rafmacalaba/datause-encoder-data huggingface dataset:
13 in the value of the real exchange rate<sup>-</sup> to eliminate<sup>excess demand for nontradable goods. Similarly,</sup> the FF schedules show the equilibrium in the tradable<sup>goods market. Again, starting from an</sup> equilibrium position, an increase in absorption wil!<sup>create excess demand for tradable goods which</sup> will be eliminated by an increase in the relative<sup>price of tradable goods. The left-hand panel depicts</sup> the relationship between the wage-rental ratio and the<sup>relative price of tradable goods under the</sup> assumption that tradables are labor intensive."3 A move away from the origin implies a redistribution of income toward labor.14 Return now to the comparison of the two economies.<sup>Both have to adjust to the same</sup> reduction in capital inflows (i.e. the same leftward<sup>shift in the FF curve measured at the initial real</sup> exchange rate). Economy F is flexible compared<sup>to economy R; small shifts in relative prices will</sup> lead consumption patterns to shift between tradables<sup>and nontradables. Likewise, small relative price</sup> shifts will elicit a supply response because the technology<sup>is flexible. By contrast, economy R is</sup> rigid. All imports are essential intermediates and<sup>capital goods that cannot be produced domestically,</sup> so there is little scope for substitution in consumption<sup>toward domestically produced goods even with</sup> large relative price shifts. Likewise tiere are<sup>rigidities in the production structure that make it</sup> difficult to shift resources from nontradables to tradables."5<sup>Consequently, the NN and FF schedules</sup> for economy F are relatively flat while those for economy<sup>R are relatively steep. Also, the factor</sup> price frontier for the flexible economy is steeper than<sup>for the rigid economy since the cost structures</sup> are different."<sup>6</sup> Consider now what happens to the distribution of income<sup>when both economies have to adjust</sup> to the same reduction in capital flows. In the flexible<sup>economy, the required real depreciation is</sup> smal! and the corresponding change in the distribution<sup>of income (W. to W,) is also small. Economy</sup> R, because of rigidities in consumption patterns and<sup>production structures, has to depreciate its real</sup>
# PRE Workira Paper Series

||Author|DAM|Contact<br>for papsr|
|---|---|---|---|
|WPS360 Compounding<br>Financial Repressio<br>with Rigid Urban Regulations:<br>Less<br>of the Korea Housing Market|n Bertrand<br>Renaud<br>ons|January 1990|L. Victorio<br>31009|
|WPS361 Housing and Labor Market<br>Distortions<br>in Poland: Linkages<br>and Policy Implications|Stephen K. Mayo<br>James I. Stein|January 1990|L. Victorio<br>31009|
|WPS362 Urban Property Taxation: Lessons<br>from Brazil|William Dillinger|January 1990|L. Victorio<br>31009|
|WPS363 Paying for Urban Services: A Study<br>of Water Vending and Willingness<br>to Pay for Water in Onitsha, Nigeria|Dale Whittington<br>Donald T. Lauria<br>Xinming Mu|January 1990|L.Victorio<br>31009|
|WPS364 Financing<br>Urban Services it Latin<br>America: Spatial Distribution<br>Issues|Gian Carlo Guarda<br>|January 1990|L. Victorio<br>31009|
|WPS365 Cost and Benefits of Rent Control<br>in Kurnasi,<br>Ghana|Stephen Malpezzi<br>A. Graham Tipple<br>Kenneth<br>G. Willis|January 1990|L. Victorio<br>31009|
|WPS366 Inflation, Monetary<br>Balances,<br>and<br>the Aggregate Production Function<br>The Case of Colombia|Robert Buckley<br>: Anupam<br>Dokeniya|January 1990|L. Victorio<br>31009|
|WPS367 The Response<br>of Japanese and U.S<br>Steel Prices to Changes in the<br>Yen-Dollar<br>Exchange<br>Rate|. Panos Varangis<br>Ronald C. Duncan|March 1990|D. Gustafson<br>33714|
|WPS368 Enterprise<br>Reform in Socialist<br>Economies: Lease Contracts<br>Viewe<br>as a Principal-Agent<br>Problem|Guttorm Schjelderup<br>d|||
|WPS369 Cost Recovery Strategy for<br>Rural Water Delivery in Nigeria|Dale Whittington<br>Apia Okorafor<br>Augustine<br>Akore<br>Alexander<br>McPhail|March 1990|V. David<br>33736|
|WPS370 Turkey: Export Miracle or<br>Accounting Trick|Ismail Arslan<br>Sweder van Wijnbergen|||
|WPS371 Tariff Valuation<br>Bases and Trade<br>Among Developing Countries...<br>Do Developing<br>Countries<br>Discrimin<br>Against Their Own Trade?|Refik Erzan<br>Alexander Yeats<br>ate|February 1990|J. Epps<br>33710|
|WPS372 Long-Term<br>Outlook for the World<br>Economy: Issues and Projections<br>for the 1990s|Shahrokh<br>Fardoust<br>Ashok Dhareshwar|February 1990|J. Queen<br>33740|
information of both a general and a specific nature. The state government data was provided by the Deutsche Strassen Liga, a non-governmental institute. 22 The local government data was collected from the yearly Finanz Bericht and from interviews with road experts and officials in seven states. In addition, technical experts in Germany were contacted and interviewed to obtain the necessary information. Tables 3 and 4 display the information contained in the panel data set. Germany was selected as a case study because it has a high level of decentralized administration and construction and a low level of decentralized maintenance, in a fiscal sense.

