rafmacalaba/datause-encoder-data huggingface dataset:
|**Tabl**|**e B5: Rule of Law**|
|---|---|
|**Code**|**Concept Measured**|
|**Repr**<br>|**esentative Sources**<br> "" ""|
|**DRI**|_Losses and Costs of Crime_: A 1-point increase on a scale from 0 to 10 in crime during any 12-month period.|
||_Kidnapping of Foreigners_: An increase in scope, intensity, or frequency of kidnapping of foreigners that reduces the GDP growth rate by 1%|
||during any 12-month period.|
||_Enforceability of Government Contracts_: A 1 point decline on a scale from "0" to "10" in the enforceability of contracts during any 12-month<br>eriod|
||p.<br>_Enforceability of Private Contracts:_A 1-point decline on a scale from "0" to "10" in the legal enforceability of contracts during any 12-month<br>|
||period.|
|**EIU**|Violent crime|
||Organized crime|
||Fairness of judicial process|
||Enforceability of contracts|
||Speediness of judicial process|
||Confiscation/expropriation|
|**GCS**|Common crime imposes costs on business|
||Organized crime imposes costs on business|
||Quality of Police|
||The judiciary is independent from political influences of members of government, citizens or firms|
||Legal framework to challenge the legality of government actions is inefficient|
||Intellectual Property protection is weak|
||Protection of financial assets is weak|
||Tax evasion|
|**GWP**|Confidence in the police force|
||Confidence in judicial system|
||Have you been a victim of crime?|
|**HER**|Property Rights|
|**HUM**|Independence of Judiciary|
|**IPD**|Respect for law in relations between citizens and the administration|
||Security of persons and goods|
||Organised criminal activity (drug-trafficking, arms-trafficking, etc.|
||Importance of the informal economy|
||Importance of tax evasion in the formal sector|
||Importance of customs evasion (smuggling, under-declaration, etc)|
||Running of the justice system|
||Security of traditional property rights|
||Security of property rights: formal property rights|
||Security of contracts between private agents|
||Government respect for contracts|
||Settlement of economic disputes: justice in commercial matters|
||Intellectual property|
||Arrangements for the protection of intellectual property|
||Agricultural sector: security of rights and property transactions|
|**MIG**|Organised Crime.|
||Legal Safeguards.|
|**PRS**|_Law and Order._The Law sub-component is an assessment of the strength and impartiality of the legal system, while the Order sub-|
||<br>component is an assessment of popular observance of the law (assessed separately).|
|**QLM**|Direct Financial Fraud, Money Laundering and Organized Crime|
|**TPR**|Trafficking in People Report|
|**WMO**|_Judicial Independence_An assessment of how far the state and other outside actors can influence and distort the legal system. This will|
||determine the level of legal impartiality investors can expect.<br>_Crime_- How much of a threat businesses face from crime such as kidnapping, extortion, street violence, burglary...|

76
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 10. The hidden costs of water supply, as a percentage of revenue**

<!-- Start of picture text --> a. Evolution of hidden costs in SODECA b. Hidden costs of select water utilities<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br>CAR CAR Congo, Rwanda DRC<br>(2005) (2009) Rep. Congo<br>Collection inefficiencies Underpricing Losses Collection Inefficiencies Underpricing Losses<br>Percentage of the revenues<br>Percentage of the revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009).

# Power

## **Achievements**

Important reforms in the CAR’s energy sector have been implemented, including market liberalization allowing participation of the private sector. But as of today there is only one operator in the power sector (ENERCA), which produces, distributes, and markets electricity throughout the country. A newly appointed electricity regulator (ARSE) was created, but its capacity is still limited.

## **Challenges**

Years of sociopolitical crisis in the CAR have left the power infrastructure in an embryonic state. Total installed capacity in the CAR is 39 MW, one of the lowest in Sub-Saharan Africa. But the limited infrastructure available is decaying due to scarce maintenance and needs to be completed refurbished. Half of the assets are more than 30 years old. The installed capacity, at 10 MW per million people, is half the average of low-income countries in Sub-Saharan Africa (table 9). The limited generation capacity consists of Boali 1 and Boali 2—the two main hydropower plants—with a capacity of 14.9 MW each, a diesel plant in Bangui with a capacity of 6 MW, an interconnected grid between Boali and Bangui that carries 34 MW of generated power, and a small distribution network. In 2007 the government started construction of Boali 3, an additional 10 MW hydropower plant that is not yet operational.

