rafmacalaba/datause-encoder-data huggingface dataset:
should obtain improved access to the markets of these countries, since the tariffs of these **cantries** will be reduced against Turkish exports. We assume that the improved access for Turkish exports in third markets will equal the improved access that Turkey will offer third country imports. This implies that improved access will increase the price Turkish exporters can obtain on exports to third countries by 4.2%.10 Improved access to these markets results in a gain in Turkish welfare of 0.5%, which is **the** largest gain of all the components." In the scenario labelled "XSB" we simulate elimination of the remaining export incenives program for exports of non-agricultural products destined to the EU only.'<sup>2</sup> Compared with more general reduction of export subsidies with respect to all countries (which appears to be the more likely policy choice), this does not affect Turkish welfare significantly (there is a negligible reduction in welfare). The reason is that with removal of export subsidies only to the EU, exporters have the incentive to switch export markets away from the EU so they continue to receive export subsidies. Thus, the distortion costs of the export subsidy program are not reduced significantly, unless they are **reduced** to all export markets. (More general reduction in export subsidies is discussed below.) In the scenario labelled "TRD" we simulate the impact of reduction in the costs of **trading** between the EU and Turkey. As a result of the customs union, closer relations with the **EU will likely** bring with it a possible reduction in costs of clearing goods at the frontier. This is an effect similar to that of the single market program of the EU. In the case of the EU this effect was **assumed** to reduce the "The difference between the EU's most favored nation CET and Turkey's third country tariff **tking** into account preferences (and weighted by Turkey's trade weights) is 4.2%. This may be an underestimate of the improved access Turkey wil receive in third markets since Turkish **external tariffs under the** common external tariff of the EU are lower on average than the MFN rates of developing countries receiving preferential access to the EU.

"Improved access, by improving the prices received by Turkish exporters, results in both 'rectangles' of gains on all previous sales in these export markets, plus triangles of gains on new sales.

"<sup>2</sup> The rate of export subsidies by sector is taken from Arslan (1995); see the appendix for the precise rates per sector.

13
# **Table 5. Drivers of the trends in the skill content of jobs, developing countries sample (STEP index)**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Non‐routine<br>analytical|Non‐routine<br>interpersonal|Routine<br>cognitive|Routine<br>Manual|Non‐routine<br>manual|
|GDP (log), change|‐0.0214|‐0.0486|‐0.00677|0.0270|0.0186|
||(0.0391)|(0.0368)|(0.0198)|(0.0328)|(0.0304)|
|GDP (lagged, log)|0.00137|0.00234|0.00145|0.00283|0.00225|
||(0.00373)|(0.00351)|(0.00188)|(0.00313)|(0.00290)|
|Industry VA (% of GDP), change|0.0812|0.0855|‐0.0470|0.207**|0.0772|
||(0.125)|(0.118)|(0.0631)|(0.105)|(0.0970)|
|Services VA (% of GDP), change|0.0387|0.0551|‐0.0478|‐0.00485|‐0.00316|
||(0.0940)|(0.0885)|(0.0475)|(0.0788)|(0.0730)|
|Skilled (% of working age population), change|‐0.0409|‐0.0558|0.0130|‐0.0418|‐0.00664|
||(0.0478)|(0.0450)|(0.0241)|(0.0401)|(0.0371)|
|Females (% of employment), change|‐0.181*|‐0.117|0.120**|0.0468|‐0.0792|
||(0.107)|(0.101)|(0.0540)|(0.0896)|(0.0830)|
|Working age population (% of population), change|0.375|0.670*|0.0367|‐0.0711|0.00311|
||(0.390)|(0.367)|(0.197)|(0.327)|(0.303)|
|Older than 65 years (% of population), change|‐1.244|‐0.818|0.538|‐0.777|‐1.146|
||(1.175)|(1.106)|(0.594)|(0.985)|(0.913)|
|Internet users (% of population), change|0.0926|0.106**|‐0.0701**|‐0.0620|0.0509|
||(0.0569)|(0.0535)|(0.0287)|(0.0477)|(0.0442)|
|Imports (% of GDP), change|0.0716|0.0651|‐0.0370|0.0664|0.0307|
||(0.0529)|(0.0498)|(0.0267)|(0.0443)|(0.0411)|
|Exports (% of GDP), change|‐0.150**|‐0.128**|0.0992***|0.0250|‐0.00696|
||(0.0597)|(0.0562)|(0.0302)|(0.0501)|(0.0464)|
|Constant|‐0.458|‐1.481|‐1.653|‐2.368|‐1.880|
||(3.508)|(3.303)|(1.772)|(2.942)|(2.726)|
|Observations|355|355|355|355|355|
|R‐squared|0.0865|0.0852|0.134|0.105|0.0606|