# _The_ **_Impact_** _of Fiscal_ **_and Functional Decentralizationfrom Cross-Country Data_**

We use the double cost approach and hidden level of effort framework to measure the impact of fiscal decentralization on road provision using the cross-country panel. We estimate the four models defined in the last section, testing the various hypotheses postulated in the theory section. The estimation results are presented sequentially by model and comparisons of findings are done cumulatively across models.

# Model 1: The Independent Impact of Decentralization on Resource Costs.

The independent impact of decentralization on resource costs from the crosscountry panel is summarized in Table 5a while the impact of the control variables is presented in Table 5b. We find that after controlling for country-specific effects, income as measured by per capita GNP, and time, increasing the level of decentralization has a significant impact on resource costs. The impact of decentralization varies depending on the type of road function.

**Table 5a: Independent Impact of Decentralization on** Resource Costs

||Partial Effects|of Functional D|ecentralization|Total Effect of|
|---|---|---|---|---|
|Variable|Construction|Maintenance|Administration|Decentralization|
|fiscal|4.33|-<br>.63|0.44|9|
|decentralization|(0.74)|.0)|(1 .3_5)*_*|(1|
|fiscal<br>2|-0.03|-0.03|3|-0.19|
|.<br>decentralization|(-0.26)|(-1 .59)*|(-1|.57)*|
|fiscal<br>|0.001|0.0001|--|0.001|
|decentralization<sup>3</sup>|(0.32)|_(1.15)_|(3.80)|(1.12)|
|R-squared|0.72|0.82|0.81|0.76|
|Observations|261|261|261|261|

Fiscal decentralization is measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

22 The Deutsche Strassenliga brings together professionals working in different areas on roads. Members come mainly from the private sector but the agency is in close cooperation with the state and federal governments. It promotes the development of roads in Germany.

16
<!-- Start of picture text --> 2<br>=<br>34} i<br>3, 3”<br>5, 3"<br>iot iea<br>i. J<br>-8 -5 -4 -3 -2 -8 -5 —4 -3 -2<br>Postulated endogeneity of voice & accountability Postulated endogeneity of voice & accountability<br>\——— V95%Clestimate ENN_----- KLSestimaeKLS>=95% Cl —— 95%WestimateCl -----NINN KLSestimateKLS>=95% Cl<br>PanelvariateA:specification Effeof voi c e andt accountabilityon sanitationusage (Bi- Paneltrols B: Effect of voice on sanitation usage (specification with con-<br>22<br>”<br>.<br>i ai<br>z2<br>.”<br>J o<br>-8 -5 -4 -3 -2 -8 -5 -4 -3 -2<br>Postulated endogeneity of voice & accountability Postulated endogeneity of voice & accountability<br><!-- End of picture text --> Panel C: p-values for the KLS exclusion restriction test of primary school enrollment Panel D: p-values for the KLS exclusion restriction test of primary school enrollment
position themselves at different stages along GVCs depending on the "skills dispersion" underlying the observed mean levels. These show that skills are key for economies to thrive in GVCs.

