rafmacalaba/datause-encoder-data huggingface dataset:
|**IEO**|||||**WEO**|
|---|---|---|---|---|---|
|Country|CO2|GDP|CO2|GDP|Country|
|or Region|7 years|7 years|8 years|8 years|or Region|
|World|4.4%|-6.6%|3.4%|-6.1%|World|
|Industrialized C.|3.1%|-1.3%|0.9%|-1.4%|Developing C.|
|Non-OECD Asia|-1.6%|-0.5%|-0.4%|-16.8%|US and Canada|
|United States|-2.6%|-7.8%|2.1%|38.3%|OECD Pacifc|
|Canada|-1.4%|-7.4%|6.5%|-18.1%|China|
|Western Europe|14.0%|-1.4%|4.5%|66.9%|East Asia|
|Japan|4.1%|19.3%|-5.1%|-7.3%|South Asia|
|Former SU|30.8%|1.9%|-14.5%|-1.7%|Middle East|
|Eastern Europe|26.6%|-0.5%|-0.2%|19.6%|Latin America|
|Middle East|-13.9%|5.1%|0.2%|13.4%|Africa|
|Mexico|16.0%|5.3%||||
|China|4.8%|-13.3%||||
|Africa|1.1%|2.6%||||
|Latin America|-0.8%|12.7%||||

Table 4: Historical forecast errors for CO2 emissions and GDP of the International Energy Outlook 1995 (EIA, 1995) and the World Energy Outlook 1994 (IEA, 1994). The WEO regions OECD and OECD Europe could not be considered, as their composition changed significantly during the 1990s. Likewise, we excluded South Korea from the OECD Pacific region.

Asia’s GDP).<sup>8</sup> Secondly, while for the IEO the average absolute error for emissions is larger than the one for GDP, the opposite is true for the WEO. Thus, as with the data in Table 3, it cannot be asserted that either type of uncertainty (emissions or output) is necessarily lower than the other. Third, when pooled over the different regions listed in Table 4, forecast errors of emissions and GDP do not show a strong correlation: the _ρ_ corresponding to the two ‘pairs’ of forecasts from Table 4 is -0.04 and 0.31, respectively.<sup>9</sup> Table 5 summarizes this section’s findings. Four key conclusions can be drawn. First, _σE_ and _σY_ are almost always found to be below 20%. Second, there is no obvious difference in patterns of uncertainties between developing and industrialized countries, except for an apparently lower emissions uncertainty for industrialized countries. Third, no robust statement

> 8Our results are comparable to those of Lutter (2000), who finds that historical ten-year ahead emissions forecasts for the United States are subject to a 4 _._ 2% absolute error, and that ‘simulated’ forecasts for cumulative emissions of five-year periods are subject to errors between 5% and 25%, or even up to 37% (India), if the fiveyear period starts six or more years ahead in the future.

> 9To avoid double-counting, these linear correlation coefficients are computed by using only those individual regions and countries that do not overlap.

22
# **Figure 19. Deprivations in Three Labor-related Indicators, 2010–15, Percent of Population**

<!-- Start of picture text --> Labor Force Long-Term Remittance-Dependent<br>Participation Unemployment 18%<br>40% 25% 16% Other<br>Urban<br>35% 14%<br>20%<br>30% 12%<br>25% 15% 10% Rural<br>20% 8%<br>10%<br>15% 6%<br>10% 4% Yerevan<br>5%<br>5% 2%<br>0% 0% 0%<br>2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> _Source:_ ILCS 2010-15 data.

Recognizing the magnitude of emigration is necessary to understand labor market dynamics in Armenia and the experience of poverty there. Migration extends the labor market beyond national borders and helps households to mitigate idiosyncratic shocks that could otherwise lower incomes and raise unemployment. The high dependence on remittances heightens vulnerabilities to external shocks. Because a relatively large number of households in urban areas other than Yerevan receive remittances, those areas also have a larger number of households that could fall into extreme poverty if remittances were to dry up.

# **Overlap of Poverty Dimensions**

The overlap of deprivations illuminates the complexity and intensity of poverty. Despite the positive trend between 2010 and 2015, the large majority of households still experienced deprivations in many dimensions. In 2015, only 27.8 percent of Armenians did not show any deprivations, but about 7.7 percent was deprived in three or more dimensions—although compared to 2010, the incidence and intensity of nonmonetary poverty have declined with recovery from the global economic crisis that hit Armenia in 2009.

