rafmacalaba/datause-encoder-data huggingface dataset:
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Bourguignon, F. and Da Silva, L.A.P. (2003) _The impact of economic policies on poverty and income distribution: evaluation techniques and tools_ . World Bank Publications.

Castañeda, G., Chávez-Juárez, F. and Guerrero, O.A. (2018) ‘How do governments determine policy priorities? Studying development strategies through spillover networks’, _Journal of Economic Behavior & Organization_ , 154, pp. 335–361.

Ceriani, L. and Gigliarano, C. (2016) ‘Multidimensional well-being: A Bayesian Networks approach’, _ECINE Society for the Study of Economic Inequality; WP_ , 399. Available at: http://www.ecineq.org/milano/WP/ECINEQ2016-399.pdf (Accessed: 7 June 2024).

Cristelli, M. _et al._ (2013) ‘Measuring the intangibles: A metrics for the economic complexity of countries and products’, _PloS one_ , 8(8), p. e70726.

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Deegan, J., Broekel, T. and Fitjar, R.D. (2021) ‘Searching through the Haystack: The relatedness and complexity of priorities in smart specialization strategies’, _Economic Geography_ , 97(5), pp. 497–520.

Duclos, J.-Y., Tiberti, L. and Araar, A. (2018) ‘Multidimensional Poverty Targeting’, _Economic Development and Cultural Change_ , 66(3), pp. 519–554.

El-Maghrabi, M.H. _et al._ (2018) ‘Sustainable Development Goals Diagnostics: An Application of Network Theory and Complexity Measures to Set Country Priorities’. Rochester, NY. Available at: https://papers.ssrn.com/abstract=3238315 (Accessed: 29 May 2023).

Gallardo, M. (2022) ‘Measuring vulnerability to multidimensional poverty with Bayesian network classifiers’, _Economic Analysis and Policy_ , 73, pp. 492–512.

Guerrero, O.A. and Castañeda, G. (2024) _Complexity economics and sustainable development: A computational framework for policy priority inference_ . Cambridge University Press. Available at: https://books.google.com/books?hl=en&lr=&id=SjXuEAAAQBAJ&oi=fnd&pg=PT12&dq=info:H1x VbwOFbngJ:scholar.google.com&ots=vCCYqnmHAU&sig=nfs1EqEPJlXDaDUCG8gLS5tlTyo (Accessed: 2 July 2024).

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33
- 10 -

same road segment, some shipping carriers may drive faster or transport cargos at lower prices than others.

21. The summary statistics are shown in **Table 1** . Our dependent variable is the road transport freight costs in U.S. cents per km. The freight costs include both vehicle transport costs, such as drivers and fuel costs, parking fees and minor repairs, and other trade and administrative costs at intermediate nodes, such as border crossing and traffic police checkpoints. All kinds of expenditures are included (see ADB (2014)). The costs are converted to U.S. cents and normalized to 2010 constant dollars using the U.S. consumer price index. The average freight cost is 4.57 U.S. cents per ton-km with a standard deviation of 2.41. It is distributed in a highly skewed manner around 4 to 5 cents ( **Figure 10** ). If a heavy truck with a maximum capacity of 20 tons is fully loaded, the average shipping cost for 100 km distance would be approximately US$91.

22. The distance, average speed and freight volume data also come from the CAREC CPMM database. Our sample is focused on long-haul cargos. The average distance is 170 km. The average speed is 50 km per hour. Note that the speed may differ across routes even though the freight operator, i.e., driver is the same. On the other hand, the freight volume is generally unchanged across the routes as long as they constitute the same shipment. The average cargo weight is 18.2 tons. As discussed above, most common vehicles in our sample data are 20-ton trucks. Thus, this variable is heavily concentrated to 20 tons or less.

23. The region is diverse in topographic complexity. The average vertical difference in elevation between origin and destination is about 144 meters. About 20 percent are international shipments.

24. Finally, our data comprises a wide variety of commodities and goods. All cargos are classified into five groups in addition to empty cargo: (i) agricultural commodities and food products, (ii) clothing, apparel and textiles, (iii) consumption and other goods, (iv) equipment (including vehicles), and (v) minerals, metals and materials. In our sample, agricultural commodities and food products account for about 46 percent.
- 18 -

funds: KHB loans, capital gains from the sale an earlier house, the return of a chonsei deposit, financial savings, assistance for relatives, loans frobi other financial institutions (not necessarily second mortgage loans), the chonsei deposit coming from a tenant, and other sources. The income of the borrower, the price of the unit and the ratio of housing price to household income show the link between a strategy and who uses it. There is a clear difference between firsttime buyers and borrowers who sold an earlier home.