# **_2.2 Effects of migration/diaspora in GVC participation_**

Studies by Saxenian (2006, 2007), Ratha (2010), and Hausmann and Neffke (2018) have demonstrated how migration and skill mobility can through technology diffusion improve the GVC participation of migrants’ origin countries. The combination of increasing mobility of skilled workers with increasing fragmentation of production and improved Information and Communication Technologies have enhanced migrants and diaspora’s ability to transfer technology know-how and market information to their countries of origin, and help jump-start local entrepreneurship (Saxenian, 2006). Previous studies have shown that migrants and diaspora can help their countries of origin by fostering exchange of knowledge and information, increasing trade partnerships, investing in infrastructure, housing, health care, and education projects (Ratha, 2010). Saxenian (2007) explored the emergence of high-technology clusters in Taiwan, China; China; India; and Israel and found that U.S.-educated migrants and diaspora have played vital roles in these countries by returning home with expertise and contacts that allowed them to start successful global companies. According to her, networks of foreign-born entrepreneurs and engineers transfer technology and institutional know-how from the United States to their countries of origin much faster than most multinational firms. In the case of Silicon Valley, foreign-born engineers and investors helped transfer the Silicon Valley model to Taiwan, China, and Israel. These migrants/diasporas also provided the cultural and linguistic know-how, their technical and operational skills, knowledge of new business models, their networks of contacts, as well as capital needed to operate profitably in their countries of origin. As a result, Israel and Taiwan, China, currently hold the largest venture-capital industries outside North America. In addition, skill mobility and migration can help pioneering firms to recruit their workforce in places where local workers do not have the relevant skills. Setting new activities requires a workforce that has the right skills and know-how. However, many of these skills and know-how are often acquired onthe-job making it very difficult to recruit local workers. One solution is to bring skilled workers from other localities. Hausmann and Neffke (2018) looked at the case of Germany where 6
|Table2.6: Contributio <br>|nto Opera<br>|ting Cos<br>|ts,2002-<br>|2006<br>||
|---|---|---|---|---|---|
|in basis points|2002|2003|2004|2005|2006|
|Internal Assets (% of total assets)|80|77|78|78|79|
|External Assets (% of total assets)|20|23|22|22|21|
|Internal Management Costs|7.0|6.2|5.1|4.9|4.8|
|External Management Costs|17.1|24.4|29.4|31.1|28.3|
|Internal Contribution to Costs|5.7|4.8|4.0|3.8|3.8|
|External Contribution to Costs|3.3|5.6|6.5|6.8|6.0|
|Total Costs|9.0|10.4|10.5|10.6|9.8|
|Performance Based Fees|1.1|1.2|2.6|2.7|2.5|
|Costs withoutPerformanceFees|7.9|9.2|7.9|7.9|7.3|

Source: NBIM Norges Bank participates in the database of operating costs of large pension funds that is compiled by the Canadian consulting firm, CEM Benchmarking Inc. This database includes cost performance data on over 250 pension funds. A peer group of large pension funds that has been selected by CEM from this database shows that NBIM has lower operating costs than the median fund from this peer group (Table 2.7).

Table 2.7: <u>Comparative Database of Operating Costs, 2003-2005</u>

|in basis points|2003|2004|2005|
|---|---|---|---|
|GPF – Global|10.4|10.5|10.6|
|PeerGroup– Median|13.1|12.0|13.4|

Source: NBIM

# **2.7 Concluding Remarks**

The Norwegian GPFG has been created to preserve oil wealth for future generations and help meet rising pension expenditures. The fund does not have a separate legal entity but is effectively a government account with the central bank.

Decisions about investment policy objectives and strategic asset allocation are the responsibility of the Ministry of Finance, but must be approved by Parliament. Norges Bank, which has been selected as the operational manager of the fund, provides advice on investment policy. Asset allocation has been conservative, despite the long investment horizon. This has been influenced by the need for parliamentary debate and approval of the strategic asset allocation and has probably reflected a low level of risk tolerance.

