rafmacalaba/datause-encoder-data huggingface dataset:
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# Table 3 (Brazil)

In Brazil there is no analytic coding in the census. The detailed information on quantities and values by product in the volume Produc&c Fisica of the Censo Industrial: Brasil (IBUE, 1981b) is listed with a sequence of numbers of 1 to 13.678. Some of these numbered items refer to production in Brazil as a whole and others to production by state. The summary volume Censo Industrial (IBGE, 1981a) gives an analytic breakdown of gross value of output, census value added, employment and inputs for 24 branches of industry, of which 23 are manufacturing branches. The numbering system is this summary volume is different from that in Producao Fisica, though the same sequence of branches is used in the two volumes.

# Table 4 (Mexico)

The MN Ican census is issued in two main volumes for Mexico as a whole, the Desgloae (SPP, 1979b) which only gives output quantities and values, and the analytic summary volvue Resumen General (SPP, 1979a) which gives information on employment and inputs as well. Mexico has its own four digit code (Catalogo Mexicano de Actividades Economicas) for 239 industries, but the items within each branch are not numbered. They aere arranged instead in descending order of the gross value of shipments. The degree of product detail in the Mexican census is generally bigger than in the US and much bigger than in Brazil. Sometimes a product with the same name is listed in more than one branch. In rarer cases one may find the same product mentioned twice within a branch.

# Tables 5. 6 and 7

The binary matchings which we made to derive PPPs for gross output are presented in tables 5 (US/Brazil), 6 (US/Mexico) and 7 (Mexico/Brazil). Before matching all quantities of measurement were expressed in metric units, e.g. US short tons were converted to metric tons and US gallons to litres. Tables 8, 9. 10 These tables give the reader a summary picture of the matching procedure. In making the matching for simple industries, i.e. sugar and sugar products, malt and malt beverages, tobacco and tobacco products, tires and inner tubes, cement and bricks we used the maximalist approach (see chapter V), where we matched as many as items as possible. For the other, more complex, industries, we had to be more selective in matching and used the ABM approach (see also chapter V). For this reason we show in tables 8, 9 and 10 the proportion of the value of an industry we were able to match in each of the binary comparisons.

# Table 11

For Brazil and Mexico the detailed information is all for 1975, the basic year of comparison. But for the US there was no census for 1975, so the detailed information is all for 1977. The conversion from 1977 to 1975 quantities and prices is shown in table 11.
# _Main findings_ :

The sample for estimating the location choice model includes all firms in the Uganda Business Registry that have more than five employees and that were less than five years old at the beginning of the survey. We limit the sample to relatively new entrants to address the concern that older firms may have made location decisions facing considerably different location attribute choices. There are 56 districts (using 2002 definitions) that firms can chose among in Uganda. In general, the model performs very well in predicting where establishments will be located. Based on the model parameters, our success in predicting actual location decisions is 98 percent. Table 1 provides the raw estimates and standard errors from the conditional logit model. Column 1 reports estimates for all manufacturing firms. Columns 2-8 provide sector specific estimates. These sectors are: Food and beverages, textiles and apparel, paper and printing, chemicals and petroleum, rubber and plastics, metal products and furniture.

<u>Infrastructure: We find that access to the power grid has a positive effect on a</u> district’s attractiveness for location of manufacturing activity.<sup>6</sup> These results are significant in estimations for all manufacturing. While the estimates are positive for each of the industry sectors, it is statistically significant for food and beverages, garments and textiles, and furniture industries. Remember that our measure of power supply is a crude one – we only have information on whether or not the power grid runs through the district. It would be useful to collect information on power breakdowns and prices for future analysis.

Market access, measured by transport connectivity to cities of 100,000 or more people is an important factor in determining industry location. Remoteness from market centers lower industrial prospects. Estimates for pooled estimates of all manufacturing industries produce statistically significant effects. For specific industries, establishments in food and beverages, and chemicals and petroleum products value market access (after controlling for the other variables). Estimates for other sectors are not statistically significant.

> 6 Similar findings are also obtained in analysis of location decisions of Indian manufacturing (Lall and Mengistae 2005, Mani et. al 1997).

