rafmacalaba/datause-encoder-data huggingface dataset:
31 the benchmarking model discussed above. Figure 3 plots the predicted and actual value of Private Credit of the median transition economy for the period 1995 to 2011. While in the early part of the sample period the actual value was below the predicted value, the actual value pulled ahead of the predicted value in the 2000s. In 2003, the two lines crossed, with subsequent rapid increase in Private Credit to GDP.

**Figure 3: Private Credit to GDP relative to a benchmark across transition economies**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25 Private Credit to GDP<br>20 benchmark<br>15<br>10<br>5<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009 2011<br><!-- End of picture text --> Source: Global Financial Development Indicators, own calculations The deepening process went hand in hand with an institutional upgrade, as illustrated in Figure 4, gauged by Doing Business Indicators. Specifically, the rights of creditors and investors increased, while the largest increase can be observed in the credit registry index. Most countries introduced credit registries or upgraded them significantly in the 2000s, with significant effects on firms’ access to credit (Brown, Jappelli and Pagano, 2009). However, other dimensions of the institutional framework only improved little or not at all, at least in the median transition economy. Specifically, Figure 5 shows that the bankruptcy recovery rate improved only slightly, while the cost of property registration dropped slightly and the cost of contract enforcement actually increased in the median country.

31
Table 4: Gravity Equations-Commuting and Trade Elasticities

||(1)<br>Formal sector<br>_Panel A: commuting_|(2)<br>Informal sector|
|---|---|---|
|Outcome|ln_λniF_|ln_λniI_|
|Minutes|-0.028***|-0.042***|
||(0.003)|(0.005)|
|Observations|2,257|2,280|
|R-squared|0.535|0.518|
|**Implied** _θ_|**3.11**|**4.66**|
||_Panel B: Trade_||
|Outcome|ln_πniF_|ln_πniI_|
|Minutes|-0.059***|-0.078***|
||(0.004)|(0.005)|
|Observations|2,128|2,108|
|R-squared|0.406|0.497|
|**Implied** _σ_|**5.39**|**6.94**|
|Origin -Transportation mode FE|X|X|
|Destination -Transportation mode FE|X|X|

_Notes:_ This table reports the results of a gravity equation relating commuting and trade flows at the municipality level with the average time for four different transportation modes: car, bus, metro or metrobus (brt), and walking. I estimate this regression via the PPML method to include the zeros. The first column presents the results for the formal sector the second column for the informal sector. Standard errors are clustered at the municipality of origin level and reported in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

51
<mark>in the diesel price under a binding blend mandate results in an (ambiguous) change in canola (soybean) price of a lower magnitude than under a binding tax exemption.</mark>

# **4. Empirical Illustration**

In Table 1, we empirically investigate how biodiesel and soybean oil prices respond to an increasing crude oil price, depending on which biofuel policy is binding. We use the United States as an example of a large country producer of biodiesel. The underlying data for the calibrated model pertain to the year 2010.<sup>17</sup> The first column of Table 1 presents theoretical crude oil prices ranging between 40 and 120 $/barrel. The second column presents predicted diesel prices for each level of crude oil price.<sup>18</sup> The predicted prices of biodiesel under the tax credit and corresponding crude oil prices are in the third column and are calculated using equation (1). The fourth column presents predicted soybean prices, using equation (6), associated with the biodiesel price under the binding tax credit. Consistent with the theoretical model, the predicted biodiesel and soybean oil prices rise as the crude oil (diesel) price increases.

However, under a binding blend mandate, associated with a higher crude oil price is a lower biodiesel price, and hence soybean oil price. This inverse relationship exists because higher crude oil prices translate into higher market prices for diesel which increases the marginal cost for fuel blenders who transfer the increase in their marginal costs onto fuel consumers. As a result, the diesel fuel consumption decreases, as does the need for biodiesel that is blended proportionally to the fuel. Therefore, biodiesel prices decrease, and so do soybean oil prices.

