rafmacalaba/datause-encoder-data huggingface dataset:
This pattern implies that several local companies effectively act as “brokers”, “fronting” business for foreign reinsurers. This practice may represent a reasonable division of labor between local companies that understand better local market conditions and foreign reinsurers that have greater capacity to underwrite risks. Assuming that local companies are operated by experienced professionals, and that foreign reinsurers are highly rated companies, this cooperative arrangement may be an efficient way of expanding insurance business on a sound basis.

On the other hand, because the local insurer has little or no retention of the business, there is no incentive to adhere to proper standards of underwriting, and because the ceding company receives a fee based on volume of premium there is every incentive to maximize volume at the expense of underwriting soundness. In light of these characteristics, fronting arrangements can result in abrupt cancellation of terms by the foreign reinsurer and sometimes even a denial of payment of claims because the domestic ceding insurer has not kept to the understandings with regard to business quality that were arrived at when the arrangement was being negotiated. Thus fronting agreements can sometimes be a source of considerable risk, with local policyholders being exposed if the fronting company is viewed by the reinsurer as having abused the arrangement.

Loss ratios and overall technical results exhibit large fluctuations reflecting the low frequency of large losses. 2002 was a very good year with a reported preliminary underwriting result of 73 percent of net earned premiums. The technical profit was much smaller in some earlier years and especially in 1997, 2000 and 2001.

|**Table 15: Performance of**|**Other Gen**|**eral Ins**|**urance,**|**1997-20**|**02**||
|---|---|---|---|---|---|---|
||1997|1998|1999|2000|2001|2002|
|Gross Premium Income (GPI) (mn JD)|31.39|25.93|24.95|26.96|30.17|38.02|
|GPI Growth||-17.4|-3.8|8.1|11.9|26.0|
|Reinsurance ratio %|81.6|85.7|85.5|85.2|85.3|88.5|
|NIC/NEP|50.7|36.2|33.7|43.7|67.0|41.8|
|Net Commissions (% of GPI)|-14.3|-15.7|-16.5|-15.4|-15.9|-15.5|
|Total Expense (% of GPI)|17.0|18.3|19.5|19.0|16.9|14.1|
|Traditional Combined Ratio|53.4|38.8|36.7|47.3|68.0|40.4|
|Net Combined Ratio/NEP|65.2|52.6|56.2|67.7|75.4|26.8|
|Underwriting Result (% of NEP)|34.8|47.4|43.8|32.3|24.6|73.2|
|Unearned Premium Reserves (% of NEP)|75.5|77.9|93.2|83.7|97.0|149.1|
|Outstanding Claims Reserves (% of NEP)|26.8|33.1|45.2|42.8|72.9|99.0|
|Total Reserves (% of NEP)|102.3|111.0|138.3|126.6|169.9|248.1|
|Outstanding Claims Reserves (% of NIC)<br>Sourc|52.8<br>e: Insuranc|91.4<br>e Commis|133.8<br>sion|98.0|108.9|236.9|

The overall level of reserves for these lines of business also appears to be reasonably high. In addition to their extensive reliance on reinsurance, companies make
Table 6.1—Regression results of years of schooling, for full sample and trimmed and weighted sample