36. Over 28 strategies were actually ir. use in 1985; the 12 most frequent covering 83.5 percent of sample borrowers are presented in Table 3. The data clearly show the inefficient and fragmented methods of financing housing. KHB loan-to-value (LTV) ratios are very low and consistently below 30 percent. This defeats the first-time ownership objective of KHB: less than half the borrowers were first time owners. This lending strategy reflects the monopolistl.- fosition of KHB, its public status, and its lack of funds. KHE's n.&formance is judged according to the number of loans it makes. Besides, low LTV ratios minimize default risk and eliminate the need for income tests, thereby greatly reducing administrative costs.

37. Among the 47 percent of the borrowers who were first time home-owners, the majority (49 percent) relied on their previous chonsei deposit and other financial assets. The chonsei deposit provided 28 percent of the funds. Those with lower incomes had to sublet part of their unit to finance their unit, their house/income ratio was the highest in the group at 5.5 times. This confirms that chonsei renters are used in the case of high housing price/income ratios. The general survey of the Korean population and the surveys of KHB borrowers clearly show the large degree of fragmentation and inefficiency which could be eliminated by the financial liberalization of housing finance and the effective development of a mortgage market.

# V. COMPOUNDING FINANCIAL REPRESSION WITH RIGID LAND REGULATIONS

38. Korea is one of the three economies with the highest population densities in the world, the other two being Taiwan and Bangladesh. Therefore some would argue that one should not be surprised to find that the ratio of the value of a unit to the income of the household is high. Also, were not the high rates of return in the land market caused by the extremely fast pace of growth of the urban population and the rapid rise of household incomes? The empirical evidence supports the view that rapid land appreciation was caused by more than rapid urbanization and the scarcity of urban land in Korea. This evidence is that land use policies have compounded the effects of financial repression, restricted the supply of housing, helped raise rents and thus accelerated the rise of the relative price of housing compared to household incomes.
# **Box 1. Variable definitions used in the translog cost function**<sup>**for Punjab agriculture, 1971-94**</sup>

# **_Independent variables_**

- _q=_ output = Crop output measured in divisia index number of<sup>production [1965=100]</sup>

- WI fertilizer =Divisia index of N, P, and K prices of nutrient<sup>and spray prices</sup>

- **w2** = water = Index of tubewell water price

- _w 3_ =labor = Index of wage rate of labor

- _w 4_ =land = Divisia index of annual rent of irrigated<sup>and unirrigated land</sup>

- **_w5_** = machine = Index of tractor prices

- w6 = animal = Index of feed, fodder, interest, and miscellaneous<sup>animal costs per standard animal unit</sup>

- S 1 = soil organic matter = Index of organic matter content in the<sup>soils (average of all the observations in a</sup> district)

- _S2_ = soil phosphorus = Index of available phosphorus in<sup>the soils (average of all the observations in a district)</sup> **_S3_** = salts in soil = Index of total soluble salts in the soils<sup>(average of all the observations in a district)</sup>

- S4 _=_ electroconductivity = Index of electroconductivity of water<sup>(average of all the observations in a district)</sup>

- II= modem variety = Index of proportion of the<sup>total wheat area under modem wheat varieties</sup>

- **I2** = cropping intensity = Index of cropping intensity defined<sup>as total cropped area divided by net sown area</sup> = education = Index of percentage of farmers who can read and write

- = road = Index of the inverse of the average distance of a village<sup>from a road.</sup>

- _T_ = trend at the cropping system level = Regional time<sup>trend variable. Its value for a specific region was equal</sup> to trend (0,1,2,...for 1965,1966,1967....), and zero otherwise

- D =district dummy = Set of district dummy variables<sup>having a value of one for a district, and zero otherwise.</sup>

# **_Dependent variables_**

**Si** = fertilizer factor share = is (total cost of all types of<sup>fertilizer nutrients and spray cost)+(total farm</sup> production expenditure)

- **s2** = water factor share = (total cost of tubewell operating<sup>cost and canal water cost)+(total farm production</sup> expenditure)

- **S 3** = labor factor share = (total cost of labor including family<sup>labor evaluated at the unskill wage rate)+(total</sup> farm production expenditure)

**_S4_** = land factor share = (rent of irrigated and unirrigated land)+(total<sup>farm production expenditure)</sup>

- _S5_ = machine factor share = (total operating cost of tractor,<sup>harvester, threshing, and bullock)+(total farm</sup> production expenditure)

**S 6** = animal factor share = (interest and miscellaneous animal<sup>cost)+(total farm production expenditure)</sup>

- _y,_ = revenue share = (marketing revenue from all crops)+(total<sup>farm production expenditure)</sup>

- C = total cost = Index of farm production expenses (1965=100),<sup>which includes total chemicals, water, labor,</sup> land, machinery, and draft power.