In Table 1, we assume that only one biofuel policy is in place at a time. However, because higher crude oil prices result in increasing biodiesel prices under the tax credit and decreasing prices under the mandate, we can determine the minimum crude oil price that makes the tax credit binding; in Table 1, this price is approximately $84/barrel. This break-even crude oil price is important for small developing countries because, as we have shown earlier, the crude oil price has differential impacts on commodity prices, depending on which biofuel policy binds. For example, one implication of Table 1 is that if the large country pursues a biodiesel mandate and oil prices are below $84/barrel, then an increase in the crude oil price will, _ceteris paribus_ , result in lower soybean oil prices, and hence the prices of soybeans.

> 17 In 2010, the U.S. blender’s tax credit for biodiesel was $1/gallon and the diesel tax $0.55/gallon. We set the blend mandate equal to 0.7 percent, which is equal to the observed energy share of biodiesel in the total biodiesel and diesel blend.

> 18 The predicted diesel price is calculated as: diesel price = 0.055 + 0.029 times the crude oil price; the coefficients were estimated using OLS on historical data for diesel and crude oil.

11
<mark>Carattini, S., M. Carvalho, and S. Fankhauser. 2017. How to make carbon taxes more acceptable. Policy re-port, Grantham Research Institute on Climate Change and the Environment and Centre for ClimateChange Economics and Policy, London School of Economics and Political Science, London.</mark> <mark>Cesur, Resul, Erdal Tekin, and Aydogan Ulker. "Air pollution and infant mortality: evidence from the expansion of natural gas infrastructure."</mark> _<mark>The economic journal</mark>_ <mark>127, no. 600 (2017): 330-362.</mark> <mark>Cesur, Resul, Erdal Tekin, and Aydogan Ulker. "Can natural gas save lives? Evidence from the deployment of a fuel delivery system in a developing country."</mark> _<mark>Journal of health economics</mark>_ <mark>59 (2018): 91-108.</mark> <mark>Chakravarty, Shoibal, and E. Somanathan. "There is no economic case for new coal plants in India."</mark> _<mark>World Development Perspectives</mark>_ <mark>24 (2021): 100373.</mark> <mark>Chen, Yuyu, Avraham Ebenstein, Michael Greenstone, and Hongbin Li. "Evidence on the impact of sustained exposure to air pollution on life expectancy from China’s Huai River policy."</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>110, no. 32 (2013): 12936-12941.</mark> <mark>Costa, Dora L., and Matthew E. Kahn. "Changes in the Value of Life, 1940–1980."</mark> _<mark>Journal of risk and Uncertainty</mark>_ <mark>29, no. 2 (2004): 159-180.</mark> <mark>Cragg, Michael I., Yuyu Zhou, Kevin Gurney, and Matthew E. Kahn. "Carbon geography: the political economy of congressional support for legislation intended to mitigate greenhouse gas production."</mark> _<mark>Economic Inquiry</mark>_ <mark>51, no. 2 (2013): 1640-1650.</mark> <mark>Davis, Lucas W., Alan Fuchs, and Paul Gertler. "Cash for coolers: evaluating a large-scale appliance replacement program in Mexico."</mark> _<mark>American Economic Journal: Economic Policy</mark>_ <mark>6, no. 4 (2014): 207-38.</mark> <mark>Davis, Lucas W., and Paul J. Gertler. "Contribution of air conditioning adoption to future energy use under global warming."</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>112, no. 19 (2015): 5962-5967.</mark> <mark>Dasgupta, Susmita, Benoit Laplante, Hua Wang, and David Wheeler. "Confronting the environmental Kuznets curve."</mark> _<mark>Journal of economic perspectives</mark>_ <mark>16, no. 1 (2002): 147-168.</mark> <mark>DellaVigna, Stefano, and Eliana La Ferrara. "Economic and social impacts of the media." In</mark> _<mark>Handbook of media economics</mark>_ <mark>, vol. 1, pp. 723-768. North-Holland, 2015.</mark> <mark>Deryugina, Tatyana. "How do people update? The effects of local weather fluctuations on beliefs about global warming."</mark> _<mark>Climatic change</mark>_ <mark>118, no. 2 (2013): 397-416.</mark> 17
# **1. Introduction**

