rafmacalaba/datause-encoder-data huggingface dataset:
TABLE 2. Baseline Specifications without and with Time-Varying Multilateral Resistance

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||PPML|PPML|PPML|PPML|
||Co-|R&D|Co-|R&D|
||inventorship|offshoring|inventorship|offshoring|
|ln(Diaspora)|0.200***|0.0929**|0.243***|0.111***|
||(0.0229)|(0.0407)|(0.0233)|(0.0423)|
|ln(Distance)|-0.267***|-0.105|-0.0468|0.173**|
||(0.0523)|(0.0721)|(0.0552)|(0.0760)|
|Contiguity|0.210*|-0.0996|0.116|-0.271|
||(0.127)|(0.211)|(0.122)|(0.191)|
|Common language|0.625***|0.931***|0.434***|0.703***|
||(0.132)|(0.220)|(0.108)|(0.187)|
|Lang. similarity|0.523**|0.822**|0.405**|0.694**|
||(0.225)|(0.386)|(0.202)|(0.352)|
|Colonial links|0.0654|0.299*|0.0315|0.291**|
||(0.124)|(0.172)|(0.103)|(0.143)|
|Religion similarity|0.706***<br>|0.219<br>|0.612***<br>|0.185<br>|
||(0.256)|(0.446)|(0.235)|(0.419)|
|ln(EXP+IMP)|0.0457**|0.0710***|0.228***|0.329***|
||(0.0197)|(0.0249)|(0.0335)|(0.0495)|
|ln(Tech.distance)|-0.0637|-0.245***|-0.136***|-0.287***|
||(0.0469)|(0.0598)|(0.0506)|(0.0689)|
|ln(# patents_i)|0.334***|0.354***|||
||(0.0530)|(0.0693)|||
|ln(# patents_j)|0.0568|0.333|||
||(0.115)|(0.205)|||
|ln(GDP p.c. _i)|1.098***|1.807***|||
||(0.233)|(0.318)|||
|ln(GDP p.c. _j)|-0.0776|-1.107|||
||(0.552)|(0.823)|||
|Constant|-6.072|-4.780|-0.410|-1.726**|
||(6.006)|(8.651)|(0.652)|(0.791)|
|Observations|26,160|26,160|19,676|20,574|
|Pseudo R2|0.958|0.918|0.978|0.956|
|Sending FE|Yes|Yes|No|No|
|Receiving FE|Yes|Yes|No|No|
|Year FE|Yes|Yes|No|No|
|Sending FE*Time FE|No|No|Yes|Yes|
|Receiving FE*Time FE|No|No|Yes|Yes|
|LogLik|-18492.28|-23746.92|-16226.61|-20463.96|

Source: Authors’ analysis based on data described in the text.

Notes: Country-pair clustered robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. ‘_i’ and ‘_j’ stand for, respectively, migrant inventor’s sending country and migrant inventor’s receiving country. Per capita GDP at origin presents several missing observations: for 1990, missing data correspond to Azerbaijan, Eritrea, Cambodia and Latvia; for 1991, to Eritrea; and for 2005, to Cyprus, Gabon, Lesotho, Oman, Rwanda, Thailand, Uzbekistan, and Zimbabwe. Moreover, data for the former Soviet Republics are only available from 1991; data for TFYR of Macedonia, Croatia, and Slovenia only from 1992; and data for the Czech Republic, Slovakia, and Eritrea only from 1993. In consequence, the final sample consists of 26,160 observations, instead of 26,800. The lower number of observations between columns (1), (2) and (3), (4) is due to the inclusion of fixed effects in pseudo-maximum likelihood estimations: the PPML method automatically drops the country-specific fixed-effects (and their corresponding observations) for which the country has zero recorded inventors’ flows to every other country in the sample in order to achieve convergence. Results are comparable to other count data methods without removing these observations (see Santos Silva and Tenreyro 2010 for further details).

22
transfers are exogenous, and the exogenous deficit target is met by making government spending on goods endogenous.

Finally, the rest of the world supplies imports and demands exports. Domestically produced goods are imperfect substitutes for imports. World relative prices and the current account balance are set exogenously in this one-country model, and an endogenous variable for terms of trade clears the current account equation. The world price of commodity _i_ relative to _j_ is assumed to be at base year ratios throughout the projection period with the exception of world oil prices, where projections from the U.S. Energy Information Administration 2013 _Annual Energy Outlook_ are used (and treated as exogenous).

On the supply side, 33 industries are distinguished, each producing output that is given by a nested series of constant-returns-to-scale CES functions.<sup>4</sup> Primary factors include capital, labor and land. Pure TFP growth and biased technical change are allowed; in particular, energy input per unit output can decline faster than other input-output ratios.