19
Table 2: Gender results with text slant, career vs family measure

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Def. win|Def. win|Def. win|Def. win|
|Judge maj. female|-0.0481<sup>_⇤⇤⇤_</sup>|-0.0479<sup>_⇤⇤⇤_</sup>|-0.0477<sup>_⇤⇤⇤_</sup>|-0.0435<sup>_⇤⇤⇤_</sup>|
||(0.0138)|(0.0138)|(0.0138)|(0.0138)|
|Pla. maj. female|-0.0505<sup>_⇤⇤⇤_</sup>|-0.0486<sup>_⇤⇤⇤_</sup>|-0.0500<sup>_⇤⇤⇤_</sup>|-0.0372<sup>_⇤⇤⇤_</sup>|
||(0.0117)|(0.0118)|(0.0117)|(0.0116)|
|Def. maj. female|-0.00350|-0.00635|-0.00619|-0.00228|
||(0.0117)|(0.0117)|(0.0117)|(0.0116)|
|Judge maj. fem. X pla. maj. fem.|0.00171|0.00211|0.00163|0.000857|
||(0.0180)|(0.0182)|(0.0181)|(0.0177)|
|Judge maj. fem. X def. maj. fem.|0.0460<sup>_⇤⇤⇤_</sup><br>(0.0174)|0.0440<sup>_⇤⇤⇤_</sup><br>(0.0169)|0.0440<sup>_⇤⇤_</sup><br>(0.0169)|0.0430<sup>_⇤⇤_</sup><br>(0.0168)|
|Slant against women, career vs family|0.0110|0.0316|0.0457|-0.0358|
||(0.0823)|(0.0815)|(0.0828)|(0.0840)|
|Pla. maj. fem. X Slant against women|-0.0645||-0.0537|-0.0160|
||(0.0852)||(0.0866)|(0.0884)|
|Def. maj. fem. X Slant against women||-0.156<sup>_⇤_</sup>|-0.152<sup>_⇤_</sup>|-0.146<sup>_⇤_</sup>|
|||(0.0856)|(0.0861)|(0.0828)|
|DV mean|||||
|Court-year FE|Yes|Yes|Yes|Yes|
|Ethnicity dummies|No|No|No|Yes|
|Other controls|No|No|No|Yes|
|Observations|18205|18205|18205|18205|

The regressions test whether defendants/plaintiffs are more likely to lose if they are female and the judge is slanted against females in their writing. The coefficients of interest are on the interaction terms in the last two rows. The measure of slant against women is based on the judges’ stereotypical association of women with family-based qualities rather than career-based qualities. All columns are based on a linear regression model. For specification details, see equation 3. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = plaintiff, def. = defendant, maj. = majority.

