rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6.B. Model comparison: Root mean squared error of copper price forecasts**

(U.S. dollars per metric tonne)

||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|
|**Approach**<br>**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus<br>3|440.6|...|...|...|...|...|
|Futures<br>3|*|145.2|...|...|...|...|
|Bivariate correlations<br>3|*|*|74|...|...|...|
|BVAR<br>3|*|*|*|789.8|...|...|
|OEM<br>3||*|*|*|334.2|...|
|Machine learning<br>3|*|*|*|*||277.1|
|Consensus<br>6|477.2|...|...|...|...|...|
|Futures<br>6|*|146.2|...|...|...|...|
|Bivariate correlations<br>6|*|*|865.8|...|...|...|
|BVAR<br>6|*|*||872|...|...|
|OEM<br>6||*|*|*|362.2|...|
|Machine learning<br>6||*|*|*||387.1|
|Consensus<br>9|551.4|...|...|...|...|...|
|Futures<br>9|*|148.7|...|...|...|...|
|Bivariate correlations<br>9|*|*|1212.2|...|...|...|
|BVAR<br>9|*|*||1165.7|...|...|
|OEM<br>9||*|*|*|406.7|...|
|Machine learning<br>9||*|*|*||684.4|
|Consensus<br>12|637.1|...|...|...|...|...|
|Futures<br>12|*|152.9|...|...|...|...|
|Bivariate correlations<br>12|*|*|1189.7|...|...|...|
|BVAR<br>12|*|*||1281.9|...|...|
|OEM<br>12||*|*|*|476.6|...|
|Machine learning<br>12|*|*|||*|1062.3|
|Consensus<br>15|701.3|...|...|...|...|...|
|Futures<br>15|...|...|...|...|...|...|
|Bivariate correlations<br>15|||||||
|<br><br>BVAR<br>15|...<br>*|...<br>...|...<br>...|...<br>1791|...|...<br>...|
|OEM<br>15||...|...|*|528.1|...|
|Machine learning<br>15|*|...|...||*|1385.7|
|Consensus<br>18|768.7|...|...|...|...|...|
|Futures<br>18|...|...|...|...|...|...|
|Bivariate correlations<br>18|...|...|...|...|...|...|
|BVAR<br>18|*|...|...|1731.8|...|...|
|OEM<br>18||...|...|*|576.5|...|
|Machine learning<br>18|*|...|...||*|1637|
|Consensus<br>21|841.1|...|...|...|...|...|
|Futures<br>21|...|...|...|...|...|...|
|Bivariate correlations<br>21|...|...|...|...|...|...|
|BVAR<br>21|*|...|...|1629.9|...|...|
|OEM<br>21||...|...|*|604|...|
|Machine learning<br>21|*|...|...||*|1805.7|
|Consensus<br>24|858.6|...|...|...|...|...|
|Futures<br>24|...|...|...|...|...|...|
|Bivariate correlations<br>24|...|...|...|...|...|...|
|BVAR<br>24|*|...|...|1721.9|...|...|
|OEM<br>24||...|...|*|728.7|...|
|Machine learning<br>24|*|...|...||*|1924.5|

_Source_ : World Bank.

_Note_ : The forecast period is 2015Q1-2022Q1. Forecasts for OEM are only available at the semi-annual frequency; forecasts for all other approaches are available at the quarterly frequency. Diagonal entries indicate average RMSE estimates for the respective approach. * indicates significant difference between the forecasts of any pair of approaches, at the 5 percent significance level according to an F-test (RMSE).