Since the electricity sector is the main contributor of CO2 emissions in China, in the version of the model used for this study, the electricity sector is disaggregated into one transmission subsector and 9 distinct generation sub-sectors – coal, gas, nuclear, hydro, other, wind, solar, coal-CCS and gas-CCS. The tier structure of electricity production is given in Figure 1. At the top node total output is the aggregate of Generation and Transmission, and Generation is in turn an aggregate of Baseload and Intermittent Renewables.<sup>5</sup> The Baseload aggregate is composed of the output from coal, gas, nuclear, hydro, miscellaneous other minor sources, and the potential technologies, coal with carbon storage and sequestration (CCS) and gas-CCS. The Intermittent Renewables are wind and solar. The elasticities of substitution among these generation sources are presented in Table A2 in Appendix A and also noted in Figure 3; we assume a high degree of substitution among the baseload sources, and we follow other studies in assuming an elasticity of one between Baseload and Renewables.

This structure reflects the reality that average generation costs are different and yet different generation sources co-exist; there are considerations beyond average costs that determine the share of various sources in a highly regulated sector. We should make a technical note regarding this formulation: the imperfect substitution of kWh’s means that the quantity

> 4 The production structure is given in Figure A1 and equations A4-A12 in Appendix A.

> 5 This aggregation of Baseload and Renewables is similar to that in C-GEM (Qi et al. 2014).

‐5‐
<!-- Start of picture text --> Annualized wage change in Miami, FL<br>20.00%<br>All occupations<br>Roofers<br>15.00%<br>10.00%<br>5.00%<br>0.00%<br>-5.00%<br>Aug-03 Oct-03 Dec-03 Feb-04 Apr-04 Jun-04 Aug-04 Oct-04 Dec-04 Feb-05 Apr-05 Jun-05 Aug-05 Oct-05 Dec-05 Feb-06 Apr-06 Jun-06 Aug-06 Oct-06<br><!-- End of picture text --> _Figure 2: Roofer wages in an area where losses have been significant after the 2004 hurricane season in Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03, May 04, Nov 04, May 05, May 06, May 07._

# **2.2.2 “Ripple effects”**

Output losses are not only due to forgone production from the assets that have been destroyed or damaged by the event. Assets that have not been affected by the hazards can also be revealed to be unable to produce at the pre‐event level because of indirect effects, sometimes referred to as “ripple effects”. This is particularly the case for infrastructure and utility services (electricity, water and sanitation, gas, etc.). In past cases, it has been shown that the loss of utility services had larger consequences than direct asset losses, both on households (McCarty and Smith, 2005) and on businesses (Gordon et al., 1998; Tierney, 1997).

(McCarty and Smith, 2005) investigated the impact of the 2004 hurricane season on households in Florida, and find that among the 21% of the households who were forced to move after the disaster, 50% had to do so because of the loss of utilities (e.g., they had no running water). In only 37% of the cases, the main reason was structural damages to the house. (Tierney, 1997) and (Gordon et al., 1998) investigate the impact of the Northridge earthquake in 1994 in Los Angeles; they find also that loss of utility services and transport played a key role. Tierney surveys the reasons why small businesses had to close after the earthquake. The first reason, with 65% of the answers (several answers were possible), is the need for clean‐up. After that, the five most important reasons are loss of electricity, employee unable to get to work, loss of telephones, damages to owner’s or manager’s home, and few or no customers, with percentages ranging from 59% to 40%. All these reasons are not related to structural damages to the business itself, but to offsite impacts. (Gordon et al., 1998) ask businesses to assess the earthquake loss due to transportation perturbations, and find that this loss amounts to 39% of total losses. (Kroll et al., 1991) find comparable results for the Loma Prieta earthquake at San Francisco in 1989: the major problems for small business were customer access, employee access, and shipping delays, not structural 10
17 literacy rates above 60%, high education appeared to be associated with reduced fertility, while where the literacy rate is below 40%, high education appeared to be associated with increased fertility. Thus increases in school enrolment rates were initially associated with increases in fertility, and it seems that the enrolment rate has to rise above 75% before a sustained decline can be anticipated. The average developing country is now well over this threshold (see footnote 11, p.38) Psacharopoulos and Woodhall arrived at the following conclusions. Education is unlikely to reduce fertility in all circumstances. It seems that the initial conditions vis-a-vis literacy are especially important. Thus, for the least literate societies evidence points in the direction that increments in education lead to increases in fertility. Nevertheless, in the long run it is usually the case that increasing education will ultimately reduce fertility. Education of females, especially girls, is usually more effective at affecting fertility than educating males. Finally, education is more likely to reduce fertility in urban areas than in rural areas. The link between education and fertility is not straightforward, but, at the levels of education found in most developing countries nowadays, education does appear to be a force reducing fertility.