|||Full S|ample||Trim|med and W|eighted Samp|le|
|---|---|---|---|---|---|---|---|---|
||Model 1||Model 2||Model3||Model 4||
|TEEP cohort*TEEP province|-0.410|(-0.76)|-0.862|(-1.57)|-0.427|(-0.73)|-0.734|(-1.25)|
|Female|1.419**|(2.44)|1.457**|(2.53)|1.892**|(3.27)|1.920**|(3.32)|
|Female*TEEP cohort|-0.183|(-1.15)|-0.184|(-1.15)|-0.200|(-1.23)|-0.195|(-1.18)|
|Female*TEEP cohort*TEEP province|0.281**|(2.01)|0.289**|(2.07)|0.337**|(2.00)|0.343**|(2.05)|
|Log(consumer durables)|0.426***|(7.60)|0.391***|(6.64)|0.463***|(7.96)|0.442***|(7.33)|
|TEEP cohort|0.910|(1.28)|0.593|(0.84)|0.969|(1.47)|0.806|(1.27)|
|TEEP cohort*Log(consumer durables)|-0.362***|(-6.13)|-0.325***|(-5.42)|-0.375***|(-7.29)|-0.353***|(-7.02)|
|Female*Log(consumer durables)|-0.0691|(-1.38)|-0.0726|(-1.47)|-0.110**|(-2.31)|-0.113**|(-2.37)|
|TEEP cohort*TEEP province*Log(consumer<br>durables)|0.00460|(0.10)|0.0470|(1.01)|-0.00406|(-0.08)|0.0236|(0.48)|
|Age|1.402***|(4.11)|1.467***|(4.20)|1.433***|(3.72)|1.503***|(3.79)|
|Age squared|-0.0442***|(-4.82)|-0.0458***|(-4.89)|-0.0455***|(-4.39)|-0.0472***|<br>(-4.43)|
|Mother’s years of schooling|0.127***|(11.28)|0.126***|(11.23)|0.134***|(10.58)|0.132***|(10.53)|
|Mother’s age|0.0200***|(5.15)|0.0197***|(5.14)|0.0198***|(4.98)|0.0197***|(5.04)|
|Number of siblings|-0.0275**|(-2.05)|-0.0277**|(-2.07)|-0.0277**|(-2.15)|-0.0284**|(-2.22)|
|Poverty incidence rate|||0.0410***|(3.79)|||0.0346**|(3.16)|
|Infrastructure index|||0.452|(1.59)|||0.634**|(2.22)|
|Number of students per classroom in high<br>school|||-0.00171|(-0.89)|||-0.00143|(-0.73)|
|If phone interview|||-0.0269|(-0.26)|||-0.110|(-0.82)|
|Othervariables and school fixed effects|yes||yes||yes||yes||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

37
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- 18 -

- (c) This formula is then applied to information on the physical characteristics of each property in the jurisdicrion's tax base, to yield individual valuations.

3.40 In principle, this approach avoids the worst aspects of the direct market information method. First, it is not dependent upon the honesty of taxpayers: the physical characteristics of property can be seen and measured. It is also less vulnerable to valuer corruption: the use of a formula reduces valuer discretion. And it can be applied to both owner- and renter-occupied property.

3.41 In designing a method for deriving valuations, two problems must be confronted:

- (a) how to get a basic core of reliable market information; and

(b) how to extrapolate from this core of market data to produce defensible valuations for all properties in the tax base.

# Address Data Constraints

3.42 Use Construction Cost Data to Value Buildings. In general, capital value countries address the data-constraint problem by limiting their reliance on sales data to the valuation of land. Buildings are valued separately, using data from a source that is considered more reliable: the construction industry. In the Philippines, the taxing authority makes its estimates of the cost of construction on the basis of materials costs obtained from lumberyards and hardware stores, and on standard material-cost to labor-cost ratios. In Brazil, taxing authorities rely on cost data supplied by local contractors, or compiled from professional building industry journals. These sources normally distinguish between the costs of construction in different exterior materials (brick, concrete, stucco) and different structure types (detached homes, apartment buildings, small commercial structures).

3.43 On the basis of this data, the taxing authority estimates the cost, per square foot, of constructing the buildings typical of the area to be valued. The unit costs yielded by this approach, while objective, are an extremely crude basis on which to estimate the market value of buildings. Most jurisdictions tend to elaborate on the methodology by adding variables: data on type of flooring, plumbing and electrical facilities, subjective estimates of the quality of repair, the state of technological obsolescence. These elaborations do not, however, improve the accuracy of valuations. Although these adjustment factors can dramatically affect a property's valuation, the weight assigned to them is not generally determined by an analysis of cost or market responses. Instead it reflects the deliberations of the valuation staff, and the views of the political leaders who must ultimately approve the tables.-L' 12/ The practice in developing countr'es suggests that this is not an inherent problem in mass appraisal systems. In the United States, for example, statistical analyses are used to determine with some precision the contribution of each factor to value. But in developing countries this opporturity to improve the accuracy and objectivity of individual valuations is not exploited.
|Table 11: OLS regression of l<br>|ogincomeper capita on<br>|farmingvariables,2|001<br>|
|---|---|---|---|
|Variable|Return|t|P>|t||
|Primary education|19.69|4.34|0.00|
|Secondary education|48.47|5.16|0.00|
|Tertiary education|426.58|5.30|0.00|
|Rural|10.79|2.21|0.03|
|Electricity|52.37|5.27|0.00|
|Water|24.63|4.06|0.00|
|Road|12.47|1.79|0.07|
|Some tools|14.95|1.96|0.05|
|Many tools|34.39|4.05|0.00|
|Fertilizer|51.66|8.94|0.00|
|Pesticides|16.33|2.17|0.03|
|Irrigation|40.80|6.01|0.00|
|0.5-1 ha|13.58|2.43|0.02|
|1-2 ha|23.93|4.09|0.00|
|2-4 ha|46.21|5.90|0.00|
|4-7 ha|51.20|4.64|0.00|
|7-10 ha|70.14|4.54|0.00|
|>10 ha|140.58|7.87|0.00|
|Land title|19.25|4.84|0.00|
|Constant|87579.5|76.97|0.00|