The fund is subject to a very high level of transparency and public accountability, but it is not completely insulated from political interference. However, the so-called 4-percent fiscal rule, which states that the annual structural budget deficit should not exceed 4 percent of Fund assets, imposes some discipline on political pressures to increase public spending.

33
one in Table 12. Therefore, the single TFI successfully replaces several separate trade facilitation and logistics indicators.

|**Table 13: Three-S**|**tage Augmented G**<br>|**ravity Estima**<br>|**tions**<br>|
|---|---|---|---|
|**Variable**|**First**<br>**Stage**|**Second**<br>**Stage**|**Third**<br>**Stage**|
||0.990***||0.893***|
|ln_gdp_ex|(0.0724)||(0.0725)|
||0.0894||0.0494|
|ln_gdp_im|(0.11)||(0.128)|
||0.829***||0.737***|
|ln_tariff|(0.244)||(0.245)|
||0.936||1.198*|
|rta|(0.614)||(0.609)|
||0.182||0.222|
|contig|(0.432)||(0.548)|
||2.478***||3.094***|
|r_transit|(0.669)||(0.742)|
||0.161||0.357|
|ln_cc_ex|(0.189)||(0.227)|
||0.023||0.043|
|ln_cc_im|(0.102)||(0.154)|
|||0.217||
|ln_distance||(0.167)||
|||2.666***||
|ln_number||(0.937)||
|||1.424*||
|ln_customs||(1.294)||
|||0.849*||
|ln_infrastructure||(1.185)||
||||3.645***|
|ln_tfi|||(1.263)|
|Observations|210|210|210|
|_R_<sup>2</sup><br>|0.809|0.231|0.831|
|Adj._R_<sup>2</sup>|0.809|0.229|0.830|
|Mean VIF#|2.58|4.84|3.19|
|chi<sup>2</sup>|28.1|18.45|51.95|
|IM-test<sup>$</sup><br>_p_|0.6159|0.4928|0.0653|
|Ramsey<br>_F_|3.49|1.39|1.95|
|<br> <sup>$$</sup><br>||||
|RESET test<br>_p_|0.0191|0.2515|0.1277|

_Notes_ : Dependent variable for first and third stages is total bilateral exports (in logs) in 2008 or latest year available. Dependent variable for the second stage is first-stage residuals. ***, **, and * indicate values significant at 1%, 5%, and 10% levels. Robust standard errors are in parentheses. Constant terms are not shown. #Variance inflation factors (VIF) of all explanatory variables test for multicollinearity (tolerance level <10). $Cameron and Trivedi‘s decomposition of IM-test is to test for heteroscedasticity.<sup>$$</sup> Ramsey RESET test is to detect model specification error or test omitted variable bias.

26
from potentially efficiency-enhancing barrier reductions (increased non-discriminatory access to transmission, improved information and clearer economic rewards for energy efficiency), better regulation of conventional pollution from fossil energy, and increased support for innovation. But much the green energy literature also assumes significant future reductions in CO2 emissions in developed countries, and/or posits increased financial support through public venture capital investments, subsidies of private investment, tariff designs, etc. (SEF Alliance 2009, GNC 2009a, 2009b, UNEP 2008a). The applicability and cost effectiveness of such measures in developing countries is open to question.

We turn next to some recent quantitative assessments from model simulations of various “green simulation” measures. One such analysis addresses U.S. measures under the Obama administration stimulus bill (Houser, Nohan and Heilmayr 2009). While not directly applicable to developing countries, the calculations may still be useful as a reference case.