43
**Table 8: Power outages and corruption**

||**Dependent v**<br>|**ariable: Tot**<br>**in a**<br>|**al hours of p**<br>**month**<br>|**ower outages**<br>|**Dep**<br>|**endent vari**<br>|**able: Bribery**<br>|**rate**<br>|
|---|---|---|---|---|---|---|---|---|
||**Full s**|**ample**|**SME firm**<br>**sample**<br>**(****_SME_=1)**|**Large firm**<br>**sample**<br>**(****_SME_=0)**|**Full sa**|**mple**|**SME firm**<br>**sample**<br>**(****_SME_=1)**|**Large firm**<br>**sample**<br>**(****_SME_=0)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Ethnic Fractionalization*SME Y:1 N:0||27.711**<br>(11.427)||||0.017<br>(0.011)|||
|Ethnic Fractionalization|43.980***|20.074|48.718***|26.611**|0.024***|0.010|0.026***|0.012|
||(16.481)|(12.507)|(18.450)|(11.738)|(0.008)|(0.013)|(0.009)|(0.008)|
|Log of Number of workers (lagged)|-1.571*||||-0.001||||
||(0.846)||||(0.002)||||
|SME Y:1 N:0||-4.395||||-0.003|||
|||(4.838)||||(0.004)|||
|GDP per capita (logs)|-7.513|-7.467|-7.808|-4.254|-0.002|-0.002|-0.002|0.000|
||(5.683)|(5.659)|(6.013)|(4.396)|(0.003)|(0.003)|(0.003)|(0.004)|
|Age of firm (logs)|-0.017|0.016|-0.043|0.624|0.001|0.001|0.002|-0.001|
||(1.721)|(1.362)|(1.670)|(1.329)|(0.002)|(0.002)|(0.002)|(0.001)|
|Manager experience (years, logs)|1.563|1.629|2.115|-2.380|-0.004|-0.004|-0.004|-0.002|
||(1.847)|(1.815)|(2.161)|(2.792)|(0.003)|(0.003)|(0.003)|(0.002)|
|Exports (proportion of sales)|-7.537|-7.626|-10.549|1.905|0.002|0.002|0.001|-0.001|
||(6.753)|(6.043)|(8.222)|(2.628)|(0.004)|(0.003)|(0.004)|(0.004)|
|Foreign Ownership Y:1 N:0|14.938|15.341|21.310|0.044|0.008|0.008|0.008|0.003|
||(9.117)|(9.539)|(12.972)|(3.034)|(0.005)|(0.006)|(0.006)|(0.005)|
|Firm is Financially Constrained Y:1|-1.244|-1.393|-1.385|-1.043|-0.007*|-0.007*|-0.007*|-0.009**|
|N:0|(2.402)|(2.391)|(2.512)|(3.958)|(0.004)|(0.004)|(0.004)|(0.004)|
|Female Top Manager Y:1 N:0|-5.676*|-5.670*|-6.188*|1.192|-0.003|-0.003|-0.004|-0.006*|
||(3.022)|(3.097)|(3.158)|(4.668)|(0.003)|(0.003)|(0.003)|(0.003)|

58
- 34 **-**

Table 16. Changes In Participation Rates, Annual Hours of Work, Earnings and Consumption as a Result of Additional Schooling and Subsequent Increase In Wage Rates. (percentage changes from base case)

|Percentage increase||Secto<br>partic|r spe<br>ipatio|cific<br>n|Secto<br>citic<br>nual<br>of wo<br>condi<br>nan*l|r spe-<br>an-<br>hours<br>rk (un<br>tio-<br>|Wag<br>earni<br>-(unco<br>tional<br>(intis|e<br>ngs<br>ndi-<br>)<br>)|Wage<br>earnin<br>(intis)|<br>gs<br>|Consum<br>tion<br>(intis)|p-|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Wag|e work|<br>S<br>emDl|elf-<br>ovment|Wag<br>|e work|<br>**Se**<br>emplo|**lf-**<br>vment|||||
||F|M|F|M|F|M|F|M|F|M|F|M|
|Base case**.....**<br>**.......**|0.32|0.62|0.34|0.35|414|1,165|414|492|2,300|8,100|17,900|27,800|
|Nine years of schooling as<br>lower limit for females ...<br>.|21.5|-1.8|1.2|**_-0.6_**|25.6|-2.7|-1.5|0|42.6|-2.0|8.4|6.5|
|Nine years of schooling as<br>lower limit for males .....|-3.5|5.6|0|-1.7|-4.4|6.7|0|-3.5|-5.0|14.8|11.2|7.6|
|Nine years of schooling as<br>lower limit for both males<br>and females ...........|19.0|3.7|0.9|-1.4|20.5|3.0|-1.2|-2.4|33.9|11.1|17.3|11.2|

Table 17. Effect of Education on Mean Level and Inequality in the Distribution of Per Capita Consumption among Households with a Subsequent Increase in Wage Rates. (percentage changes from base case)

||Mean level|A-coefficient|Gini-coefficienit|
|---|---|---|---|
|Base case.|7600|0.566|0.438|
|Nine years of schooling as lower<br>limit for females .5.3||0|0|
|Nine years of schooling as lower||||
|<br>limit for males .6.6||-1.8|-1.8|
|Nine years of schooling as lower||||
|<br>limit for both males and females|10.5|-3.0|-3.2|
captures demand side factors such as, urban growth, and the share of agriculture and industry in GDP. All these indicators are published by the World Bank under the World Development Indicators.