Further limitations derive from aging infrastructure stock and the pilfering of power equipment. There are frequent breakdowns at power plants, which reduce the supply of already-scarce energy resources—as happened in 2008, when the country faced a power crisis. Boali 2 has been plagued by breakdowns of its power turbines and lost half its generation capacity due to technical failures. Theft of grounding equipment for light has made the transmission network more vulnerable to forces of nature and severely limited transmission capacity.

24
|Senegal Health & Nutrition Financing|Maud Juquois|
|---|---|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|Senegal Health & Nutrition Financing|Maud Juquois|
|MZ First Agriculture Development PolicyOperation AgDPO-1|Jan Joost Nijhoff|
|MZ First Agriculture Development PolicyOperation AgDPO-1|Jan Joost Nijhoff|
|TG - M&E CapacityStrengthening|Robert A. Yungu|
|TG - M&E CapacityStrengthening|Robert A. Yungu|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN<br>|Emeran Serge M. MenangEvouna<br>|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|NATURAL RESOURCES MANAGEMENT IN A CHANGING CLIMATE IN|Emeran Serge M. MenangEvouna|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|MZ-Social Protectionproject|Edmundo Murrugarra|
|Vietnam: Strengtheningthe Capacityof Accountingand AuditingPo|Christopher Robert Fabling|
|Vietnam: Strengtheningthe Capacityof Accountingand AuditingPo|Christopher Robert Fabling|
|Vietnam: Strengtheningthe Capacityof Accountingand AuditingPo|Christopher Robert Fabling|
|Vietnam-Modernizingthe Vietnam Association of Certified Public A|cCungVan Pham|
|Vietnam-Modernizingthe Vietnam Association of Certified Public A|cCungVan Pham|
|Vietnam-Modernizingthe Vietnam Association of Certified Public A|cCungVan Pham|
|Ethiopia Protection of Basic Services Social AccountabilityProgram|Alex Kamurase|
Kennedy, M. G., O'Leary, A., Beck, V., Pollard, K., & Simpson, P. (2004). Increases in Calls to the CDC National STD and AIDS Hotline Following AIDS‐Related Episodes in a Soap Opera. _Journal of Communication_ , _54_ (2), 287‐301.

Keysar, B., Hayakawa, S. L., & An, S. G. (2012). The Foreign‐Language Effect Thinking in a Foreign Tongue Reduces Decision Biases. _Psychological science_ , _23_ (6), 661‐668.

Klinger, B., & Schündeln, M. (2011). Can entrepreneurial activity be taught? Quasi‐experimental evidence from Central America. _World Development_ , _39_ (9), 1592‐1610.

La Ferrara, E., Chong, A., & Duryea, S. (2012). Soap Operas and Fertility: Evidence from Brazil. _American Economic Journal: Applied Economics_ , _4_ (4), 1‐31.

Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. _Handbook of affective science_ , _619_ (642), 3.

Lusardi, A. (2007). _Household saving behavior: the role of literacy, information and financial education programs_ (No. 2007, 28). CFS working paper.

Lusardi, A., & Mitchell, O. S. (2007). Baby boomer retirement security: The roles of planning, financial literacy, and housing wealth. _Journal of monetary Economics_ , _54_ (1), 205‐224.

Lusardi, A., & Mitchell, O. S. (2013). _The Economic Importance of Financial Literacy: Theory and Evidence_ (No. w18952). National Bureau of Economic Research.

Lusardi, A., & Tufano, P. (2009). _Debt literacy, financial experiences, and overindebtedness_ (No. w14808). National Bureau of Economic Research.

Mailath, G. J., & Samuelson, L. (2011). Repeated games and reputations: long‐run relationships. _OUP Catalogue_ .

McKenzie, D., & Woodruff, C. (2012). What are we learning from business training and entrepreneurship evaluations around the developing world?.

McKnight, P. E., McKnight, K. M., & Figueredo, A. J. (2007). _Missing data: A gentle introduction_ . Guilford Press.

Mulaj, F., & Jack, W. (2012). _Evaluating the efficacy of mass media and social marketing campaigns in changing consumer financial behavior_ (No. 73924). The World Bank.

Ottati, V. C., & Isbell, L. M. (1996). Effects of mood during exposure to target information on subsequently reported judgments: An on‐line model of misattribution and correction. _Journal of Personality and Social Psychology_ , _71_ , 39‐53.