15 -
- _50_ -

to the widely held view regarding the importance of this effect in SSA.' Finally the negative sign of the coefficient of the deviation term in equation (5.1) shows that growth in agriculture tend to revert to the average where previous period higher than average growth rates get corrected for in the following period.

'Given the very few observations available for the estimation of this effect and others, further and more robust application- of the model is needed, perhaps in the context of a broadly based panel data from SSA.
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**Table 5.3.1 : Additional Canadian Data Included in CVD-CAN-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In CVD-**|**CAN-Master :**||
|**U.**|**CASE_CBSA**|Case code associated with the subsidy investigation administered<br>by the Canadian Border Services Agency (CBSA)|
|**V.**|**CASE_CITT**|Case code associated with the injury investigation administered by|
|||the Canadian International Trade Tribunal (CITT). Only assigned<br>in the final stage.|
|**W.**|**INIT_DATE_CBSA**|Date of initiation of the case at the CBSA|
|**X.**|**INIT_DATE_CITT**|Date of initiation of the case at the CITT|
|**Y.**|**P_AVG_DUTY**|The weighted average preliminary duty|
|**Z.**|**F_AVG_DUTY**|The weighted average final duty|
|**AA.**|**TERM_DATE**|The date a case was terminated for reasons such as the case was<br>withdrawn by the petitioner or there was insignificant evidence of<br>dumping|
|**AB.**|**NOTES**|Comments|
|**In CVD-**|**CAN-Products :**||
|**D.**|**NOTES**|Comments|

In terms of the data, the duties were usually reported in per unit terms until CAN-CVD-24, when duties were imposed as a percentage of the export price, for the most part. Second, preliminary case information for the years covered by hard-copy cases were not available at the time of data collection, so a number of duty values are missing in that time period.

# **5.4 Mexico (MEX)**

As with Mexican dumping investigations, subsidy cases are investigated by the Unidad de Prácticas Comerciales Internacionales (UPCI, International Trade Practices Unit), part of the Secretaría de Economía. Countervailing duty investigations are far less common than antidumping actions. The first cases described in the database date from 1990, while dumping cases began in 1987. Regardless, countervailing duty investigations follow the same track as that used for dumping, with the UPCI ruling on both injury and the existence of subsidies simultaneously. The data here was gathered from electronic copies of the existing case files.

42
Table 6: **Heterogeneity: Economic conditions in destination countries**

|||Share <br>who<br>|of migrants in<br> are long-sta<br>|FMP<br>yers<br>|
|---|---|---|---|---|
|||(1)|(2)|(3)|
|Tone of the news at destination||-0.0300*<br>(0.0185)|||
|Tone of the news at destination (during mont|hs when)||||
|Employment rate at de|stination||||
||High||-0.0179<br>(0.0176)||
||Low||-0.0292*<br>(0.0175)||
|GDP per capita at de|stination||||
||High|||-0.0146<br>(0.0170)|
||Low|||-0.0268*<br>(0.0164)|
|Employment rate in destination countries||-0.0310|-0.0296|-0.0282|
|||(0.0663)|(0.0667)|(0.0668)|
|GDP per-capita in destination countries||-0.2813***<br>(0.0981)|-0.2823***<br>(0.0976)|-0.2825***<br>(0.0979)|
|Other controls||Yes|Yes|Yes|
|FMP FEs||Yes|Yes|Yes|
|Year-month FEs||Yes|Yes|Yes|
|Country of destination FEs||Yes|Yes|Yes|
|Country of origin FEs||Yes|Yes|Yes|
|Number of Observations||1,568|1,568|1,568|