The term in equation (5a) is the estimated weighting matrix. One of the advantages _FGLS_ is that it provides consistent estimates even if the weights used in the estimation are biased. In case ωω� of even an unknown form of heteroskedasticity, _FGLS_ can be applied. Another advantage of _FGLS_ model over even _GLS_ is that it addresses of _multicollinearity_ issues in a model since it uses the estimated covariance matrix, instead of . ωω� ωω

# **3.3 Main differences in housing investment - developing economies and the OECD**

The evidence clearly shows an inverse relationship between the current account balance (deficits) and housing investment in OECD countries. Table 4 presents our empirical findings. They suggest that OECD countries have made use of foreign investments in expanding housing investment. The use of foreign investment to step up housing helped these countries strengthen their financial portfolios. However for developing countries (columns 1 and 2), domestic savings are the mainstay of housing investment – these are depleted as housing investment is stepped up. Neither the current account balance, nor investments in bond and equity have significant association with housing investment in developing economies. SSA follows the same pattern found in average for developing countries with domestic savings being depleted as housing investment is stepped up (Table 5).

**Table 4: Results for low, middle and high income countries**

|VARIABLES|(1)<br>Low and lower middle income|(2)<br>Upper middle income|(3)<br>High income<br>(OECD)|
|---|---|---|---|
|Domestic savings(% of GDP)|-0.0118***|-0.0300***|0.0297**|
||(0.00339)|(0.00598)|(0.0149)|
|Current Account balance (% of GDP)|0.00313|0.00896*|-0.0621***|
||(0.00323)|(0.00515)|(0.0133)|
|Investment in bond and equity (% GDP)|0.00380|0.00389|-4.04e-05|
||(0.0149)|(0.00359)|(0.00111)|
|Cyclical component of GDP growth|-0.0157***|-0.0595***|0.0155|

23
Grilli, E.R., R. Heterline and P. Pollak, 1981. "An Rconometric Model of the World Rubber Economy," in World Bank Commodity Models, Volume I, World Bank.

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48
disaggregated by month in the course of a given year we chose to use only annual aggregates; the reason for this decision is the fact that most capital expenditure is concentrated towards the end of the year and as a result comparing periods other than full years would be misleading. This information is available for a large number of possible cuts, in particular disaggregated by type of spending (i.e. current vs. capital expenditures). Crucially, this information is available disaggregated for expenditures that are financed with resources that have different types of restrictions attached to them. A particularly important distinction in the Peruvian context is that between ‘ordinary’ and ‘defined’ resources. If unspent at the end of the year ordinary resources revert back to the central government. In contrast, if defined resources are unspent at the end of the year the balance gets reallocated to the municipality in the following year’s budget. This is relevant because all revenues related to natural resources shared between the different levels of governments by formula are included among the defined resources. This municipal public expenditure data has become available due to the roll out of the Integrated Financial Management System ( _Sistema Integrado de Administración Financiera, SIAF_ ) in all municipalities in Peru and is currently available on-line – a key element in improving - accountability to citizens (http://transparencia <u>economica.mef.gob.pe/amigable/). The Ministry</u> of Economy and Finance kindly provided the data, including for the years in which SIAF’s information was not made available on-line.

Population data are from the 2007 Census. In addition to the total number of inhabitants per municipality we also know the number of so-called ‘populated centers’ within a given municipality and the number of people living in each of those populated centers. This allows us to exploit the information content in the dispersion of population within a given municipality.

The human resources of each municipality are available through the annual National Registry of Municipalities ( _Registro Nacional de Municipalidades, RENAMU_ ), a census of municipalities run by the National Institute of Statistics. This registry allows us to know not only the total number of staff working in a given municipality but also the type of work they perform, whether it is managerial, administrative and technical, or manual and support work.