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24
> equation (21). Moreover, we predict β 2<sup>></sup> 0 when log( _wH_<sup>∗</sup> / _wH_ ) is the dependent variable.<sup>_x_</sup> _ij_ , _t_ − 1 is a vector of other (lagged) controls that potentially affect log income differences between _i_ and _j_ , such as relative school enrollment rates, relative investment rates, relative urban population shares, and fixed effects for the source country to capture institutional differences to OECD destination countries. With respect to the dependent outcome measures, we focus on the year 2000 and usually measure controls other than skilled migration in lagged form (for 1990) to reduce endogeneity bias. _u_ is an error term. _ij_

To construct a measure of log( _wH_<sup>∗</sup> / _wH_ ) , we would like to use (log) wages differences for high-skilled individuals. However, because wage income by education category is not available, we construct several empirical proxy measures. Freeman and Oostendorp (2000) analyze information on earnings by occupation and industry from the ILO October Inquiry Survey from 1983 to 1998 for a number of countries.<sup>10</sup> For each country, we use Freeman and Oostendorp’s earnings measures to calculate the 80th and the 90th percentiles as two measures for wages of high-skilled workers. For most countries, data are available for only a few years. Therefore, for each country, we take the mean across the period from 1995 to 2003 to obtain wage data for the year 2000.<sup>11</sup> The two constructed (log) relative wage variables for the 80th and the 90th percentiles are denoted by _RelWage80ij,t_ and _RelWage90ij,t_ .

One may argue that migrating skilled workers do not receive wage income in the same percentile as they do at home. In particular, high-skilled workers from developing countries may not be considered highly skilled in the destination country. Therefore, as a 16
Elderly people also consider RM as an insurance against health risk. Research reveals that a large medical expense shock is the predominant reason to take out an RM in the United States (Nakajima & Telyukova, 2017). The risk of out-of-pocket medical expenditure shocks is considerable for the elderly living in countries with patchy health insurance systems, such as the United States and many developing countries. Health risk concerns may trump bequest motives, that is the desire to pass on accumulated wealth (such as a house) to heirs (Ameriks, Caplin, Laufer, & Van Nieuwerburgh, 2011; De Nardi, 2004). Supporting evidence is provided by a 2012 comparison between RM holders and non-holders among 10,000 homeowners in the United States. The study finds no significant difference in the number of children or the financial planning horizon, but it shows that RM holders are more risk averse and less likely to have long-term care insurance (Chatterjee, 2016).

However, RM is a flawed health care insurance tool because it does not address elderly care risk. In accordance with regulations, RM must be repaid after the last borrower leaves the home. If deteriorating health obliges an elderly person to move to an elderly care facility, they face the risk of loan termination at a time when their health expenditures rise significantly. In this case, the affected person either needs to certify on a regular basis that they are still living in their home or face foreclosure after one year (Nakajima, 2012). In the United States, the risk of ending up in a residential facility for elder care is estimated to be above 50% (Hurd, Michaud, & Rohwedder, 2013). The failure of RM to address elderly care risk may be a key reason why demand for it is so low (see below).

RM provides insurance against a decline in house price, albeit at a cost. Borrowers are not liable if the house price drops below the outstanding loan balance. Because RM are non-recourse loans, borrowers have to repay only the smaller of the loan balance or house price, and heirs are not responsible for any outstanding balance. Lenders do not shoulder this risk either because they are insured, often as mandated by regulation. In the United States for example, the Federal Housing Agency insures lenders against this so-called crossover risk, but the (substantial) cost of this insurance is paid in full by the borrower, not the lender (see section 4 for details).