_Notes:_ OLS regression results. Standardized OLS estimated coefficients are reported. Standardization of coefficients is obtained using the formula _sdsd_ <u>((</u> _xy_ ))<sup>_βx_,where</sup><sup>_βx_isthepointestimateassociatedtocontrolvariable</sup><sup>_x_,</sup> while _sd_ ( _x_ ) and _sd_ ( _y_ ) indicate the standard deviation or respectively control variable _x_ and dependent variable _y_ . Standard errors are clustered at the FMP level in brackets. The dependent variable _Share of migrants in the FMP who are long-stayers_ is the share of migrants in the FMP _i_ in month _m_ in year _t_ who report being in that location for more than 6 months. The variable _Tone of the news at destination_ ( _Tone News Destinationsi,m−_ 1 _,t_ ) is the average tone of migration-related news articles published in the top destination countries for migrants in FMP _i_ in month _m −_ 1 in year _t_ .“Other controls” include: _Intensity of night lights within 5 km_ ; _Number of conflicts within 5 km_ ; _FMP with migrants planning to remain in Libya (1 = Yes)_ ; _FMP with migrants planning to return home (1 = Yes)_ . All other variables are defined in Tables A1 and A2 and additional details are provided in Section 4. *, **, *** indicate statistical significance at the 10%, 5%, and 1%.

29
how they react to shocks. Some models are based on the Input-Output (IO) linear assumption (Leontief, 1951), in which the production of one unit in one sector requires a fixed amount of inputs from other sectors, and in which prices do not play any role (Bockarjova et al., 2004; Haimes and Jiang, 2001; Hallegatte, 2014, 2008; Okuyama, 2004; Okuyama et al., 2004; Santos and Haimes, 2004). Other models are based on the Calculable General Equilibrium (CGE) framework, which assumes that changes in relative prices balance supply and demand in each sector (Rose and Liao, 2005; Rose et al., 2007a). A comprehensive review of this literature is beyond the scope of this paper (reviews can be found in, e.g., Cavallo and Noy, 2009; Hallegatte, 2014; Kousky, 2014).

<!-- Start of picture text --> 400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>0 100 200 300<br>Asset losses (US$b)<br>Ouput losses (US$b)<br><!-- End of picture text --> _Figure 4: The link between asset losses and output losses, in Louisiana for disasters of increasing magnitude (but the same pattern as hurricane Katrina). Source: Hallegatte (2008)._ As an illustration, Hallegatte (2014, 2008) use the Adaptive Regional Input-Ouput (ARIO) model calibrated on Louisiana to investigate the link between asset and output losses. The analysis concludes that total losses due to a disaster affecting Louisiana increase nonlinearly with respect to asset losses when the latter exceed $50 billion (see Figure 4). When asset losses are lower than $50bn, aggregated output losses are close to zero (even though the aggregation hides important disparities across sectors). Beyond $50 billion of asset losses, output losses increase nonlinearly. When asset losses exceed $200 billion, for instance, output losses are equal to asset losses.

# **2.3.2 A simple rule to assess total output losses**

In the framework presented here, one dollar of direct loss in productive capital translates into more than the same amount of output losses. The instantaneous decrease in output is instead equal to the amount of asset losses multiplied by the _average_ productivity of capital, which is about three times the marginal productivity of capital ( ), possibly increased by a factor (1+ that represents ripple-effects.

16
**Table 4: Gender Gap in Educational Returns Implied by Table 3**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Panel A: Gender Wage Gaps(W|omen – Men|)|||||
|% No Primary|-0.808***|-0.328***|-0.255***|-0.222***|-0.267***|-0.250***|
|% Primary|-0.529***|-0.308***|-0.204***|-0.185***|-0.205***|-0.184***|
|% Junior|-0.250***|-0.191***|-0.088***|-0.121***|-0.090***|-0.123***|
|% High+|-0.439***|-0.292***|-0.163***|-0.193***|-0.170***|-0.211***|
|Log Min.Wage*% No Primary|||||-0.213|-0.372**|
|Log Min.Wage*% Primary|||||-0.064|0.088|
|Log Min.Wage*% Junior|||||0.316***|0.295**|
|Log Min.Wage*% High+|||||0.058|0.254**|
|Panel B: Returns to Education By|Gender||||||
|Men|||||||
|Primary|0.202***|0.023|0.110***|0.003|0.010***|-0.004|
|Junior|0.466***|0.112***|0.211***|0.057|0.202***|0.053|
|High+|0.878***|0.343***|0.239***|0.205***|0.230***|0.202***|
|Women|||||||
|Primary|0.481***|0.043|0.161***|0.040|0.161***|0.063*|
|Junior|1.025***|0.249***|0.378***|0.158***|0.380***|0.180***|
|High+|1.247***|0.379***|0.331***|0.233***|0.326***|0.241***|

_Note_ **:** *** p<0.01, ** p<0.05, * p<0.1. Reported wage gap in Panel A is calculated as effect on women minus the effect on men, where a negative sign implies that women are paid less. Returns to education in Panel B is calculated by gender relative to omitted category of workers with no primary education. Minimum wage, educational variables and share of female workers are demeaned.