Note: No. of observations: 3932; all farmers. Excluded categories: No completed education and <0.5 ha land. The percentage return is calculated as (exp <u>(coefficient estimate) – 1) * 100</u> _Source:_ Own calculations based on HLCS 2001.

Access to infrastructural services such as water, electricity, and roads are important for producer income generation. Farmers with access to roads (paved and gravel), water and electricity earn statistically significantly higher incomes than do farmers without access to these services. Findings in Table 11 show that access to roads, water and electricity increase producer incomes by 12, 25, and 52 percent respectively. Surprisingly, telephones and obtained credit did not significantly improve producer incomes in Haiti; therefore this finding is not reported in this paper (only 18 households have a phone.

The use of productivity enhancing production techniques and inputs such as tools, fertilizers, pesticides, and irrigation, are also important for increasing producer incomes in Haiti. The use of tools, fertilizers, pesticides, and irrigation are all significantly positive determinants of farm incomes. Farms that apply these productivity enhancing technologies experience 15, 34, 52, 16, and 41 percent higher incomes than farms that do not use or have access to some tools, many tools, fertilizers, pesticides, and irrigation respectively. These findings, coupled with the findings on farm size and titles, indicate that farm intensification and increased access to production enhancing inputs are essential to increase farmer incomes. With title to their land, farmers should have increased access to credits to finance more tools, fertilizer, etc. to enhance productivity of their land.

This section showed that workers’ and producers’ incomes are strongly impacted by the level of completed education of the worker or farmer. In both cases the more 22
goods. Under negative shocks, the implicit taxes resulting from overshooting imposed on US agriculture were limited by the price support policy, which introduced a downward price inflexibility for the supported commodities, but which in turn cause the incidence of the macroeconomic policy tax on agriculture to shift towards the cost of mantaining stocks.

The implications of these findings for other economies needs to be examined. One would expect that, unlike the US, most LDCs are price takers in grain markets and so (at a given nominal exchange rate), domestic farm prices would change relatively less than those of home goods after a monetary shock, which would not induce overshooting. This is likely to apply to most Latin American countries but not necessarily to other ones. For instance, state trading in agricultural commodities is still pervasive in India. In that case or in the case of non-tariff barriers, the domestic price may be unrelated to the world price and overshooting may occur as well.

# **_The_** _EU_ **_and their Common Agricultural Policy_**

The fiscal burden of the Common Agricultural Program (CAP) has inspired substantial analyses of the impact of agricultural protection on macroeconomic variables in studies such as Gylfason (1995), and Stoeckel, Vincent, and Cuthbertson (1989). Gylfason argued that although partial equilibrium analysis found that deadweight losses resulting from farm policies represented about 1 percent of GDP, long-term general equilibrium considerations were shown to raise the loss estimates to about 3 percent of GDP. The CAP has been responsible for higher consumer food prices and an increased tax burden. Total per capita agricultural transfers per full-time farmer equivalent<sup>6</sup> averaged $17,700 in the EU, compared with a median labor income of $12,000 in 1992.

Based on general equilibrium models described in the volume edited by Stoeckel et.al., various authors quantified the impact of agricultural protection on relative prices to manufacturing, manufacturing output, and employment. The studies found that industrial countries would benefit from removing agricultural protection because manufacturing output and exports would increase, as would employment.