RM may help optimize intergenerational wealth transfer – but could put the elderly at risk from abuse. This function is not widely discussed in the literature, and there is scant evidence of it in practice. But Chatterjee (2016) suggests that the elderly can use a RM to pass on their housing wealth to their offspring. In the United Kingdom, a portion of asset and income-rich households report using RM to pass on an inheritance early (European Mortgage Federation, 2009). Such bequests may be welfareenhancing because they would come at an earlier stage of children’s lifespan, when the demand for 10
|Total Yearly Cash & In-kind Income from Casual Labor in Rs. -<br>winsorized-1 percent|26,086.23|25,295.06|791.168|3.1|934.133|
|---|---|---|---|---|---|
|Total Yearly Cash IncomefromSalaryin Rs. -winsorized-1percent|12,502.80|11,330.75|1,172.05|10.3|1,652.72|
|Total Yearly Cash & In-kind Income from Agriculture in Rs. -<br>winsorized-1percent|17,385.49|16,649.72|735.766|4.4|958.202|
|Total Yearly Cash & In-kind Income from Land-winsorized-1<br>percent|116.514|193.376|-76.862|-39.7|241.346|
|Total Yearly Cash & In-kind Income from Fisheries in Rs. -<br>winsorized-1percent|474.856|117.792|357.063|303.1|347.547|
|Total Yearly Cash & In-kind Income from Enterprises in Rs. -<br>winsorized-1 percent|2,753.96|2,732.80|21.155|0.8|1,631.83|
|Total Yearly Income from Public Transfers MGNREGS and other<br>Government Programs|8,053.78|8,268.87|-215.092|-2.6|372.232|
|Total Yearly Income from Private Transfer in Rs. -winsorized-1<br>percent|805.32|367.79|437.53|119|271.398|
|Total Yearly Cash Income from Other Sources in Rs. -winsorized-1<br>percent|1,508.61|1,423.21|85.402|6|167.774|
|Total Paid days for all Household Members under MGNREGS work<br>(in a Year)|128.561|167.853|-39.291|-23.4|45.516|
|Number of Members Migrated to Urban Areas for Employment or<br>Search of Employment|0.062|0.035|0.028***|79.400***|0.009|
|<br>Number of Members Migrated to Rural Areas for Employment or<br>Search of Employment|0.018|0.006|0.012***|203.100***|0.005|
|Total Number of Enterprises in Household|0.045|0.032|0.013*|40.400*|0.007|
|Total numberofemployees (Permanent andTemporary)|0.055|0.04|0.015|37.5|0.015|
|Total number of external employees (Permanent and Temporary)|0.01|0.013|-0.003|-20.5|0.011|
|Does anybody in the Household have Non-Farm business?|0.103|0.078|0.025**|32.100**|0.01|
|Does the Household Own Any Enterprise? (Y/N)|0.043|0.032|0.012*|37.600*|0.007|
|Any external Funding for enterprises|0.025|0.006|0.018***|278.400***|0.004|
|LogTotal fundsinvestedinenterprise|0.421|0.206|0.214***|104.000***|0.067|
|Log Total funds borrowed for enterprise|0.237|0.059|0.178***|304.700***|0.043|
|Log Total Cash & Kind Revenue from Enterprises in Rs.|0.438|0.337|0.101|30|0.075|
|Households cultivating HVA Crop (Non-Paddy-Wheat-and-Millets)-<br>(Y/N)|0.385|0.291|0.094***|32.300***|0.017|
|Total NumberofVillageLevelGroupParticipation|0.098|0.048|0.050***|103.200***|0.01|
|Total Number of Different Programs|1.118|1.145|-0.027|-2.4|0.028|
|Does anybody in the Household have a NREGS card? (Y/N)|0.694|0.612|0.083***|13.500***|0.017|

38
and data on total credit to domestic enterprises and financial institutions, we can generate a rough estimate of the government's total implicit liabilities stemming from guarantees to the financial sector.<sup>23</sup> Table 6: Estimated Total Non-Performing Bank Loans

||||No|n-Performin<br>(estimat|g Credit<br>ed)|
|---|---|---|---|---|---|
||Non-<br>Perform.<br>Loans<br>(% of<br>all loans)|Credit to<br>Domestic<br>Enterprises<br>and<br>Financial<br>Institutions*<br>(6/97)|% f<br>MB|% of<br>1997<br>GDP**|% of<br>1997<br>Central<br>Gov't.<br>Revenue"|
|Indonesia|17|343|129.6|9.4|63.0|
|Korea|16|589|454.1|22.1|103.7|
|Malaysia|16|403|88.8|23.3|98.3|
|Philippines|14|1365|84.5|7.8|41.1|
|Thailand|19|7912|289.6|30.2|176.7|
|Hong Kong|4|2223|91.6|6.6|33.6|
|Singapore|4|160|35.0|4.4|11.8|
|Taiwan|4|12157|26.8|6.0|50.0|

*For Indonesia and Korea, expressed in trillions of local currency units. For all others, in billions of local currency units "**Estimates.

Columns 2, 3 and 4 of Table 6 report total non-performing bank loans as a percentage of the monetary base, real output and central government revenue, respectively. Table 7 reports the analogue percentages for the liabilities associated with non-bank foreign borrowing. Note that regardless of which measure we use, Korea and Thailand exhibit high levels of total implicit liabilities. For example, in 23Our data on domestic lending are from the IMF International Financial Statistics. We have attempted, as best as possible, to net out lending from one type of financial institution to another (for example, lending by a deposit money bank to some other type of financial intermediary).