22
variable DGPs that we used and, in any event, its properties have already been examined using Monte Carlo techniques. We investigated the Two-Step Method of Moments estimator of Xiong and Chen (forthcoming), but did not find encouraging results with our sample.

# **V.1. ET-Tobit data generating process (DGP)**

For the threshold-Tobit model, we ensured that a significant number of observations would have zero values by adding a negative intercept term, -k, in the levels version of the datagenerating equation, and then transforming all realizations of the latent variable with a value below zero into zero values. We refer to this DGP as the ET-Tobit DGP because it is the model underlying the Eaton and Tamura (1994) estimator. It has the interpretation of introducing a threshold that must be exceeded before trade actually occurs. It differs fundamentally from the rounding approach used by SST (2006) and the approach of setting observations to zero randomly or for particular groups used by Martinez-Zarzoso (2013).

The data generating process for these initial simulations was equation (6) augmented with the negative intercept term, -k, that defines the ET-Tobit model (7) yi<sup>*</sup> = exp(xiβ) + εi – k ≡ exp(xiβ) ).ηi - k = exp(β0 + β1x1i + β2x2i).ηi - k i = yi<sup>*</sup> if yi<sup>*</sup> ≥ 0; yi = 0 if yi<sup>*</sup> < 0 Within these samples, we found that a value for k of 1 provides numbers of zero trade values consistent with the 40-50 percent of zero values frequently observed in analyses of total bilateral trade. As shown in Appendix Table 1, a k value of 1.5 generated higher shares of zeros. The analysis was performed in Stata 9.2, using double precision to minimize numerical errors, with samples of 1000 observations, replicated 10,000 times as in SST (2006).<sup>7</sup>

# _Semi-Parametric Estimators_

Given the pervasive uncertainty about the distribution of the errors, and the relatively poor results obtained using standard limited-dependent variable estimators, it seemed important to investigate the performance of the semi-parametric, Least-Absolute-Deviations (LAD) approach proposed by Powell (1984). Although this model has not, to our knowledge, been

> 7 Our first estimation task was to replicate the simulations of SST (2006). While our results are not exactly the same as theirs because of the stochastic nature of the analysis, they are completely consistent. The replicated simulation results are available upon request.

14
# TABLE 4A

# EXTERNAL SHOCKS

||1981-84 Rela|tive to|1970-30|1986-88**Re**|**lative**to|1970-80|.'I9-88 R|elative t|o 1981-84|
|---|---|---|---|---|---|---|---|---|---|
||Terus|Roal|Total|**Terws**|Reel|Total|**Terus**|Real|Total|
||of<br>Trade|Int.<br>Rate|Shock|of<br>**Trade**|Int.<br>Rate|Shock|of<br>**Trade**|**Int.**<br>**Rate**|Shock|
|EIAL|-10.6|-1.9|-12.4|-o.0|-3.2|-9.2|1.1|-0.2|0.9|
|CAL|-2.9|-2.0|-4.9|-3.4|-3.6|-7.0|-0.4|-0.6|-1.0|
|hAL|-10.4|-1.2|-11.6|-13.9|-2.0|-16.9|-3.3|-0.3|-3.8|

## Source: World Bank data.

Note: The total effect of **the** external shocks<sup>as</sup><sup>**a %**of CDP is computed</sup><sup>**as**the sum of</sup><sup>**the**real</sup> interest rate offect and **the** terms of trade<sup>offect. The interest rate effect is calculatedas-(r-</sup> rbas*):(debt/GDP)be, **where** r is the real interest<sup>rate computedas(i-dp/p)/(1+dp/p); rb,s is the</sup> averag resl intereit rate of the base period<sup>(1976-80) or (1981-84); i is the rat;o of intorest</sup> payments to total debt; interest payments<sup>are calculatedby adding public interest payments to</sup> private interest payments; private irterest payments<sup>**are**proxied by multiplying private debt by L</sup> (L equals the thr-4-month annualized LIBOR plus<sup>1%); private debt is estimatedbysubtracting public</sup> and publicly guaranteed debt from total debt;<sup>dp/p is 'world' inflation (proxied by the percentage</sup> change of the CNP deflator in the US, and (debt/gdp) is the ratio of debt to GOP in the year preceding the beg nning of the end reriod. The debt<sup>**dat9**correspond to total disbursed guaranteed</sup> and non-guaranteed debt. The debt and interest rate<sup>information is available startingii</sup> 1976 only. Therefore, the average for the par;cd 1970-1980 is estimated<sup>using information for the period 1975-</sup> 80.