Household indebtedness has grown rapidly in recent years in a number of countries in Central and Eastern Europe and the Baltic region, especially in many of the new European Union (EU) member countries. Between 2001 and 2006, for example, household debt grew at an average rate of close to 40 percent across these countries, while rising only by 11 percent in the older EU member countries. Though household debt levels in the new EU countries (about 11 percent of GDP, on average) are still not at the level of more advanced economies (close to 50 percent of GDP), there are significant variations across new EU countries. Much of the growth in household indebtedness has been driven by increasing mortgage debt, underpinned by the availability of a broad range of mortgage instruments.<sup>1</sup> On the one hand, this increase in access to credit by households can be seen as relaxing the financing constraints of households, helping smooth their consumption over the business cycle and life time; on the other hand, household over-indebtedness is typically seen as a root cause of the current global financial distress. This paper uses household data from the EU Statistics on Income and Living Conditions (SILC) to address the welfare and stability implications of increasing household indebtedness in the new EU member countries.<sup>2</sup> The macro- and microeconomic consequences of rising household access to credit and indebtedness can be significant. A priori, however, it is not clear whether benefits or costs dominate. On the one hand, the rising indebtedness could reflect the benefits of financial deepening, allowing households to smooth consumption and acquire home ownership without significant previous saving periods. A dwelling can constitute an important, if not the most important, asset for households and access to and cost of financing the acquisition of a dwelling has therefore important repercussions for household welfare. On the other hand, while a dwelling can be the most important asset, the loan on

> 1 See for example European Central Bank (2007) and Roy (2008).

> 2 See ECB (2009) for a discussion on housing finance in the Euro zone.

2
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# **6 Simulations**

This last section of the paper uses the estimated coefficients from table 6 to compute, for each student, the total _test-score gain_ attributable to peer effects. The aim is to analyze how the average performance and the gap between the poorest and the wealthiest group change as the degree of segregation varies. The change in mean test score will depend on whether the losses incurred by poor students are compensated by the gains accruing to the rich. Two elements are key: (1) the coefficients on peer variables for each group, and (2) the proportion of students in each socioeconomic group in the whole distribution. I will construct a (fictitious) sample of students which have the same proportion of pupils in each category of socioeconomic status as found in Argentina. Each student is then assigned randomly to a school of size equal to the median class size in the country (27). In the present exercise, each school has only one class. For each student, I compute the proportion of peers belonging to each socioeconomic group that results from the random assignment.

The random allocation of pupils will determine a specific degree of segregation across schools. I use the same segregation index as in the introduction, defined as the proportion of the total variance in number of schools that is ‘explained’ by the variance between schools. This particular degree segregation will be associated with a certain average gain in test scores from peers, and a gap between the average gain for the wealthiest group (upper) and the average gain for the poorest one (lower). I repeat this exercise reallocating students into schools randomly, and computing once again the degree of segregation, the average gain in test score and the average gap in the gains. I do this fifty times, enough to have a sufficient spread in the segregation index. The results are presented in figure 3 and table 9. The vertical lines in the graph are used as reference and indicate the lowest and highest degree of segregation found in the Argentine provinces. Because these results are dependent on the original distribution of socioeconomic characteristics of pupils, I also repeated the same exercise for two other distributions, one corresponding to one of the richest districts in the country, the City of Buenos Aires, and a second for one of the poorest provinces, Santiago del Estero.

Two main features emerge from these graphs. First, as the degree of segregation across schools increases both the average and the gap in test scores rise. This means that, at least in Argentina, there is a trade-off between efficiency (increasing the mean) and equity (decreasing the gap). Second, the rate at which the gap increases is much larger in the case of 26
# **References**

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- Adrian, Tobias and Markus K. Brunnermeier (2009), "CoVaR." Federal Reserve Bank of New York Staff Report 348.