What is needed in this area is a better understanding of the economics of household and family development. Caldwell (1982) presents a theory for fertility decline based on the costs of rearing children. He presents an analysis of how households evaluate the costs and benefits of having children. Not surprisingly, to explain a declining fertility rate it is suggested that the expected costs of having a child increase with education, with proportionate changes in the benefits not offsetting this. This is argued to be related to both the indirect costs (opportunity costs) and the direct costs of education, fees, etc. This approach is similar in spirit to the human capital model, see Becker (1964). The _World Development Report_ 1990, however, suggests that the most effective way to deal with high fertility rates is to improve family planning services.

The results of careful cross-section studies are presented by Rosenzweig (1990). He suggests that fertility of landless laborers in India in 1971 is positively-linked to child wages and to adult male wages but negatively-linked to adult female wages. Mother's schooling was not an important influence in the results of this study. It is not clear, however, whether fertility is responding to the difference between wage rates for educated and uneducated labor. School enrolment rates respond negatively to child wages and positively to fathers' wages but, once again the results do not actually indicate how they respond to the return to education.

Perhaps the best cross-section evidence for a link between the return to education and fertility is offered by Rosenzweig's assessment of the green revolution. In areas covered by the Intensive Agricultural Development Program (IADP) the return to education seems to have been
[m it Y y Ri*AI( It WORKINI. PAPhK I (305.

# Summary findings

hle valu.tion (it en:tcrpries 11.1ha beien a nmajir stLimbling bllck to privarization In traimiglonial ecOnoiIes. Data on ni perluormiance it static-owncd enterprises under ceiitral planning **is** petilutil, biut thalt information is not wolfrthi milch in a market economn, especially onc in whiich mtich progress has hecn madc in price and trade likralization.

Votclher privatization schemes ha.ve emerged as a politically attractive alternative; the valuation probleim is ovcrcome throiugh a decentralized systenm of bidding, whiile shares are efficientlv all.icated throiugh an auction. BIut nobody has analyzed how these information markers function or how prices emcrge from bidding rounds.

Shafik presents econometric cvidencc on the functioning of information markets and on the process hy which prices cmerged for enterprise shares under the Czech and Slovak mass privatization scheme. The rcsults indicatc that public information about enterprises' past performance clearly mattered, especially in the early roinds wIiLhil private information had not bcen revealed. lint suici **h** istorical information alone never explained miore thain 29 perceint of the variation in Ihc tiltimate ctiLllihritiuim price.

Instead, information about enterprises' prospects revealed throuigh the bidding process explained about _85_ perecnt of the variation in prices _by the_ final rounds. Private or 'insider" information about enterprises' prospects played a grad;ually diminishing role as participants learned quickly from each other's bidding behavior.

Olther mass privatization schemes rely more heavily on the sccondary markc to generate an appropriate valualtion of shares, rather than on the initial valuations emerging from the primary markect. But if improved information is the first step toward efficient asset markets and corporate governance, participants in the Czech and Slovak mass privatization scheme have a head start on other transitional economies.

This paper-a product of the Country **Operations** D)ivision. Europe and Central Asia, Country Dcparrmcnrt 11-is part of a larger effort in thc department to analyzc innovative a pproaches to privatization in transition economies. Copics of thc papcr are availabIc frce from the World Bank, 1813 H Street NW, Washington, DC 20433. Plcase contact Azeb Yidcru, r(oom H7-05 1, cxtcnsion i6(67 (23 pages). June 1994.

_Pru Woring l'apP_ **_e_** _r Scnes Research drssemtnates blicy rhe findtngs cf u-rk in pragress on encouraete exchange of deas about develpment issues. An objectrveoxfthe serres is t Jger theffnJungs (iNr qusckly. even rtfre presenttionvsare less than fully polzsked The papers cry the nJmCeS of the authors arnd should be Nsed and cited Jc-crdrngly. l7be findings . interprestatins. and ronclusrons are the auttors -ou n and should not be attrrbuted to the World Bank;. Its Fxecutive Roard of Dircrors or any of ts member coun tries._ Produced by the Policv Rcscarch Dissernination Ccnrcr
**h. External Aid.** To the extent that countries do not have to incur the burden of taxation, they may not have the incentive to use resources in the most cost-effective way.   Another channel through which aid-financing may affect efficiency is through the volatility and unpredictability of its flows.  Given that this financing source is more volatile than other types of fiscal revenue (Bulir and Hamann, 2000), it is difficult to undertake mediumterm planning with in activities funded with aid resources. If this is the case, we would expect a negative association between aid-dependence and efficiency in those activities funded with aid, mostly health services. To our knowledge there are no previous attempts to establish a relationship between efficiency and the degree to which activities are financed by external aid.  There is, however, recent evidence of a negative association between donor financing and some health outcomes (Bokhari, Gottret, and Gai, 2005)

**i. Institutional Variables.** Countries with better institutions, more transparency, and less corruption are expected to have higher efficiency scores. Similarly, countries that have suffered wars or state failures are expected to register lower efficiency scores.  To capture these effects we included different indicators: the ICRG International Country Risk Indicators, the Worldwide Governance Research Indicators, in particular the Control of Corruption component (Kaufmann, et.al, 2002).  We also included a dummy variable if there had been some type of state failure, such as internal wars, from the State Failure Task force database.