**Table 6.1: Impact of US$1 Billion Additional Spending on “Direct Green Stimulus” Activities.  Projected for U.S. Under the Obama Economic Stimulus Bill.**

|“Green” program|Overall<br>employment<br>impact, job<br>years, initial<br>year|Energy cost<br>saving, US$ million annually,<br>2012-2020|CO2emissions<br>reduction, 1000<br>tons annually<br>2012-2020|Private share,<br>overall<br>generated,<br>average|
|---|---|---|---|---|
|Household<br>weatherization|25100|207.8|440.7|0|
|Federal building<br>retrofits|25300|386.7|546.9|0|
|Green school<br>construction|25200|609.2|905.8|0|
|PTC extension|39100|562.5|727.7|76.1|
|ITC increase|33300|208.7|213.4|47.0|
|CCS demo projects|28500|225.3|341.6|68.8|
|“Cash for<br>clunkers”|46900|433.0|1112.5|86.8|
|Hybrid tax credit|11100|-|-|0|
|Battery R&D|22500|1278.8|1332.8|0|
|Mass transit|34500|23.6|87.3|27.4|
|Smart metering|40000|918.0|207.4|50.0|
|Average for green<br>stimulus|30100|450|593|-|
|Road investment|25200|-32.8|-35.4|0|

Source: Houser, Nohan and Heilmayr (2009).

The main results, given in Table 6.1, are scaled to show figures for a $1 billion increase in expenditure.<sup>19</sup> The table indicates that the effects on employment, energy consumption and

> 19 Note that the table includes some “current spending” items, and some items more naturally categorized as infrastructure investment (including school construction, battery R&D and mass transit). All are consistent with our definition of “direct green stimulus” including government expenditures and provision of incentives for increasing specific activities.  Employment effects include all directly and indirectly induced effects.

15
|Mean household size per village||-0.123<br>(5.367)***||||||||
|---|---|---|---|---|---|---|---|---|---|
|Constant|13.189<br>(159.2)***|12.847<br>(100.06)***|12.628<br>(204.5)**|12.066<br>(99.28)**|12.826<br>(117.13)*|12.663<br>(117.64)***|12.737<br>(126.09)*|19.11<br>(12.26)**|10.85<br>(17.62)***|
|Observations|187|601|287|490|194|195|206|398|<br>2192|
|Number of cluster in HBS|2|6|3|5|2|3|2|5|19|
|Weight applied|NO|YES|YES|YES|YES|NO|NO|YES|NO|
|Location Effectmodelled|NO|NO|NO|NO|NO|NO|NO|NO|YES|
|**R-squared**|0.45|0.33|0.42|0.36|0.31|0.56|0.32|0.36|0.29|
|Absolute value of t statistics in parentheses<br>significant at 10%; ** significant at 5%; *** si|gnificant at 1%|||||||||

# **Table 4: Rural Strata**

||_Nakhchirvan_<br>_AR_|_Absheron-_<br>_Guba_|_Mugan -_<br>_Salyan_|_Ganja-_<br>_Gazakh_|_Sheki_|_Lankaran_|_Shirvan_|_Karabah_|
|---|---|---|---|---|---|---|---|---|
|Period 2|-0.035|-0.056|0.024|0.0007|0.089|0.022|0.067|0.033|
||(0.82)|(1.28)|(0.79)|(0.02)|(2.43)**|(0.67)|(1.80)*|(0.96)|
|Period 3|0.090|0.267|0.137|0.151|0.204|0.186|0.223|0.104|
||(2.38)**|(6.18)***|(4.49)***|(4.55)***|(5.13)***|(5.91)***|(5.67)***|(2.53)**|
|Period 4|0.121|0.279|0.075|0.194|0.283|0.165|0.209|0.067|
||(2.59)**|(6.67)***|(2.19)**|(6.14)***|(7.67)***|(4.96)***|(5.40)***|(1.64)|
|**Demographic**|||||||||
|Proportion of hh member b/w 15 to 59|-0.658<br>(8.47)***|-0.337<br>(5.40)***||||-0.542<br>(12.25)***||-0.424<br>(7.62)***|
|Proportion of hh member above 60 years old and||||||||-1.33|
|Men||||||||(6.18)***|
|Proportion of hh member above 60 years old and<br>Men squared||||||||1.20<br>(3.32)**|
|Proportion of hh member b/w 15 to 59 and men||||||-1.17<br>(5.47)***|||
|Proportion of hh member b/w 15 to 59 and men||||||1.17|||
|Squared||||||(2.94)**|||
|Proportion of hh member economically active|0.176<br>(2.00)**|0.145<br>(2.27)**||-0.112<br>(2.06)**|||||