15
**Pollcy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS977 Income Security for Old Age:<br>Conceptual<br>Background<br>and Major Is|Estelle James<br>sues|September<br>1992|D. Evans<br>37496|
|WPS978 How Restricting<br>Carbon Dioxide<br>and Methane Emissions<br>Would Affec<br>the Indian Economy|Charles R. Blitzer<br>t R. S. Eckaus<br>Supriya Lahiri<br>Alexander Meeraus|September<br>1992|WDR Office<br>31393|
|WPS979 Economic<br>Growth and the<br>Environment|Dennis Anderson|September<br>1992|WDR Office<br>31393|
|WPS980 The Environment:<br>A New Challenge<br>for GATT|Piritta Sorsa|September<br>1992|WDR Office<br>31393|
|WPS981 After Socialism and Dirigisme:<br>Which Way?|Andr6s Solimano|September<br>1992|S. Fallon<br>37947|
|WPS982 Microeconomics<br>**of**Transformation<br>in Poland: A Survey of State<br>Enterprise Responses|Brian Pinto<br>Marek Belka<br>Stefan Krajewski|September<br>1992|S. Husain<br>37139|
|WPS983 Legal Reform for Hungary's Private<br>Sector|Cheryl W. Gray<br>Rebecca J. Hanson<br>Michael Heller|October 1992|R. Martin<br>39065|
|WPS984 Barriers to Portfolio Investments<br>in Emerging<br>Stock Markets|Asli DemirgOg-Kunt<br>Harry Huizinga|October 1992|W. Patrawimolpon<br>37664|
|**WPS985**Regional Integration,<br>Old and<br>**New**|Jaime de Melo<br>Claudio Montenegro<br>Arvind Panagariya|October 1992|D. Ballantyne<br>37947|
|WPS986 The Administration<br>ofRoad User<br>Taxes in Developing<br>Countries|Roy Bahl|October 1992|J. Francis-O'Connor<br>35205|
|WPS987 How the Epidemiological<br>Transition<br>Affects Health Policy Issues in<br>**Three**Latin American Countries|Jose Luis Bobadilla<br>Cristina de A. Possas|October 1992|0. Nadora<br>31091|
|WPS988 Economic<br>Valuation and the<br>Natural Worla|David Pearce|October 1992|WDR Office<br>31393|
|WPS989 The Indian Trade Regime|M. Ataman Aksoy|October 1992|R. Matenda<br>35055|
|WPS990 Protection and industrial Structure<br>in India|M. Ataman Aksoy<br>Francois M. Ettori|October 1992|R. Matenda<br>35055|
|WPS991 Environmental<br>Costs of Natural<br>Resource Commodities:<br>Magnitude<br>and Incidence|Margaret E. Slade|October 1992|WDR Office<br>31393|
Dietz, T., Gardner, G. T., Gilligan, J., Stern P. C., Vandenbergh, M. P. (2009). Household actions can provide a behavioral wedge to rapidly reduce U.S. carbon emissions. _Proceedings of the National Academy of Sciences_ .

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<mark>Dunlap, R. E., & Van Liere, K. D. (1978). The new environmental paradigm: A proposed measuring instrument and preliminary results</mark> _<mark>. Journal of Environmental Education, 9,</mark>_ <mark>1019.</mark> Ellsberg, D. (1961). Risk, ambiguity and Savage axioms. _Quarterly Journal of Economics, 75,_ 643-79.

Ellwood, C.A. (1901). The Theory of Imitation in Social Psychology. _American Journal of Sociology,_ 6, 721-741.

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Habermas, J. (1972). _Knowledge and Human Interests_ (trans. J. Shapiro). London: Heinemann.

39
**Table 3. Soundness of the Banking Sector: Non-performing Loans** (Percent of total loans, end of period)

||1990|1991|1992|1993|1994|1995|1996|1997|1998|
|---|---|---|---|---|---|---|---|---|---|
|_Central and East_|_ern Europe_|||||||||
|Bulgaria|54||||7|13|15|13|10|
|**Czech Rep.**||2 /l|19 /1|23/1|37|33|30|27|29|
|**Hungary**||||29|28|20|12|8||
|**Macedonia**||||80||44||36||
|**Poland**||16|30|29|28|21|13|10|10|
|_Baltics_||||||||||
|Estonia||||7|3|3|2|1|1|
|Latvia||||5|10|19|20|10||
|**Lithuania**|||||27|17|32|28||
|CIs||||||||||
|**Georgia**|||||24|41|7|7||
|**Kazakhstan**||||||33|41|25|27|
|Kyrgyz**Rep.**|||||92|72|26|8||
|**Ukraine**/2|||||5|13|12|11||

/I Estimates reported in Capek (1994). According to other estimates, non-performing loans were 50-66% of total loans in the early 1990s (OECD, 1996).

/2 Official data that probably underestimate the actual share of non-performing loans.

_Source: EBRD Transition Report, 1998; International Monetary Fund; Central Banks._ There were significant differences in the magnitude of crises. For those crises arising from nonperforming loans inherited from the centralized system, estimates of bad loans as a share of total loans range from about 21 percent in _Hungary_ to 50-66 percent in the _Czech Republic._ For those episodes of acute financial distress, not related to inherited bad debt, the ratio of bad loans in total loans varied from 40 percent (in _Georgia_ in 1995) to more than 90 percent (in the _Kyrgyz Republic_ in 1994). While quantitative information on the magnitude of bank runs is not available for those crises involving liquidity problems, it appears that on some occasions liquidity problems were widespread (as in _Bulgaria_ in 199697 and in _Kazakhstan_ in 1996), while in others they were limited to one or two banks (as in _Hungary_ in 1997 and in _Bulgaria_ in 1995).