34
Charnes, Cooper, and Rhodes (1978) were the first researchers to present DEA. Roll and Hayuth (1993) were the first to explicitly advocate its use to estimate port efficiency. By presenting a hypothetical application of DEA to a fictional set of container terminal data, they revealed the potential of the approach. Since then various researchers (including Cullinane and Wang 2010 and Serebrisky and others 2016) have used DEA to estimate port or terminal efficiency.

An output-oriented efficiency measurement problem can be written as a series of _K_ linear programming problems (one for each port) with the constraints differentiating between DEA constant returns to scale (DEA-CCR) and DEA variable returns to scale (DEA-BCC) models. The objective is to maximize the proportional increase in output while remaining within the production possibility set ( _U_ ): subject to where _M_ inputs **xh** = ( _x1h, x2 h, ..., xM h_ ) ∈ R<sup>M</sup> + are used to produce _N_ outputs **y** h = ( _y1h, y2h, ... , yNh_ ) ∈ R<sup>N</sup> +. The row vectors **xh** and **yh** form the _hth_ rows of the data matrices X and Y, respectively. The term **z** = ( _z1, z2,..., zH_ ) ∈ R<sup>H</sup> + a nonnegative vector, which forms the linear combinations of the _H_ ports or terminals; **e** = ( _1, 1, ...,1_ ) is a dimensioned vector of unity values.

A key factor in the efficiency estimations is the choice of inputs and outputs.<sup>3</sup> The choice of port facilities’ follows the most commonly used approach in container port analysis. Capital is represented by the total port area and the length of all container and multipurpose berths in the port. Information on labor inputs is derived from a predetermined relationship to cranes. Following the approach used in the port literature (Notteboom, Coeck, and Van Den Broeck 2000; Cullinane, Ji, and Wang 2005; Cullinane and Wang 2010), the analysis includes the number of

> models make no such assumption; within this category, data envelop analysis is the most commonly applied methodology. Suárez-Alemán and others (2016) provide a detailed review of studies employing both approaches.

> 3 The inputs usually selected for these analyses are physical facilities, such as the number or size of berths, gantry cranes and equipment, terminal yardage, and the labor force (Cullinane, Ji, and Wang 2005).

8
using data from 1990 to 2014. Their research identified energy consumption, openness to trade, GDP, the exchange rate and investment as the most important determinants of the current account.

**This paper contributes to the literature on the determination of the current account deficit in Turkey in several ways.** First, by covering a period under analysis of 1980 to 2017, this is the longest quantitative, multi-variate time series study on Turkey’s current account balance to our knowledge. Secondly, we use and extend a methodology developed by Cusolito and Nedeljkovic (2013) that was first piloted in Turkey in 2013 and since then has been used in an increasingly large number of countries. This methodology uses a novel model-averaging technique to overcome small-sample issues and aims to overcome the most serious issues of endogeneity by careful selection of variables and instrumentation approaches.

# **Turkey’s current account balance, external balance, and their components**

# **_An expanded current account deficit after 2001_**

**From the mid-1980s to 2002, Turkey’s current account was on average in balance but has since moved into a deficit.** From 1985 to 2001, Turkey’s average CAD was 0.5 percent of GDP. However, net gold imports, which are an important form of saving for Turkish households and a reserve item held by the Central Bank, also averaged 0.5 percent of GDP over the period, and netting off gold imports meant that there was an average CAD of 0 percent over those 17 years. Turkey last recorded an annual current account surplus in 2001 (Figure 1), and since then it has incurred a CAD of 4.5 percent of GDP on average per year. The CAB is relatively volatile, with both business cycle trends and year-to-year volatility. The current account is quite variable from year to year, with a standard error of 2 percent of GDP over the period. In the post-2000 period, there are clearly discernable peaks and troughs which coincide with business cycles and the CAB and GDP trends appear closely related (Figure 2).