Note: OLS coefficients from a cross-country regression using the skill content of jobs (in first differences) as the dependent variable (multiplied by 100).

These findings could be driven by the fact that the level of development may be correlated with the speed at which the demand for skills and the adoption of new technologies take place. Poorer countries tend to grow faster, and thereby increase their human and physical capital faster than other countries as well. To analyze if this could be affecting the results, **Table 6** controls for different non-parametric trends across income groups. The coefficients associated with internet access and exports are not significantly different to the ones from Table 5. Moreover, the coefficients associated with changes in the sectoral structure of the economy continue to be statistically insignificant.

24
inspection tax, under SIMPLES municipalities only directly get tax revenue from service firms<sup>14</sup> : one component of the SIMPLES tax, called the ISS, is 2% of revenues on the first R$180,000 of revenues, and 2.79% after that. So the municipality would gain approximately R$570 per year from formalizing a service firm. On this basis, it still appears it would be worth the municipalities attempting more inspections to formalize more service firms of the size in our sample, particularly if this inspection effort can be targeted at firms that would not otherwise be inspected.

# **5.6. Comparing Actual Impacts to Expectations of Treatment Impacts**

A standard question with impact evaluations is whether they deliver new knowledge or merely formally confirm the beliefs that policymakers already have (Groh et al, 2012). In order to measure whether the results differ from what was anticipated, in January 2012 (before any results were known) we elicited the expectations of the Descomplicar team as to what they thought the impacts of the different treatments would be. Their team expected that 4 percent of the control group would register for SIMPLES between the baseline and follow-up surveys. We see from Table 7 that this is an overestimate of the SIMPLES registration rate, but given the change in MEI requirements, in line with the combined SIMPLES and MEI registration level.

They then expected the communication only group to double this rate, so that 8 percent would register, that the free cost treatment would lead to 15 percent registering, and that the inspector treatment would lead to 25 percent registering. They did not expect there to be any indirect inspector effect, and so expected only 4 percent of the untreated firms in the inspector blocks would register. The zero or negative impacts of the communication and free cost treatments therefore are a surprise. The overall impact of the inspector treatment is much lower than expected, but is in line with the IV estimates, suggesting the Descomplicar team have a reasonable sense of what to expect when an inspection actually occurs, but may have overestimated the amount of new inspections that would take place. Their expectation of a lack of impact for the indirect inspector treatment was also accurate.

# **5.7. Impacts on Trust and Attitudes toward Government**

De Mel et al. (2012) offer Sri Lankan firms monetary payments to get them to formalize, and find that one outcome of formalization is that firms have more trust in local government. In their case formalization is much cheaper and quicker than firms had believed, and so they note that one possible

> 14 Municipalities also gets part of the taxes that go to federal and state governments back indirectly through transfers from these levels of government back to the municipality, but we ignore this indirect component here which is based on a complicated revenue-sharing procedure that doesn’t just depend on municipal tax takes.