The living standards of the population in a given municipality are available thanks to the work produced by World Bank staff for operation purposes using the Poverty Maps 9
**Table 14 Effects of IC Variables on Firm Growth – Panel Firms**

||Small|Medium|Large|Age|IC|Const|Obs|RSqr|
|---|---|---|---|---|---|---|---|---|
|External Fin|‐0.149|‐0.233|‐0.19|0.002|0.000|0.196|416|0.14|
||(0.084)*|(0.067)***|(0.079)**|(0.002)|(0.001)|(0.100)*|||
|Collateral|‐0.177|‐0.235|‐0.098|0.002|0.000|0.212|116|0.51|
||(0.067)**|(0.043)***|(0.040)**|(0.002)|(0.000)**|(0.101)*|||
|Sales on Credit|‐0.148|‐0.233|‐0.19|0.002|‐0.001|0.232|414|0.15|
||(0.084)*|(0.067)***|(0.080)**|(0.002)|(0.000)|(0.142)|||
|Overdraft (y/n)|‐0.143|‐0.241|‐0.207|0.002|0.048|0.127|416|0.15|
||(0.081)*|(0.068)***|(0.082)**|(0.002)|(0.039)|(0.110)|||
|Line of credit (y/n)|‐0.143|‐0.241|‐0.207|0.002|0.048|0.127|416|0.15|
||(0.081)*|(0.068)***|(0.082)**|(0.002)|(0.039)|(0.110)|||
|Loss PowOut|‐0.202|‐0.343|‐0.276|0.003|0.004|0.171|308|0.22|
||(0.080)**|(0.071)***|(0.080)***|(0.003)|(0.002)|(0.143)|||
|Infor Pay|‐0.15|‐0.23|‐0.208|0.002|‐0.001|0.205|368|0.15|
||(0.089)|(0.073)***|(0.089)**|(0.002)|(0.003)|(0.145)|||
|Tax Inspect|‐0.145|‐0.23|‐0.2|0.002|0.001|0.175|406|0.14|
||(0.087)|(0.070)***|(0.083)**|(0.002)|(0.002)|(0.079)**|||
|Time Tax|‐0.144|‐0.241|‐0.202|0.003|0.001|0.198|403|0.14|
||(0.088)|(0.077)***|(0.082)**|(0.002)|(0.001)|(0.121)|||
|Days to Export|‐0.111|‐0.295|‐0.188|0.003|‐0.008|0.25|201|0.27|
||(0.049)**|(0.139)*|(0.111)|(0.001)**|(0.002)***|(0.085)**|||
|Days to Import|‐0.065|‐0.091|‐0.027|0.001|‐0.004|1.405|139|0.37|
||(0.095)|(0.133)|(0.097)|(0.002)|(0.002)**|(0.128)***|||

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Control for foreign and government ownership, export status, 2‐digit industry, and region fixed effects. *** p<0.01, ** p<0.05 p<0.1 22
Caliendo, M., and S. Kopeinig. 2008. “Some Practical Guidance for the Implementation of Propensity Score Matching.” _Journal of Economic Surveys_ 22 (1): 31–72.

Cerdan-Infantes, P., and C. Vermeersch. 2007. “More Time Is Better: An Evaluation of the Full-Time School Program in Uruguay.” Working Paper, World Bank, Washington, DC.

Contreras, D., S. Cabrera, and P. C. Sepúlveda. 2010. “The Effects of Lengthening the School Day on Female Labor Supply: Evidence from a Quasi-experiment in Chile.” Serie documentos de trabajo, Departamento de Economía, Facultad de Economía y Negocios, Universidad de Chile, Santiago, 323.

De Aquino, J. M., and A. Kassouf. 2011. “A ampliação da jornada escolar melhora o desempenho acadêmico dos estudantes?” Doctoral thesis in Applied Economics, Escola Superior de Agricultura Luiz de Queiroz, University of São Paulo.

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Fundação Itaú Social and World Bank. 2015. “Relatório de avaliação econômica e estudos qualitativos, o programa Mais Educação-Ministério da Educação.” Fundação Itaú Social, Brasilia, and Washington, DC.

Hincapié, D. 2013. “Do Longer School Days Improve Student Achievement? Evidence from Colombia.” Working paper presented at LACEA meeting.