32
35

|**Region**|**Countries**|**Year policy**<br>**information**<br>**collected**|**Source of**<br>**information**|**Government**<br>**confirmation**<br>**received**<sup>**10**</sup>|
|---|---|---|---|---|
|Middle East and North Africa (MENA)|Algeria|2008|WB survey||
|Middle East and North Africa (MENA)|Egypt|2008|WB survey|Yes|
|Middle East and North Africa (MENA)|Iran, Islamic Rep.|2008|WB survey||
|Middle East and North Africa (MENA)|Jordan|2008|WB survey||
|Middle East and North Africa (MENA)|Lebanon|2008|WB survey||
|Middle East and North Africa (MENA)|Morocco|2008|WB survey||
|Middle East and North Africa (MENA)|Tunisia|2008|WB survey|Yes|
|Middle East and North Africa (MENA)|Yemen|2008|WB survey||
|Gulf cooperation council (GCC)|Bahrain|2008|WB survey||
|Gulf cooperation council (GCC)|Kuwait|2008|WB survey||
|Gulf cooperation council (GCC)|Oman|2008|WB survey||
|Gulf cooperation council (GCC)|Qatar|2008|WB survey||
|Gulf cooperation council (GCC)|Saudi Arabia|2008|WB survey||
|Latin America and Caribbean (LAC)|Argentina|2008|WB survey||
|Latin America and Caribbean (LAC)|Bolivia|2008|WB survey||
|Latin America and Caribbean (LAC)|Brazil|2011|WB survey|Yes|
|Latin America and Caribbean (LAC)|Chile|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Colombia|2008|WB survey||
|Latin America and Caribbean (LAC)|Costa Rica|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Dominican Republic|2008|WB survey||
|Latin America and Caribbean (LAC)|Ecuador|2008|WB survey||
|Latin America and Caribbean (LAC)|Guatemala|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Honduras|2008|WB survey||
|Latin America and Caribbean (LAC)|Mexico|2011|WB survey|Yes|
|Latin America and Caribbean (LAC)|Nicaragua|2008|WB survey||
|Latin America and Caribbean (LAC)|Panama|2008|WB survey||
|Latin America and Caribbean (LAC)|Paraguay|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Peru|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Trinidad and Tobago|2008|WB survey|Yes|
|Latin America and Caribbean (LAC)|Uruguay|2008|WB survey||
|Latin America and Caribbean (LAC)|Venezuela|2008|WB survey||
|Eastern Europe and Central Asia (ECA)|Albania|2008|WB survey||
|Eastern Europe and Central Asia (ECA)|Armenia|2008|WB survey|Yes|
|Eastern Europe and Central Asia (ECA)|Belarus|2008|WB survey|Yes|
|Eastern Europe and Central Asia (ECA)|Bulgaria|2008|WB survey||
|Eastern Europe and Central Asia (ECA)|Georgia|2008|WB survey||
|Eastern Europe and Central Asia (ECA)|Kazakhstan|2008|WB survey|Yes|
|Eastern Europe and Central Asia (ECA)|Kyrgyz Republic|2008|WB survey||
|Eastern Europe and Central Asia (ECA)|Lithuania|2008|Publicly available<br>sources|Yes|
|Eastern Europe and Central Asia (ECA)|Poland|2008|Publicly available<br>sources|Yes|
|Eastern Europe and Central Asia (ECA)|Hungary|2008|Publicly available<br>sources||
> % Exports 0.196 0.735' 0.488 0.202 _OSS6_ 0506 (0370) (0379) (0350) _(0.405)_ (0.423) (0394) Inputs: h (Senegalese 0.468* _0.424_ Skiled and Unscilled) (0.172) (0.188) In (Other African 0.892-4 031E-3 Skilled and Unskilled) (0.182-3) (0.20E-3) In (Non-Affican 0.39E-3* 032E-3 Skiled and Unskilled) (0.23E-3) (0.25E-3) In ISeegalese Middle 0.53-S -033E-4 and Upper Management) (0.192-3) (021EB-3) In (Other African Middle 0.55E-3' 0.69Ee3 and Upper Manigement) (027E-3) (029E-3) In (Non-Afnan Middle 0.27E-3 0.19E-3 and Upper Marnageent) (038E-3) (0.42E-3) In (Unilled Workers) 0.632 0.673' (0.157) _A S*_ (0.174) In (Skilled Workcs) 0.24E-3 J(0 6 0.43B-r 3(0.153) (022E-3) (024E-3)