- Adrian, Tobias and Hyun Shin (2008). "Financial Intermediary Leverage and Value at Risk," Federal Reserve Bank of New York Sta¤ Report No. 338.

- Allen, Franklin and Douglas Gale (2000) “Financial Contagion”, Journal of Political Economy , 108, 1-33.

- Ang, Andrew and Joseph Chen (2002) "Asymmetric Correlations of Equity Portfolios", Journal of Financial Economics , 63, 443-494

- Avesani, R., Pascual, A. G., Li, J., (2006).”A new risk indicator and stress testing tool: A multifactor Nth-to-default CDS basket”. IMF Working Paper No. 06/105.

- Battiston, S., D. Delli Gatti, M. Gallegati, B. Greenwald, and J. Stiglitz. (2009). “Liaisons Dangereuses: Increasing Connectivity, Risk Sharing and Systemic Risk”, NBER Working Paper No. 15611

- Beck, Thorsten; Demirgüç-Kunt, Asli; Levine, Ross, (2000), "A New Database on the Structure and Development of the Financial Sector" The World Bank Economic Review 14, 597605.

- Bekaert, G., R. J. Hodrick, and X. Zhang, (2010), “Aggregate idiosyncratic volatility”, Working Paper, Columbia University.

- Bekaert, Geert, and Xiaozheng Wang, (2009),” Globalization and Asset Prices”, Working Paper, Columbia University.

- Bekaert G. and Wang X. (2010), "Inflation Risk and the Inflation Risk Premium", Economic Policy, 25(64), October, p. 755-806.

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- Bongini, P., Laeven, L. & G. Majnoni (2002) ”How good is the market at assessing bank fragility? A horse race between different indicators”, Journal of Banking & Finance , (26)5: 1011-1028.

16
# **Table 7.  Product typology and statistics (% of country-product pairs)**

||**RCA > 1**|**RCA < 1**|
|---|---|---|
|**LCA > 1**|Type 119.2%|Type 219.8%|
||Revealed potential|Untapped potential of<br>diversification|
|**LCA < 1**|Type 37.1%|Type 453.9%|
||Declining sectors?|Marginal sectors|

For each country the following derived indicators can be produced:

1. The number of products in each of the four categories: Type 1-4 #

2. The share of latent trade in each of the four categories Type 1-4 %

# **_Competitiveness as the Realization of the Latent Advantage_**

The data file provided in Annex 5 includes:

- Indices by country: Theil actual, Theil latent, KL Distance, Overlap, Type 1-4 #, Type 1- 4 %,

- The “discovery” matrix with the product typology by country.

The main observations are the following.

**LCA metrics.** The country-level indices of KL distance, overlap, and GL are expectedly related as they all measure how close latent and actual export compositions are. As expected, distance or overlap between actual and latent are also related to the relative importance of the untapped potential for diversification (Type 2).

Most significantly, the KL distance and the overlap indices seem to capture much of the export “competitiveness”, with the KL distance being slightly better:

- i. Both indicators are highly correlated with commonly accepted indicators of competitiveness such as the WEF Global Competitiveness index (Fig 9).

- ii. The ranking of countries in the sample is consistent with the general knowledge of countries’ exports (Table 10), despite a few unintuitive cases such as the Nordic countries.

- iii. The KL distance is also associated to a higher degree of diversification of their actual trade for countries close to their latent potential, as measured by the Theil index (Fig 10).

- iv. The metric of distance between latent and actual trade is better associated with generally accepted competitiveness outcomes than the diversification (Theil) index.

28
Slemrod, J., 1995. “What Do Cross-Country Studies Teach about Government Involvement, Prosperity, and Economic Growth?” Brookings Papers on economic Activity, pp.373-431.