24
10 Food aid<sup>3</sup> is divided into three categories -- emergency aid, program aid, and project aid. Each of these categories is subdivided into cereal and noncereal aid. All figures are in tons of grain or grain equivalent. Since African data involve large measurement errors, the price and output data must oe interpreted with care. The only extended food output and input data available for most of Sub-Saharan Africa is total cereal production. This series is used to estimate random shocks to domestic production and imports. Since several countries received no emergency food aid, or received aid for only a short period, the sample includes those 26 countries that received some emergency food aid for an extended period (see table 1).

# III. INTERNATIONAL ALTRUISM: THE EXTENT AND SPEED OF THE RESPONSE

Table 3 estimates the cereal and noncereal emergency food aid response functions. The number of observations is lower than the product of the number of both countries and years because for certain years there are missing values for emergency aid. Each equation included the contemporaneous and three lagged values of the negative as well as the positive shocks of output. The results were not at all sensitive to variations in the specification used to estimate the output shocks. As suggested earlier, positive shocks to output should have no effect on emergency aid. Indeed, the hypotheses that the coefficients of the positive shocks and the constant are equal to zero were accepted at the 5 percent significance level. For example, the F statistic for the hypothesis that all the YPvariables have zero coefficients is 0.621. Since the tabulated value of 3 The World Food Program supplied aid data for this study.
# **Box 3.2: Benchmark regressions on servicified and non‐servicified firm performance**

To perform a more formal analysis of whether some types of firms are more productive than others in Turkey, the EIS firm‐level data set was used to perform benchmark regression analysis. By applying dummy variables for all but one category that is omitted in the regression equation, an empirical conclusion can be reached on whether the included categories are positively or negatively (non) significant against the omitted variable category. The following equation was used: in which TFP stands for the productivity measure as defined by Ackerberg et al. (2015). CAT denotes the vector of types of firms separated by four categories, namely goods firms, servicified goods firms (i.e. both non‐exporters), goods exporters, and servicified goods exporters. Based on which category is omitted, the other three categories are included in the regression equation (1).

Since we are dealing with firm‐level data, but cannot include firm‐fixed effects as this would wipe out the variable of interest as well as the dependant variable, we include firm‐level control variables in the vector FIRM, which are the size of the firm, whether the firms is a “Kurum” (which is a bigger type of enterprise), and whether the firm is more capital‐intensive. Moreover, we also apply sector (γ) as well as time (δ) fixed effects while standard errors (ε) are clustered by sector‐year.

# **Figure B3.1: Results of benchmark regression analysis (2007‐2014)**

<!-- Start of picture text --> Benchmark against goods firms Benchmark against goods exporters<br>24.6<br>13.5<br>-8.8<br>-17.7 -19.7<br>-34.0<br>Servicified Goods exporter Servicified exporter Servicified exporter Goods Servicified<br>Source: EIS database; author’s calculations<br>50 50<br>25 25<br>0 0<br>Percentage difference Percentage difference<br>-25 -25<br>-50 -50<br><!-- End of picture text --> The results are demonstrated in the two panels of Figure B3.1, while the extended output regresion tables can be found in Table A1a and b in Annex 1. The left‐hand panel shows the results where goods firms (i.e. non‐exporters) are omitted. It shows that servicified domestic firms in Turkey are _18 percent_ less productive than non‐servicified goods firms. In line with the trade literature, exporters are more productive than non‐exporters. The right‐hand panel shows the results when goods exporters (with no services affiliate) are omitted, and therefore these results should be interpreted against this category. The panel shows that servicified exporters are _9 percent_ less productive than non‐servicified exporters. In short, firms with a services affiliate are less productive, whether they are exporters or not.