Stoeckel and Breckling (1989) examined the effects of removing both the CAP and national agricultural protection in the four largest EU countries, namely Germany, France, Italy, and the United Kingdom. They found that the elimination of both measures of protection could generate an increase in aggregate output of over 1 percent, create 3 million new jobs, and lead to a 5 percent increase in manufacturing exports to the rest of the world.

Dicke, Donges, Gerken, and Kirkpatrick (1989) focused on the case of West Germany, the largest economy in the EU and the largest contributor to the CAP. They also found that Germany would experience gains from liberalization similar to those estimated GFull-time farmer equivalent is defined as 2200 hours of work in agriculture annually.

28
# D. Access policy framework index for the Philippines and comparators, 2015

||**Colombia**|**Peru**|**Philippines**|**Vietnam **|<sup>**International**</sup><br>**benchmark**|
|---|---|---|---|---|---|
|**Regulation of New Connections**|**68%**|**88%**|**57%**|**14%**|**65%**|
|Roles of regulator, utility, rural electrification agency<br>clearlydefined||||NAV|93%|
|Utilities have regulatory obligation to connect new<br>customers|||||94%|
|Regulatory entity has authority to approve connection<br>charges for new customers|||||71%|
|Connection charges are set usingshallow entry||||NAP|58%|
|Governmentprovides subsidyfor new connections|||||53%|
|Connection has to beprovided in a specified time|||||94%|
|Regulatory entity monitors tiem taken to provide new<br>connections|||||44%|
|Regulator has authority to levy penalties for not<br>connectingcustomers on time|NAP||NAP|NAP|71%|
|Time taken toprovide connectionspubliclyavailable|||||24%|
|There are connection charges||||NAV|94%|
|Customerpays limited connection charges||||NAP|43%|
|Connection charge ispubliclyavailable|||||71%|
|**Regulation of solar home systems**|**100%**|**50%**|**100%**|**0%**|**66%**|
|Minimum technical standards and post-installation<br>warrantyrequirements for solar home systems|||||71%|
|Regulator reviews and approves prices of surplus SHS<br>sales of electricityto thegrid operator|NAV|||NAV|62%|
|**Regulation of mini-grids**|**33%**|**33%**|**67%**|**NAV**|**44%**|
|Privatelyowned mini-grids legallyallowed to operate||||NAV|81%|
|Clear options for mini-grid operator when the<br>interconnected grid reaches the area, including<br>compensation||||NAV|7%|
|Subsidy or other mechanism to help mini-grid<br>operators recover their costs||||NAV|47%|

# E. Corporate governance- accountability index for the Philippines and comparators, 2015

|**Cororate overnance**|**Viet**|**nam**|**Philipp**|**ines**|**Col**|**ombia**|**Pe**|**ru**|**International**|
|---|---|---|---|---|---|---|---|---|---|
|**p g**|NPC|HCMPC|MERALCO|BENECO|EPM|CODENSA|Luz del Sur|Hidrandina|**benchmark**|
|**Accountability**|**17%**|**17%**|**100%**|**67%**|**75%**|**92%**|**92%**|**58%**|**60%**|
|Private orpublic shareholders appoint board|||||||||36%|
|Transparentprocess exists for Board selection|||||||||36%|
|Board members cannot be removed at will|||||||||29%|
|Chairperson & CEO are separatepositions|||||||||75%|
|Function of CompanySecretaryexists|||||||||82%|
|Board Sub-Committees for different issues|||||||||68%|
|Audit committee of the Board|||||||||71%|
|Board Code of Conduct exists|||||||||64%|
|Requirement to declare conflicts of interest|||||||||75%|
|Utility has carried out any third party<br>transactions in last fiveyrs|||||||||46%|
|Minorityshareholders' rights areprotected|||||||||39%|
|Utility publishes an Annual Report|||||||||93%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 62
# **Are mega-farms the future of global agriculture?**

**Exploring the farm size-productivity relationship**

**for large commercial farms in Ukraine**

Klaus Deininger<sup>1</sup> , Denys Nizalov<sup>2,</sup> Sudhir K Singh<sup>1</sup>