Th- effect of the terms of trsde is computed **as**<sup>((PX/PXbas,)-13J(X/CDP)</sup> -((PM/PUbase)1)S(U/CDP)beg, where PX and PM are the average<sup>export and import price indices</sup><sup>**deflated**by the US</sup> GNP deflator, respectively; PX Jaso and PWbase<sup>**are**the average price indices of the base period; X</sup> and **U are** exports and imports o'? goods and non-factoral services;<sup>and (X/CoP)b,g</sup><sup>**and**(U/CDP)be*</sup><sup>**are**</sup> the ratios of X and W to CDP respectively in<sup>the yeor preceding the beginning of the end peFiod.</sup> All the variables **are** denom nated in current US<sup>dollars.</sup> (ARATEI.WKJ)FQ (ASNOCKT1.WK1)FQ (T23S,WP)FQ X-30-90
_Annex 3 Table 2: Covariate balanced estimates of program impact on nonbeneficiary children's_ _<u>anthropometry</u>_

||Height-for-<br>Age Z score|Stunting|Severe stunting|Weight-for-<br>Age Z score|Underweight|
|---|---|---|---|---|---|
|_Panel A: Program Impact_||||||
|Program village|-0.388**<br>(0.180)|0.104*<br>(0.061)|0.040<br>(0.033)|-0.064<br>(0.174)|0.044<br>(0.044)|
|_Panel B: Interaction with _|_saturation_|||||
|Program village|-0.076|-0.039|0.015|1.377**|-0.437***|
|(0.242)|(0.740)|(0.240)|(0.164)|(0.619)||
|Saturation|-0.757|0.159|0.130|0.456|-0.437*|
||(0.937)|(0.273)|(0.186)|(0.677)|(0.231)|
|Program<br>village*saturation|-0.638|0.279|0.056|-2.522**|0.813***|
||(1.141)|(0.374)|(0.262)|(0.994)|(0.259)|
|_Panel B: Interaction with_|_Above Median_|_Saturation_||||
|Program village|-0.294|0.049|0.033|0.294|-0.072|
||(0.242)|(0.247)|(0.082)|(0.042)|(0.220)|
|Above median<br>saturation|-0.434|0.072|0.035|0.101|-0.088|
||(0.303)|(0.095)|(0.050)|(0.286)|(0.074)|
|Program village*Above|-0.206|0.122|0.016|-0.785**|0.255***|
|median saturation|(0.356)|(0.117)|(0.068)|(0.359)|(0.085)|
|_Panel C: Interaction with_|_topquartile of _|_saturation_||||
|Program village|-0.227|0.028|0.010|0.157|0.002|
||(0.198)|(0.063)|(0.034)|(0.185)|(0.048)|
|Fourth quartile of|-0.001|0.010|-0.020|0.604**|-0.139|
|saturation|(0.347)|(0.096)|(0.064)|(0.280)|(0.086)|
|Program village*Fourth|-0.730*|0.343***|0.147|-1.338***|0.264**|
|quartile of saturation|(0.392)|(0.126)|(0.092)|(0.381)|(0.116)|
|Control Observations|145|145|145|156|156|
|Treated Observations|158|158|158|175|175|
|Control Mean|-1.124|0.317|0.069|-0.922|0.186|

*** p<0.01, ** p<0.05, * p<0.1. Estimates include municipality fixed effects and are propensity weighted following Hirano et al. (2003). Standard errors clustered at the village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey

81
Have Commercial Banks lg )red History?

by Sule Ozler*

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|Empirical Issues|4|
||2.1 Methodology|4<br>|
||2.2 a.<br>Economic Determinants<br>of Spreads<br>b.<br>Political Determinants<br>of Spreads|6<br>8|
|3.|Results|9|
||3.1 Sample Characteristics|9|
||3.2 Estimation Results|11|
|4.|Discussion|**14**|
|5.|-onclusions|17|
|Foot|notes|19|
|Refe|rences|22|
|Tabl|es|25|

* I thank Alberto Alesina, Barry Eichengreen, Peter H. Lindert, Maurice Obstfeld, Andreas Savvides,<sup>and especially Rudiger Dornbusch for</sup> helpful comments. I have also benefitted from comments of seminar participants at UC-Berkeley, UCLA, UC-San Diego, Stanford University, USC, the Federal Reserve Bank<sup>of New York, the World Bank's Debt and</sup> International Finance Division, and participants at the All UC Economic History Conference. Funding from the World Bank's Debt and International Finance Division, NSF grant SES 89-10253,<sup>and an NBER Ford</sup> Foundation Fellowship are gratefully<sup>acknowledged.</sup>

Sule Ozler John F. Kennedy School of Government liarvard University Cambridge, MA 02138
to be revisited in the light of new ideas and information, including knowledge from the broad range of experiences of developing countries.