Solow, Robert M. 1956. A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, Vol. 70, No. 1. (Feb., 1956), pp. 65-94 Sturm, J.-E. and De Haan, J. 2001. How robust is the relationship between economic freedom and economic growth really? Applied Economics 33: 839–844.

Sturm, J. E. and J. De Haan, 2005. Determinants of long-term growth: new results applying robust estimation and extreme bounds analysis. Empirical Economics 30: 597–617.

Tavares J. and R. Wacziarg, 2001. How democracy affects growth. Eur Econ Rev 45(8):1341–1378 Vega-Gordillo M. and J. L Alvarez-Arce, 2003. Economic growth and freedom: a causality study, Cato Journal 23: 199-215.

World Bank, 2006. Where is the Wealth of Nations? Measuring Capital for the 21st Century, The World Bank, Washington DC.

31
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incidence per capita for each quintile as a result of a one dollar increase in aggregate program spending (Lanjouw and Ravallion 1999).

The Lanjouw-Ravallion method described above relies on a single cross-sectional data set. It is possible to infer marginal incidence of a program based on simple comparison of incidence at different points in time. This comparison requires the availability of at least repeated cross-sectional data. Take the case of a given level of education for instance and let **Rqt** be the number of students from quantile **q** enrolled in that level of schooling in year t=1,2. Let **Rt** stand for total school enrollment for that level in year t. The changes in incidence over time across quantiles can be looked at in one of the following ways (van de Walle 2003): _MBI q_ = ⎛⎜<sup>⎜</sup> _Rq_ 2 − _Rq_ 1 ⎞⎟<sup>⎟</sup> or ⎝ _R_ 2 _R_ 1 ⎠ time include Hammer, Nabi and Cercone (1995) for Malaysia, and Al-Samarrai and Zaman (2007) for Malawi. In particular, the Malawi study compared the incidence of public expenditure on education (primary and secondary level) between 1990/91 and 1997/98. It was found that, the changes in incidence were pro-poor. The improvement for the primary level of education is attributed to the abolition of fees decided by the government in 1994.

Finally, one can apply discrete choice models<sup>35</sup> to marginal impact analysis by estimating the probability of participation in a program. Participation is assumed to provide the highest level of utility relative to available options. Thus, the estimated probability is viewed as the expected demand for the services provided by the program. In the context of education for instance, the underlying utility is usually taken to be a function of school quality, household and individual characteristics. Estimated probabilities are interpreted as expected enrollment rates. Changes in these probabilities (of participation) induced by program expansion provide a measure of marginal incidence that can be reported by quantile. Alternatively, one can estimate the underlying random utility model to conduct the analysis in terms of changes in compensating variation induced by the policy reform.

> 35 Section 4 contains a more detailed discussion of such models.

37
24 global agricultural markets than standard NRAs and CTEs (and even more so than the OECD’s producer and consumer support estimates, which are expressed as a percentage of distorted rather than undistorted prices and so are smaller than their NRA and CTE counterparts). More specifically, the TRI offers a much truer indication of the world trade effects of government interventions in the markets for traded products, by properly accommodating trade subsidies alongside trade taxes; and the WRI offers a much truer indication of the global welfare effects of government interventions in the markets for traded products, by also properly taking into account the fact that the welfare cost of a price distortion is proportional to the square of the tax or subsidy rate.

The database we have used provides greater coverage in terms of commodities, countries and instruments than in any previous estimates of the effects of distortions of global agricultural commodity markets. They reveal in which markets the reduction in imports and the loss of welfare is greatest, and provide a breakdown into the components due to distortions of producer and consumer prices separately.

True, these two indexes have been calculated with the help of a number of simplifying assumptions, most notably that each country is small and that its price elasticity of supply (demand) for a particular product is the same as that for every other country, and that cross-price elasticities are zero. But that is what trade negotiators typically assume when they attempt to calculate the trade effects of market access ‘concessions’ they are considering exchanging. It is also commonly what would be assumed when calculating, for the Arbitrator of a trade dispute settlement case, the