> **1** World Bank; **2** Kyiv School of Economics/ Kyiv Economics Institute

JEL Codes: O13, Q15, Q18 Keywords: Land reform, transition, agricultural productivity, Ukraine, market structure We thank H. Binswanger, D. Byerlee, M. Childress, J. Conning, C. Csaki, G. Feder, T. Gagalyuk, A. Kaliberda, V. Lapa, Z. Lerman, A. Lissitsa, W. Liefert, O. Liefert, W. Martin, O. Muliar, O. Nivyevsky, M. Raiser, J. Swinnen, T. Sulukhia, and H. Strubenhoff, for insightful comments. Funding support from the TF-ESSD is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of their respective institutions.
Table 4: Relationship between management (all sub-scores) and energy intensity in value (1)-(3) and quantity (4)-(6) terms.

||(1)<br>All Firms|(2)<br>Manufacturing|(3)<br>Services|(4)<br>All Firms|(5)<br>Manufacturing|(6)<br>Services|
|---|---|---|---|---|---|---|
|Targets z-score|-0.211<sup>_∗_</sup>|-0.584<sup>_∗∗∗_</sup>|0.007|0.060|0.020|0.076|
||(0.114)|(0.144)|(0.227)|(0.057)|(0.077)|(0.102)|
|Monitor z-score|0.006|-0.013|-0.155|0.187<sup>_∗∗∗_</sup>|0.268<sup>_∗∗∗_</sup>|0.072|
||(0.095)|(0.124)|(0.198)|(0.052)|(0.075)|(0.092)|
|Operations z-score|-0.003|-0.043|0.074|0.078<sup>_∗_</sup>|0.043|0.086|
||(0.094)|(0.134)|(0.167)|(0.042)|(0.065)|(0.066)|
|Incentives z-score|0.023|0.218|0.054|-0.018|-0.054|0.010|
||(0.124)|(0.171)|(0.240)|(0.060)|(0.085)|(0.103)|
|No. Employees|1.124<sup>_∗∗∗_</sup>|0.835<sup>_∗∗∗_</sup>|1.138<sup>_∗∗∗_</sup>|0.465<sup>_∗∗∗_</sup>|0.286<sup>_∗∗∗_</sup>|0.427<sup>_∗∗∗_</sup>|
||(0.130)|(0.176)|(0.221)|(0.062)|(0.091)|(0.103)|
|Log(Sales)|-1.603<sup>_∗∗∗_</sup>|-1.679<sup>_∗∗∗_</sup>|-1.851<sup>_∗∗∗_</sup>|-0.549<sup>_∗∗∗_</sup>|-0.597<sup>_∗∗∗_</sup>|-0.575<sup>_∗∗∗_</sup>|
||(0.098)|(0.134)|(0.174)|(0.041)|(0.063)|(0.065)|
|Capital Stock||0.567<sup>_∗∗∗_</sup>|||0.234<sup>_∗∗∗_</sup>||
|||(0.079)|||(0.042)||
|3-digit sector|✓|✓|✓|✓|✓|✓|
|Age|✓|✓|✓|✓|✓|✓|
|Country|✓|✓|✓|✓|✓|✓|
|Observations|6124|3011|2055|3513|1805|1135|
|Adjusted _R_<sup>2</sup>|0.264|0.215|0.339|0.299|0.323|0.310|
|df<br>~~m~~|124|88|69|116|85|63|

Standard errors in parentheses Capital, labor, and output enter in logs. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 22
# **Exhibit 2a: Evidence from cross-national studies (I/II)**

|**Study**|**Brief**<br>**description**|**Time**<br>**period**|**Key finding**|**Implications for market vs.**<br>**intervention question**|**Implications for**<br>**centre vs. region**<br>**question**|
|---|---|---|---|---|---|
|Barry<br>(2003)|• Economic<br>performance<br>of cohesion<br>countries -<br>Greece,<br>Ireland|1960-1973|• Convergence, except in Ireland<br>• Ireland similar in outward orientation,<br>protectionism, education to others<br>• Labour market disequilibrium main<br>difference|• Market distortions impede<br>convergence|• NA|
||,<br>Portugal,<br>Spain -<br>relative to rest<br>of EU|1973-1986|• General divergence<br>• Lax fiscal and monetary policies;<br>labour market disequilibrium|• Negative impact if markets not<br>allowed to decide wage rates<br>• Big government policies causing<br>macroeconomic problems|• NA|
|||1986-2000|• Resumption of convergence,<br>especially Ireland<br>• Tighter fiscal and monetary policy for<br>euro entry; EU regional aid; labour<br>market reforms in Spain; increased<br>FDI flows in Ireland; administrative<br>reforms in Greece; liberalization<br>after EU accession in Spain and<br>Portugal|• Mixed implications as both<br>liberalization and better market<br>discipline due to EU and euro<br>entry, and EU aid identified as<br>factors for catch-up<br>• However, preponderance of<br>factors lie towards the market<br>end of the spectrum|•<br>NA|