54
2

**Table** 1. Ul **Beneffta as a** Percentage<sup>of GDP In OECD</sup> Countries, Recent Years

<!-- Start of picture text --> Country 1989 or 1990 Previous 3 Years<br>Australia 1.07 0.99<br>Austria 0.11 0.15<br>Belgium 2.04 °.39<br>Canada 1.57 1.69<br>Denmark 3.21 2.95<br>Finland 0.62 0.79<br>France 1.27 1.29<br>Germany 1.14 1.29<br>Groee 0.46 0.43<br>Ireland 2.79 3.26<br>Japan 0.32 0.39<br>Netherlands 2.30 2.65<br>New Zealand 1.77 0.97<br>Norway 1.15 0.64<br>Portugal 0.32 0.32<br>Spain 2.33 2.47<br>Sweden 0.59 0.61<br>Switzerland 0.14 0.18<br>Great Britain 0.90 1.17<br>United States 0.60 0.47<br><!-- End of picture text --> Source: OECD, Emoloyment Outlook,<sup>1991, pp. 238-248.</sup>

# B. Potentlal Goals of Ul Programs<sup>-</sup> **and** Rationales **For** Qovernment Intervention

# IndividualiBased Goac

Among the goals that have been adduced<sup>for Ul are two that can be viewed as hndiduai-</sup> based, in the sonse that they stem from<sup>theview that the govemment can directly improve economic</sup> oAtcomes at the micro level. To be reasonable<sup>these goals must be based on the belief that the private</sup> market, or the individual agents themselves,<sup>cannot optimize instantaneousiy or Intertemporally.</sup> The major motivation for Ul is the individual-based<sup>goal of consumption stabilization. This</sup> argument for Ul as social Insurance is<sup>that without Ul benets households will have insufticnt savings</sup> to prevent substantial welfare losses<sup>when a family member becomes unemployed. It is trueafortiori that</sup>
<!-- Start of picture text --> 76<br>72<br>60 ————____-_- _—___—___— ———<br>56<br>48<br>40<br>36<br>2017-2018 2019, 2020. 2021 2022, 2023. 2024 2025<br><!-- End of picture text --> <!-- Start of picture text --> en<br>69 ——--<br>ag<br>a4<br>36<br>28.<br>24.<br>M2017 2018 2019 2020 20212022 20232024 205<br><!-- End of picture text --> <!-- Start of picture text --> 165<br>‘60 —_---___________-_<br>55<br>‘50<br>‘45<br>135<br>'30<br>15<br>to<br>_0<br>Mm 2017 2018 2019 2020 2021 2022 2023 2024 2025<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>80<br>70<br>BO ————— ———<br>50<br>30<br>20<br>M2017. 2018 2019-2020. 2021. 2022-2023 2024-2025<br><!-- End of picture text --> <!-- Start of picture text --> 75<br>70<br>ios<br>60 ——_—______ eC<br>55<br>50<br>45<br>i<br>m 2017. 2018 2019 2020 2021 2022 2023 2024 2025<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>65<br>60 ___S ———<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>m 2017 2018 2019 2020 2021 2022 2023 2024 2025<br><!-- End of picture text -->
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 19 Electricity and Water Subsidies That Reach the Poor**

- a. Electricity

<!-- Start of picture text --> measure of distributional incidence<br><!-- End of picture text -->

- b. Water

<!-- Start of picture text --> measure of distributional incidence<br><!-- End of picture text --> _Sources_ : Banerjee and others 2008b; Wodon and others 2007a, 2007b.

_Note_ : A measure of distributional incidence captures the share of subsidies received by the poor divided by the proportion of the population in poverty. A value greater than 1 implies that the subsidy distribution is progressive (pro-poor), because the share of benefits allocated to the poor is larger than their share in the total population. A value less than 1 implies that the distribution is regressive (pro-rich).

How expensive would utility bills be if tariffs reflected costs? For power, a monthly subsistence consumption of 50 kilowatt-hours at a cost recovery tariff of $0.18 per kilowatt-hour would translate to a utility bill of $9 per month. In the case of water, with a cost recovery tariff of $1.17 per cubic meter and a 38