**Turkish firms with service affiliates have more sluggish TFP performance over time.** Although manufacturing firms in Turkey have seen some improved firm productivity performance, particularly from 2009 to 2014 (after the global financial crisis), the productivity trend for servicified firms is less optimistic

16
# **_Regression Model_**

We use the systemic banking crisis database of Laeven and Valencia (2013) to identify banking crises in individual countries. We construct a binary variable that takes the value of 1 if a country experienced a systemic banking crisis after 2007 according to Laeven and Valencia (2013) and 0 otherwise. We relate this dependent variable to explanatory variables averaged over 2003–07. In addition, for the robustness tests, we use the banking crisis classification of Reinhart (2010) and Reinhart and Rogoff (2011).

More specifically, we estimate the following model to conduct our analysis: where _i_ identifies the country, _y_ is the binary (0/1) crisis measure, and �� is a set of _j_ = 3 institutional supervisory variables (a 1/0 dummy variable CMiMa indicating whether the bank supervision is in/out of the central bank, an index that measures the quality of microprudential supervision (Anginer, DemirgüçKunt, and Zhu 2013),<sup>7</sup> and the 1/0 dummy indicating whether the central bank does/does not publish a financial stability report); and a separate variable indicating how many years the central bank has been publishing an FSR. The vector �� is a set of _p =_ 10 macroeconomic and financial variables used in the literature as established determinants of banking crises (Demirgüç-Kunt and Detraghiache 1998, 2005; Kaminsky and Reinhart 1999; Babecky et al. 2012) and the global financial crisis (Lane and MilesiFerretti 2011; Berkmen et al. 2009; Frankel and Saravelos 2012; Masciandaro, Pansini, and Quintyn 2011; Caprio et al. 2014; Eichengreen and Dincer 2011). And _α, β,_ and _δ_ stand for estimated coefficients;  � is an error term.

> 7 We followed the Anginer, Demirgüç-Kunt, and Zhu (2013) methodology and computed the index by aggregating the answers to 14 selected questions on supervisory powers that were collected in the 2003, 2007, and 2011 surveys conducted by Barth, Caprio, and Levine (2008).

9
# **Table 1: Data Coverage and Summary Statistics of Distance-Default.**

This table shows the summary statistics for the distance-to-default measure for countries for which we have at least one observation. Our original data from both BankScope and Datastream covers 2,294 publicly traded banks across 86 countries. Of these banks, 1,942 of them satisfy our requirement that 1) they have at least 36 weekly non-zero volume and non-missing returns over the previous 12 months, 2) they have non-missing liabilities and market capitalization, in order to compute distance-to-default.

|**Country**|**Number of Banks**<br>**with BANKSCOPE**<br>**and DATASTREAM**<br>**Coverage **|**Number of Banks**<br>**with Distance-to-**<br>**Default Measure**|**Mean**|**Standard**<br>**Deviation**|**p5**|**p50**|**p95**|
|---|---|---|---|---|---|---|---|
|**ARGENTINA**|8|8|4.09|1.81|1.87|3.80|7.81|
|**AUSTRIA**|14|13|9.33|3.22|2.77|10.83|12.00|
|**AUSTRALIA**|20|19|6.47|1.95|3.05|6.56|9.73|
|**BELGIUM**|6|6|6.99|2.98|1.84|6.78|12.00|
|**BAHRAIN**|16|11|5.30|2.26|2.30|4.87|9.98|
|**BERMUDA**|17|13|4.02|2.24|1.46|3.53|8.84|
|**BRAZIL**|30|25|3.88|2.07|1.35|3.39|8.14|
|**CANADA**|20|18|6.60|2.63|2.71|6.28|12.00|
|**SWITZERLAND**|24|24|7.63|3.44|2.56|7.24|12.00|
|**CHILE**|9|8|6.00|2.41|2.40|5.76|11.70|
|**CHINA-PEOPLE'S REP.**|12|12|4.68|1.86|2.21|4.48|8.03|
|**COLOMBIA**|9|6|4.83|1.63|2.59|4.80|7.87|
|**CZECH REPUBLIC**|3|2|5.33|2.62|1.94|4.67|11.68|
|**GERMANY**|31|30|6.90|3.48|1.99|6.54|12.00|
|**DENMARK**|20|18|7.56|2.59|3.33|7.51|12.00|
|**EGYPT**|3|3|3.83|1.40|2.20|3.40|6.03|
|**SPAIN**|12|12|6.85|2.57|3.06|6.62|11.65|
|**FINLAND**|7|7|6.66|2.71|2.34|6.41|11.65|
|**FRANCE**|58|55|8.42|3.41|2.73|8.71|12.00|
|**UNITED KINGDOM**|53|50|5.78|2.75|1.87|5.32|11.30|