There are concerns about the relevance of some aspects of current development research. Is it consistently addressing the most important problems facing developing countries, and is it doing so effectively? Relevance is defined not just by the questions asked and the internal validity of the answers given—establishing that the conclusions about the setting follow from the data. Relevance is also about the external validity of research findings—their validity for the purposes of drawing lessons for other settings, including scaling up. Relevance is defined as well by the usefulness of the research for addressing the questions that policy makers and clients are asking – or highlighting other questions that need to be addressed.

There are many challenges ahead in improving development data, and engagement with both the research and policy communities is crucial for meeting those challenges. There are concerns about incomplete coverage across countries and over time of some core areas of development data, including on health, education, infrastructure and gender indicators. There are also concerns about the internal consistency of development data, such as the discrepancies found between consumption aggregates and growth rates as estimated from national accounts versus those obtained from household surveys. There are concerns about the quality and coverage of public finance data, including at sub-national levels. Data on physical infrastructure stocks and government spending on infrastructure are weak in most countries.

Another generic theme in our thinking about future directions is our belief that not enough attention has been given to the need for differentiated policy recommendations for countries at different stages of development. Differences between countries in ( _inter alia_ ) physical and human capital endowments and their distribution, and associated differences in the evolution of production activities at different points in the development process, lead to the need for different implications regarding the appropriate institutions and policy frameworks. This theme cuts across all areas of development policy making.

There is a dearth of research on development strategy, including on the policies and mechanisms that have been used by some fast-growing countries to accelerate structural transformation. The Growth Commission suggests that “Growth entails a structural 10
with safety net subsidy averaged over all banks in the country and over all years between brackets): family (128 bp), company (91 bp), other financial institution (57 bp), state (36 bp), and widely (15 bp). These figures indicate that concentrated ownership links between banks and other parties, such as in the Japanese _keiretsu_ or the Korean _chaebol,_ increase risk-taking by banks, and that dispersed ownership of banks is to be preferred. The impact of state-ownership on risk-taking of a bank is found to be of an intermediate level (see Appendix, Table 3.c). Note that not all countries have banks with all four different ownership forms. In Western countries, for example, most banks are widely held. On the other extreme, in Indonesia most banks have concentrated ownership. In our sample, 32% of Indonesian banks have an owner that holds at least 20% of shares.

# **5. Empirical Analysis**

In the previous section we have quickly interpreted the summary statistics of the calculated gross safety net subsidies. Although those summary statistics show some clear patterns, we will conduct in this section a more accurate analysis of the differences of subsidies across countries, periods, and ownership forms using econometric techniques to control for bank-specific effects. We transform the variables with the logarithm operator. Calculated gross safety net subsidies, for example, are transformed as follows: _isubsidy = In(l+subsidy),_ where _subsidy_ is the gross safety net subsidy in basis points of total debt calculated using the Ronn and Verma (1986) method. With _Isubsidy_ as the dependent variable we estimate a series of OLS regression models. Our results will be presented with White's (1980) heteroskedasticity-consistent standard errors.

# **5.1 Ownership, Size and Credit Growth**

First, we regress the level of the gross safety net subsidies on a dispersed ownership dummy, country dummies and year dummies. The dispersed ownership dummy takes value one if no shareholder owns more than 5% of shares in the bank, and zero otherwise. The country dummies will control for differences in institutional environments across countries. We take the US and year 1991 as benchmark variables to prevent nulticollinearity. Table 4.a in the Appendix presents the results. We find that for the 14
Leonardo Carlos Gasparini (2021) “The distributional effect of a massive exodus in Latin America and the role of downgrading and regularization,” documentos de trabajo del cedlas.

- Mastrobuoni, Giovanni and Paolo Pinotti (2015) “Legal Status and the Criminal Activity of Immigrants,” _American Economic Journal: Applied Economics_ , 7 (2), 175–206.