Figure 1: Current account balance (% of GDP), including and excluding non-monetary gold trade <!-- Start of picture text --> 4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>-8.0%<br>-10.0%<br>Net non-monetary gold exports CAB Mean, split sample<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text --> Source: IMF World Economic Outlook; Central Bank of Turkey; author’s estimates Figure 2: Current account deficit (percent of GDP) and GDP growth 5
App. Table 3b: Complete regression results for Panel B of Table 6

||Overall<br>growth<br>1989→2010|Overall<br>growth<br>2000→2010|1-Person firm<br>growth<br>1989→2010|<br>1-Person firm<br>growth<br>2000→2010|Average 1-<br>person firm<br>employment<br>share 1989-2010|Tradables 1-<br>person firm<br>growth<br>1989→2010|Tradables 1-<br>person firm<br>growth<br>2000→2010|Tradables 1-<br>person firm<br>employment share<br>1989-2010|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log population|0.087|0.090|0.095|0.194|0.068|0.071|0.016|0.010|
||[0.051]*|[0.084]|[0.088]|[0.108]*|[0.068]|[0.065]|[0.101]|[0.081]|
|Log population density|0.022|-0.009|-0.102|-0.040|0.008|-0.046|-0.009|-0.002|
||[0.050]|[0.040]|[0.063]|[0.058]|[0.039]|[0.047]|[0.054]|[0.051]|
|Share of population that is urban|0.130|0.070|0.178|0.172|-0.094|0.088|0.151|-0.092|
||[0.044]***|[0.046]|[0.076]**|[0.085]**|[0.048]*|[0.057]|[0.079]*|[0.060]|
|Share of population that is middle aged (15-49)|-0.118|-0.061|0.031|-0.014|-0.120|0.051|-0.112|-0.219|
||[0.070]*|[0.106]|[0.052]|[0.079]|[0.069]*|[0.042]|[0.114]|[0.081]***|
|Share of population in scheduled caste/tribe|-0.077|0.022|-0.129|-0.010|0.011|-0.068|-0.099|-0.049|
||[0.040]*|[0.036]|[0.073]*|[0.072]|[0.042]|[0.054]|[0.089]|[0.055]|
|Female labor force participation rate|0.112|0.155|0.105|0.151|0.078|0.032|0.108|0.086|
||[0.082]|[0.069]**|[0.083]|[0.077]*|[0.057]|[0.044]|[0.099]|[0.067]|
|Educated worker share (middle and up)|0.273|0.238|0.111|0.212|-0.228|0.043|0.174|-0.432|
||[0.094]***|[0.151]|[0.058]*|[0.123]*|[0.082]***|[0.050]|[0.108]|[0.118]***|
|Literacy rate|-0.090|-0.214|-0.033|-0.215|0.249|-0.005|-0.194|0.470|
||[0.076]|[0.134]|[0.064]|[0.147]|[0.104]**|[0.062]|[0.140]|[0.157]***|
|Infrastructure: share of villages with electricity|0.028|0.124|-0.093|0.186|-0.025|-0.045|0.151|-0.099|
||[0.104]|[0.082]|[0.106]|[0.083]**|[0.078]|[0.057]|[0.077]*|[0.098]|
|Infrastructure: share of villages with paved roads|-0.016|0.007|0.061|-0.085|-0.076|0.053|-0.060|0.084|
||[0.061]|[0.051]|[0.071]|[0.076]|[0.067]|[0.045]|[0.064]|[0.086]|
|Labor laws strength|-0.037|-0.082|0.049|-0.105|0.000|0.022|-0.049|-0.040|
||[0.026]|[0.036]**|[0.046]|[0.069]|[0.057]|[0.041]|[0.061]|[0.055]|
|Travel time to nearest of India's ten largest cities|-0.026|-0.056|0.079|-0.095|0.195|0.069|-0.078|0.340|
||[0.048]|[0.076]|[0.042]*|[0.092]|[0.050]***|[0.038]*|[0.083]|[0.066]***|
|Observations|198|198|198|198|198|198|198|198|
|Adjusted R-squared|0.160|0.078|0.157|0.083|0.292|0.107|0.050|0.286|

Notes: See Table 6.