24
**Annex Table 4 Real Annual Investment Returns, Selected Public Pension Funds 1977-1990**

||Years|1977|1978|1979|1980|1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Defined-Benefit Schemes||||||||||||||||
|**Africa and Asia**||||||||||||||||
|Egypt||.|.|.|.|-5.4|-9.9|-10.9|-12.1|-7.1|-18.9|-14.7|-11.8|-15.6||
|Jordan||.|.|.|.|.|.|.|-1.1|2.1|5.7|6.3|-5.2|||
|Mauritius||.|.|-31.6|<br>-5.1|||||||||||
|Madagascar||.|.|.|.|.|.|.|.|.|-7.0|-10.4|-7.2|||
|Morocco||.|.|-2.8|<br>-4.0|||||||||||
|Pakistan||.|.|1.4|<br>1.8|||||||||||
|Senegal||.|.|-0.5|<br>4.4|||||||||||
|Tunisia||.|.|.|-1.4|-4.8|.|.|.|-3.3|.|.|.|.|-1.3|
|Philippines (SSS)||.|2.8|-11.0|<br>-3.8|6.9|1.4|<br>6.5|-31.6|3.0|<br>18.4|11.3|<br>5.9|<br>5.0|5.9|
|Turkey (ES)||.|.|.|.|.|.|.|-35.7|-25.1|-15.2|7.2|_-50.1_|||
|Turkey (SS[)||||.|||.|-13.9|-25.1|-29.5|<br>-9.8|-16.5||||
|Korea||.|.|.|.|.|.|.|.|.|.|.|4.8|<br>7.2|7.0|
|**North America, Latin Am**|**erica and t**|**he Caribb**|**ean**|||||||||||||
|Canada (Quebec Plan)||-0.2|-0.7|-0.9|<br>-1.2|-2.9|-0.5|<br>4.8|6.6|7.1|<br>6.9|6.8|7.3|<br>6.6||
|Canada (Canada Plan)||1.2|1.2|1.2|<br>0.7|-1.3|0.5|<br>5.3|7.4|9.9|<br>9.2|6.9|5.8|<br>6.8||
|Costa Rica||.|.|.|-9.7|-34.5|-40.8|<br>-2.0|-5.2|3.8|<br>1.8|2.6|-6.0|<br>6.7|-6.9|
|Ecuador||.|.|.|1.8|-2.1|-3.3|-25.2|-15.8|-15.1|-10.6|||||
|Mexico||.|.|.|-12.0|-17.9|-32.7|||||||||
|Peru||.|.|.|.|-17.2|-12.7|-31.0|-28.4|-46.9|-23.8|-45.8|-93.5|||
|Venezuela||-1.1|-0.6|-5.4|-14.7|-6.9|-0.1|<br>2.5|-2.9|-2.2|0.8|-18.9|-20.3|-71.5||
|Jamaica||.|.|.|-13.8|-2.4|3.8|<br>-2.0|-15.9|-9.8|-2.1|3.7||||
|Trinidadand Tobago||.|.|.|.|.|.|-7.5|-6.4|-4.9|-2.7|-4.5||||
|U.S. OASDITF||0.5|-0.5|-3.9|<br>-4.9|-0.4|5.0|<br>7.5|7.3|7.8|9.2|6.3|5.8|<br>4.7|3.9|
|Provident Funds||||||||||||||||
|**Africa and Asia**||||||||||||||||
|Kenya||.|-2.7|-5.8|<br>-8.8|-6.8|-15.4|<br>-6.5|-2.2|-5.0|<br>4.1|2.8|-0.3|<br>-1.8|-3.8|
|Zambia||.|.|.|-7.2|-1.4|-8.1|-14.0|-14.5|-32.0|-46.3|-37.5|-50.0|||
|Fiji||2.3|-1.7|-10.3|<br>-2.8|1.8||||||||||
|India||-0.2|5.8|2.0|<br>-3.0|-4.6|0.9|<br>-2.7|1.7|<br>4.6|2.3|2.3|2.4|<br>5.8|3.1|
|Malaysia||2.1|2.1|3.6|<br>1.2|-1.6|2.2|<br>4.8|4.9|8.1|<br>8.0|7.7|5.4|<br>5.3|4.9|
|Singapore||1.4|3.5|-0.8|<br>0.1|-4.7|2.7|<br>5.2|3.9|6.5|7.9|5.4|1.8|<br>0.9|0.3|
|Sri Lanka||6.2|-9.7|-8.0|-15.7|-8.0||||||||||