The data on educational and health indicators are not available on a continuous annual basis for many countries. Thus, averages of the variables were computed over sub-periods both in the first stage calculation of efficiency score and in the second stage of regression analysis. Specifically, educational indicators are averaged over two periods (1975-80 and 1996-2002) and health indicators over two periods (1996-99 and 2000-02). This discrepancy in the sub-period construction is due exclusively to the lack of data for earlier years. The averages are treated as separate observations. The advantages of this approach are threefold. First, the averages may serve as a better measure of the educational and health attainment, which can hardly be substantially improved on a yearly basis; Second, the averaging maximizes the coverage of countries for each period, since one observation of a certain year is sufficient to help the country survive in the cross sectional comparison; Third, the time series thus constructed for each country, although short, facilitates the implementation of econometric techniques on panel data to explore the efficiency variations across countries and through time.

27
45 that would have prevailed in the absence of the program. This will require a methodology that allows for endogenizing the participation decision itself. But also the validity of the maintained identification conditions regarding the participation decisions must be addressed as well. An appropriate methodology to address the above concerns was developed and implemented in sections 3 and 4 of this paper.

The results of our modified-control-group estimation on the impact of adjustment on the economic performance of the EIAL countries of Sub-Saharan Africa relative to others, suggest that adjustment programs have had a statistically significant and positive effect on export performance, and that they have also caused statistically significant drop in aggregate investment. These results corroborate the findings obtained for Bank-wide adjustment lending by Corbo and Rojas(1991).18 Also according to our results, there is only weak evidence of a statistically significant effect of adjustment on GDP growth. This result is at variance with the strong evidence in support of a positive effect for Bank-wide adjustment programs on GDP growth (e.g. Corbo and Rojas(1991)).'<sup>9</sup> The combination of the estimated nonsignificant marginal effect of adjustment on economic growth, and its negative and significant influence on investment, obtained for Sub-Saharan Africa's experience with adjustment lending;

**18** Conway (1990) - who applied a different statistical methodology on 76 developing countries, arrived at broadly similar conclusions to that of Corbo and Rojas (1991). Using a similar statistical methodology to ours though a different base period, the Third Report on Adjustment Lending (RAL III, 1992), however, concludes that adjustment has restored growth in SSA to the moderate levels of the 1970s. Still, the majority view in the international development community, including the Bank, is that there is much to be desired in terms of restoring growth and social welfare to Sub-Saharan Africa (e.g. Summers (1992)).

**1'** Faini et al (1990), who estimated a modified-control-group model using cross sectional data from 93 developing countries; found similar evidence to ours, however, regarding the effect of adjustment on economic growth.
globalization, transition and diversification. We find that in many cases structural factors do not have a statistical significant impact on employment growth. However, we uncover some evidence that reforms of labor and product markets, economic policy frameworks, governance and diversification are associated with higher employment growth.

# **Labor Market Trends**

We assess trends in labor market performance with three indicators: labor force participation rates; employment ratios; and unemployment rates (Box 2).

# Box 2: Definitions of Labor Market Variables

The labor force participation rate is a measure of the proportion of a country’s working-age population that engages actively in the labor market, either by working or looking for work. It provides an indication of the relative size of the supply of labor available to engage in the production of goods and services.

The employment-to-population ratio, or employment ratio, is defined as the proportion of a country’s working-age population that is employed. It provides information on the ability of an economy to create employment.

The unemployment rate gives us the proportion of the labor force that does not have a job and is actively looking for work. It is probably the best-known labor market measure. However, the unemployment rate is viewed to be more reliable as an indicator of unutilized labor supply in developed countries than in developing countries due to informal work and underemployment. Source: KILM Manuscript 2012.

Median labor force participation rate is low in both ECA and Western Europe compared to their peers (Figure 2). But while Western Europe’s labor force participation peaked in the 2000s, ECA’s labor force participation stayed low throughout, in spite of a modest improvement in the mid2000s. In 2010, ECA is the only region with labor force participation rates of less than 60 percent. Within ECA, the Balkan and Turkey fare worst and the CIS best. However, labor force participation rates in all four subregions were no higher in 2010 than in 1995.

4