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28
**Table 2: Dependent variable: Inflow FDI (Fixed-effect)**

||1|II|III|IV|V|VI|
|---|---|---|---|---|---|---|
|CFDI|-0.090|0.308**|1.081***|0.607|1.190***|0.672|
||(0.363)|(0.157)|(0.312)|(0.726)|(0.306)|(0.773)|
|FDIt-1|0.528***|0.153***|0.158**|-0.028|0.150**|-0.003|
||(0.049)|(0.039)|(0.064)|(0.165)|(0.062)|(0.170)|
|GPCGR|-0.036|-0.017|-0.041**|-0.031|-0.045**|-0.034|
||(0.033)|(0.013)|(0.019)|(0.032)|(0.018)|(0.034)|
|GDP||2.017**|5.786***|4.608*|6.176***|4.931*|
|||(0.927)|(1.557)|(2.602)|(1.518)|(2.649)|
|WFDI|0.797***|0.588***|0.533***|0.326|0.701***|0.354|
||(0.299)|(0.129)|(0.158)|(0.262)|(0.173)|(0.314)|
|WAGE|-0.275|0.355|-0.971*|-2.612***|-1.277**|-2.600***|
||(0.458)|(0.274)|(0.560)|(0.963)|(0.553)|(0.978)|
|ILLIT||-0.465**|-0.378*|-3.483***|-0.577**|-3.271***|
|||(0.198)|(0.228)|(1.125)|(0.256)|(1.180)|
|OPEN|-0.009|-0.003|-0.005|-0.020*|-0.004|-0.019|
||(0.008)|(0.003)|(0.006)|(0.012)|(0.006)|(0.013)|
|TARIFF||||0.050||0.039|
|||||(0.072)||(0.078)|
|INCTAX||||-0.017||-0.015|
|||||(0.020)||(0.021)|
|PSR|||0.040**|0.070***|0.036**|0.062**|
||||(0.018)|(0.027)|(0.018)|(0.029)|
|TELE|||||-0.808*|-0.107|
||||||(0.459)|(0.834)|
|CRISIS||-0.082*|-0.156**|0.087|-0.131*|0.063|
|||(0.048)|(0.076)|(0.142)|(0.080)|(0.147)|
|ACCESS||0.418***|0.560***|0.823***|0.611***|0.771**|
|||(0.147)|(0.189)|(0.299)|(0.187)|(0.309)|
|Obs.|157|132|101|62|99|61|
|R-squared|0.707|0.792|0.807|0.802|0.803|0.794|

# Notes:

(1). Standard errors are reported in parentheses. ***, ** and * indicate coefficient significant at 1, 5 and 10%, respectively.

34
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5 Section IV extends the analysis to the special problems of developing countries, incluling financial repression and obstacles to financial reform. We draw several related kinds of policy conclusions. First, even though frictions exist which impede the performance of private financial markets, a decentralized capital market is vastly superior to a system of publicly managing credit flows. To flourish, a private capital market requires an efficient system of contract enforcement and a viable borrowing class. Public policy should be directed specifically toward these objectives, and away from directly tinkering in credit flows. Second, liberalization must be coordinated both with policies that encourage growth and stability of the real sector. Financial reform alone is insufficient to generate recovery, and some aspects of reform may even be counterproductive in the short term due to a initial adverse impact on borrowers' creditworthiness. Enhancing the creditworthiness of borrowers through prudent "real sector" policies is crucial to the success of any liberalization. Finally, to avoid potentially massive efficiency costs, any deregulation of financial markets must be coordinated with the design of the financial safety net. While some (or all) of these policy conclusions may not be new, we think that our way of deriving them may offer a fresh perspective.

Section V presents some formal econometric evidence on the relation between finance and growth, in order to confirm that the evolution of the financial sector is an important d.%vension of the growth process, and to give some feel for the rough magnitudes. :%e use a panel of developing countries, and differ from most existing studies, beginning with Goldsmith (1969), by formally exploiting both the time series and cross-sectional dimensions of the panel. Our results are consistent with the earlier studies. We find that, across developing countries, a one percentage point rise in per capita income is associated with a one and a half percentage point increase in private domestic credit. While differences arise across continents (stronger for Asia, weaker for Africa), the relation is otherwise very robust. Concluding remarks are in Section VI.

To narrow the focus, we ignore specific issues related to sovereign