Sources: See data notes.

Some of the new data obtained since the publication of the report should, however, be taken under consideration in the debate over public vs private management of pension reserves. The Canadian data, for example, show a low, but positive real rate of return over the period from 1971-1989. Interestingly, the Quebec Plan which is subject to fewer investment constraints and even has foreign equity holdings, 20
institutional barrier index for Singapore is negative and significant at the five percent level for the 1984-2003 aggregate and manufacturing samples suggesting that government policy to promote stability and actions against bribery and corruption over time makes the country attractive for investment. However, the direct impacts of investment costs as well as institutional and infrastructure barriers in outbound aggregate investment and outbound manufacturing investment achieve the expected signs and are significantly different from zero. Thus, Singapore’s aggregate investment abroad as well as in manufacturing avoids higher investment costs as well as higher institutional and infrastructure barriers. Meanwhile for aggregate and manufacturing outbound investment, trade costs of the host countries are insignificant while the trade cost of the parent is negative and significant at the one percent level, for the former only. Finally, distance does not have much of an impact on inbound stocks. It however significantly reduces aggregate outbound and manufacturing investment.

Our main interest lies in the impact of relative skill endowments. The direct coefficients on relative skill differences, specified as the parent country’s share of skilled occupations in the labor force minus the corresponding figure for the host country, are negative and significant at the five percent level for the 1984-2003 aggregate inbound samples. They are also negative and significant at the five percent level for the aggregate 1994-2003 inbound sample and insignificant for the manufacturing inbound samples. This suggests that although inbound manufacturing investment increases with a relative increase in Singapore skills it is not sensitive to changes in skill differences. However, skill differences are significantly positive in all outbound regressions, suggesting that an increase in Singapore’s skills compared to the countries in which it invests tends to increase FEI stocks. Singapore’s outbound investment has largely gone to developing countries and in 22
# Benchmarking Container Port Technical Efficiency in Latin America and the Caribbean: A Stochastic Frontier Analysis<sup>1</sup>

Javier Morales Sarriera, Gonzalo Araya, Tomas Serebrisky, Cecilia Briceño-Garmendía, Jordan Schwartz JEL Classification Codes: L90 Key words: Technical efficiency, stochastic frontiers, Latin America ports, trade cargo Sector Board: Transport (TR)

> 1 The authors would like to thank peer-reviewers Gylfi Palsson, Jean Francois Arvis, and Andreas Kopp for their useful suggestions. Additional valuable comments were provided by Theresa Osborne, Heinrich Bofinger and Adam Stern. This paper builds on an initial work developed in collaboration with Aiga Stockenberga. This work has been supported by a grant from PPIAF.