**Evidence for market         Evidence for interventionist policy         Evidence for decentralized approach Evidence for centralized approach**

18
Table 4: Extensions with changes in elasticities, median values (minimum, maximum in parentheses)

|**_Scenario_**||**_Young, LS_**|**_Young, HS_**|**_Old, LS_**|**_Old, HS_**|
|---|---|---|---|---|---|
||Total|8.23<br>(-0.88 ; 18.05)|-10.46<br>(-19.71 ; 7.28)|4.84<br>(-3.89 ; 14.34)|-13.33<br>(-21.09 ; 0.87)|
||Socio-demographic|7.65<br>(-0.69 ; 14.55)|-10.31<br>(-24.63 ; 7.92)|4.37<br>(-4.74 ; 10.39)|-12.88<br>(-20.93 ; 0.34)|
|_Baseline_|Immigration|0.48|-0.47|0.45|-0.30|
|||(-1.34 ; 5.36)|(-5.3 ; 1.76)|(-0.62 ; 4.9)|(-4.97 ; 1.36)|
||Emigration|-0.63|0.75|-0.48|0.83|
|||(-3.83 ; -0.04)|(-1.48 ; 3.54)|(-5.43 ; 0.68)|(-1.25 ; 3.91)|
||Total|6.03|-7.27|4.32|-9.75|
|||(0.95 ; 13.96)|(-22.38 ; 4.37)|(-4.13 ; 10.85)|(-20.99 ; -1.81)|
||Socio-demographic|7.03|-8.84|3.48|-11.63|
|||(-0.45 ; 13.1)|(-21.84 ; 7.24)|(-4.49 ; 8.87)|(-18.31 ; -0.4)|
|_σs_=2|Immigration 0.42|-0.39|0.40|-0.26||
|||(-1.18 ; 4.76)|(-4.56 ; 1.58)|(-0.46 ; 4.3)|(-4.28 ; 1.17)|
||Emigration|-1.65|1.20|-0.33|1.47|
|||(-13.62 ; 2.13)|(-3.42 ; 8.71)|(-4.73 ; 1.88)|(-3.74 ; 5.64)|
||Total|7.56<br>(-1.18 ; 17.57)|-10.88<br>(-19.5 ; 6.81)|5.46<br>(-3.34 ; 14.84)|-13.17<br>(-20.53 ; 2.22)|
||Socio-demographic|7.18|-10.96|4.77|-12.37|
|||(-1.08 ; 13.99)|(-24.08 ; 7.38)|(-3.99 ; 10.97)|(-21.03 ; 1.95)|
|_σa_=7|Immigration|0.44|-0.47|0.41|-0.37|
|||(-1.2 ; 5.27)|(-5.2 ; 1.71)|(-0.75 ; 4.98)|(-4.8 ; 1.46)|
||Emigration|-0.64|0.74|-0.48|0.82|
|||(-3.76 ; 0.07)|(-0.77 ; 3.52)|(-5.34 ; 0.85)|(-0.65 ; 3.78)|
||Total|7.87|-10.75|4.65|-13.37|
|||(-0.91 ; 17.77)|(-19.84 ; 7.21)|(-3.92 ; 14.28)|(-21.21 ; 0.8)|
||Socio-demographic|7.53<br>(-0.72 ; 14.47)|-9.90<br>(-24.4 ; 7.86)|4.40<br>(-4.77 ; 10.43)|-12.86<br>(-20.78 ; 0.32)|
|_σm_=1000|Immigration|0.38|-0.64|0.42|-0.37|
|||(-1.75 ; 5.57)|(-6.45 ; 1.74)|(-0.8 ; 4.72)|(-6.67 ; 1.21)|
||Emigration|-0.64|0.74|-0.48|0.82|
|||(-3.76 ; 0.07)|(-0.77 ; 3.52)|(-5.34 ; 0.85)|(-0.65 ; 3.78)|

The table reports equilibrium wage responses when we vary the parameter values in table 3 45