group J-Former Soviet Union countries; Group K-Middle Eastern and Arab North African countries; Group L- countries of Asia and Oceania (including Australia and New Zealand).

## **_Income Groups_**

> The countries were also divided into the following three income classes for the selection process, with the classification following that used by the World Bank: High Income, Upper Middle Income, and all others.

# **References**

The word "processed" describes informally reproduced works that may not be commonly available through library systems.

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_27_
3 in terms of immigration policies as well as related measures that contribute to their successful implementation.

The paper is organized as follows. Singapore's and Malaysia's immigration policies are laid out in section II, followed by a discussion in section III of additional measures that complement immigration policy, together forming a comprehensive and effective strategy to manage foreign labor. The fourth section describes evidence of trends in the composition of labor with respect to citizenship, and section V presents the results from regression analysis on Malaysian household data to assess the determinants of earnings. The paper then explores additional factors contributing to the success of foreign labor policy in Singapore and Malaysia, as well as policy shortcomings, evidenced by large informal economies staffed mostly by illegal aliens (sections VI and VII, respectively). Finally, section VIII concludes the analysis with a discussion of the lessons for GCC countries with respect to the policy experience of Singapore and Malaysia.

# **II. Foreign labor management policies in Singapore and Malaysia**

Although Singapore and Malaysia have relatively smaller foreign shares of total employment compared to the GCC experience, they nevertheless play host to vast numbers of expatriates. Malaysia's large formal economy employs 1.2 million foreigners (plus an estimated 800,000 illegal aliens), representing a moderate but still significant 13 percent of the labor force (according to the Malaysian Ministry of Human Resources).1 More than one-fourth of Singapore's labor force consists of legal foreign workers, numbering an estimated 500,000 in 1997 (Wong, 1997; _Economic Survey of Singapore,_ 1996). Both Singapore and Malaysia manage their large expatriate populations through complex and tightly regulated immigration policy.

# Singapore

Singapore's foreign labor management relies on immigration regulations in the form of work permits. There are four different types of permits that control both the quantity and quality of labor entering Singapore. Permits are differentiated by skill level, sending country, permit duration, and sector of work, and a variable levy is charged according to classification. For example, a permit is granted to a

- I The foreign labor share including illegal workers is estimated at 20 percent.