- Miguel, Edward A., Bailey Palmer, Sandra Viviana Rozo Villarraga, Sarah Virginia Stillman, Emma Smith, and Abdulrazzak Tamim (2022) “The Syrian Refugee Life Study : First Glance,” Policy Research Working Paper Series 9940, The World Bank.

- Milligan, Kevin (2005) “Subsidizing the Stork: New Evidence on Tax Incentives and Fertility,” _The Review of Economics and Statistics_ , 87 (3), 539–555.

- Mincer, J. (1963) “Market prices, opportunity costs, and income effects,” _Measurement in Economics: Studies in Mathematical Economics and Econometrics in Memory of Yehuda Grunfeld_ , 67–82.

- Monras, Joan (2018) “Economic Shocks and Internal Migration,” cepr discussion papers, C.E.P.R. Discussion Papers.

- Ozler, Berk, C ̧ i ̆gdem C ̧ elik, Scott Cunningham, P Facundo Cuevas, and Luca Parisotto (2021) “Children on the move: Progressive redistribution of humanitarian cash transfers among refugees,” _Journal of Development Economics_ , 153, 102733.

- P ́erez, Santiago (2021) “Southern (American) Hospitality: Italians in Argentina and the United States During the Age of Mass Migration,” _The Economic Journal_ , 131 (638), 2613– 2628.

- Pinotti, Paolo (2017) “Clicking on Heaven’s Door: The Effect of Immigrant Legalization on Crime,” _American Economic Review_ , 107 (1), 138–68.

- Qian, Nancy (2009) “Quantity-Quality and the One Child Policy:The Only-Child Disad-

20
**2** positive and highly significant coefficient in their equation. The Freedom House _Nations in Transit_ report (1999) divides the transition countries into three groups: consolidated democracies, transitional societies, and consolidated autocracies<sup>3</sup> . It argues that political and economic freedoms are correlated with faster growth, because growth in the consolidated democracies averaged 4.7% in 1997, while transitional societies had an average growth of 1.4%, and consolidated autocracies witnessed an average economic decline of 2.8%. Clearly, an explanation for many of those empirical findings could simply be that faster growing economies are able to afford greater political freedoms, which would be consistent with the conclusion of Barro (1997) that improvements in standards of living predict increases in democracy.

Do greater political and civil freedoms help or hinder economic liberalization? Here we attempt to shed additional light on this question on the basis of the recent historical experience of 25 transition countries. We do this in two ways: we identify the channels through which political freedom could affect economic liberalization, and we then carry out empirical tests, while trying to deal with problems of simultaneity. The paper is divided into six sections. After this introduction, section II summarizes the analytical arguments-and counter-arguments-of why more political freedom could endanger the economic transition. Section III presents the estimated equation, section IV shows our empirical findings, section V provides tests of robustness and section VI summarizes the main conclusions and areas for future work.

# **II. Are Democracy and Economic Transition Incompatible? Arguments For and Against**

There are compelling arguments for why authoritarian leadership may be needed to push through reforms in the early stages of economic transition. But, there

> 2 This result may be simply a reflection of simultaneity bias, since political freedom is probably not predetermined with respect to economic liberalization.

> 3 According to Freedom House consolidated democracies are: the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland and Slovenia. They define transitional societies to include: Albania, Armenia, Azerbaijan, Bosnia and Herzegovina, Bulgaria, Croatia, Georgia, Kazakhstan, Kyrgyz Republic, Macedonia, Moldova, Mongolia, Romania, Russia, Slovakia, Ukraine and Yugoslavia. Freedom House identifies four consolidated autocracies: Belarus, Tajikistan, Turkmenistan, and Uzbekistan.

3
Policy Research Working Paper 5375

# **Abstract**

Bibliometric measures based on citations are widely used in assessing the scientific publication records of authors, institutions and journals. Yet currently favored measures lack a clear conceptual foundation and are known to have counter-intuitive properties. The authors propose a new approach that is grounded on a theoretical “influence function,” representing explicit prior beliefs about how citations reflect influence. They provide conditions for robust qualitative comparisons of influence—conditions that can be implemented using readily-available data. An example is provided using the economics publication records of selected universities and the World Bank.

This paper—a product of the Director’s office, Development Research Group—is part of a larger effort in the department to assess the impact of World Bank research. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at mravallion@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team