With regard to the ownership structure of the financial institutions involved in banking crises, both state-owned and private banks incurred insolvency and liquidity problems. However, in government restructuring programs directed to clean up banks' portfolios from non-performing loans inherited from the Socialist period, mainly state-owned banks were involved.

7
O.A.M.D.G.

||Pr(m=1, p=1)|0.06|Pr(m=1, p=0)|0.05|Pr(m=0, p=1)|0.47|
|---|---|---|---|---|---|---|
||**dy/dx**|**X**|**dy/dx**|**X**|**dy/dx**|**X**|
|_Violence_|||||||
|public order problem|0.008|0.66|0.007|0.66|-0.010|0.66|
|guerrilla attack rate|-0.002|1.22|-0.001|1.22|-0.001|1.22|
|(guerrilla attack rate)2|0.0002|10.61|0.0002|10.61|-0.0001|10.61|
|homicide rate|0.0004|49.28|0.0004|49.28|-0.0006|49.28|
|(homicide rate)2|-2.25E-06|5,433.77|-2.47E-06|5,433.77|5.24E-06|5,433.77|
|_Municipality controls_|||||||
|grouped settlement|-0.002|0.40|0.006|0.40|-0.037|0.40|
|rural sparse settlement|-0.007|0.09|0.005|0.09|-0.048|0.09|
|Oriental Region|-0.011|0.26|-0.018|0.26|0.063|0.26|
|Central Region|-0.002|0.16|-0.017|0.16|0.091|0.16|
|Pacific Region|-0.014|0.09|-0.019|0.09|0.064|0.09|
|altitude|5.71E-06|579.22|4.60E-06|579.22|-4.83E-06|579.22|
|altitude2|-1.68E-09|862,343.00|7.32E-10|862,343.00|-9.20E-09|862,343.00|
|percentage of people lowest two strata|0.029|0.66|0.024|0.66|-0.026|0.66|
|index of quality of life|-0.001|26.02|-0.0003|26.02|-0.0033|26.02|
|capture rate|-1.33E-05|482.72|-1.07E-05|482.72|1.12E-05|482.72|
|_Household/ head variables_|||||||
|age|0.005|44.78|0.002|44.78|0.004|44.78|
|age2|-3.46E-05|2,167.12|-2.30E-05|2,167.12|4.39E-06|2,167.12|
|married|0.015|0.33|0.002|0.33|0.037|0.33|
|female|0.021|0.19|0.032|0.19|-0.076|0.19|
|some or complete primary education|-0.010|0.57|-0.006|0.57|-0.001|0.57|
|some secondary or more education|-0.018|0.14|-0.010|0.14|-0.018|0.14|

Results-Table 4: Marginal effects after bi-variate Probit on migration and saving, income definition (1) When considering the mere participation in savings, the guerrilla attack rate maintains the shape, but varies in significance with the income definition, being insignificant for income definition (1), but highly significant for income definition (2). A public order problem appears to increase participation in savings for the narrower income definition.

Concerning migration, the homicide rate shows a bell-shaped influence, consistent with the physical impact of violence encouraging migration, and with the findings of Schultz (1971). The capture rate shows a negative effect. Both results are in line with Ibañez and Vélez (2004) who find that homicides spur migration, while police activity prevents it. However, we miss the displacing effect predicted for guerrilla violence and also found by Ibañez and Vélez (2004). For migration, the capture rate shows a negative sign, as do the Oriental and Pacific regions. Inequality, as far as measured by the share in the two lowest strata, appears to spur migration.
# **Figure 8.  Saving in Latin America: Actual and Benchmarks 1990-2012**

<!-- Start of picture text --> 22<br>21<br>20<br>19<br>18<br>17<br>16<br>15<br>14<br>13<br>12<br>Actual Observed Benchmark Equilibrium Benchmark<br>in percent of GDP<br>1990-1992 1993-1995 1996-1998 1999-2001 2002-2004 2005-2007 2008-2010 2011-2012<br><!-- End of picture text --> Sources: Authors' calculations based on UN, WDI and Institutional Investor data. Notes: Sample includes Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, El Salvador, Guatemala, Mexico, Peru, and Uruguay. Equilibrium (observed) benchmarks are derived from the predicted values of the saving regression based on the equilibrium (observed) values of the rate of growth and real exchange rate. See Section 3 for more details on how the benchmarks are calculated.

36