rafmacalaba/datause-encoder-data huggingface dataset:
**Chart 14. ROE indicators of banking sector in international comparison**

<!-- Start of picture text --> per cent per cent<br>20 20<br>CZ<br>15 Without bank levy 15<br>PL SK BE<br>10 RO DE BG HU 10<br>5 HU SI IT AT 5<br>0 0<br>-5 -5<br>-10 -10<br>EE<br>-15 -15<br>LT<br>-20 -20<br>-25 -25<br>-30 -30<br>-35 -35<br>LV<br>-40 -40<br><!-- End of picture text --> Source: MNB.

# **_III. 1. Corporate lending: Future challenges_**

In the initial period of the crisis, banks responded to mounting liquidity problems primarily by cutting back corporate lending. This was justified by several factors. As the average residual maturity of the corporate loan portfolio is significantly shorter than that of loans to households, in the short run it allows more robust adjustments on the part of banks. Since competition is much stronger in the corporate segment in the Hungarian banking system, margins are tighter and banks‘ profits are smaller on these loans. Finally, corporate loans usually have higher capital requirements than the mortgage loans constituting the bulk of the household portfolio.

From the last quarter of 2008, the corporate segment recorded a negative credit flow in each subsequent quarter. Although the economy started to recover as early as end-2009, this trend of negative credit flows continued throughout 2010. Contrary to developments observed in the region, economic growth has so far not been followed by a recovery in lending. Banks‘ loan supplies remain restrained for two reasons. On the one hand, banks‘ risk appetite remains low; on the other hand, reflecting the deterioration in the portfolio and the very high bank levy, capital buffers decreased, curbing lending ability. According to the forecast of the MNB, corporate lending is not expected to pick up until late 2011 (Chart 15). Without a pickup in lending, the recovery of the economy may remain weak and fragile (MNB, 2011).

As seen above, we cannot say that credit expansion in the corporate sector was excessive. Although risks were exacerbated by the currency structure of the loan portfolio, we have not found evidence of a problem similar in magnitude to that encountered in the household segment. Nonetheless, against the backdrop of the drastic downturn in the economy, the quality of the corporate loan portfolio deteriorated substantially. This, in turn, was reflected in the mounting losses on the portfolio, which impairs the lending ability of the banking system even further. Thus, the greatest challenge lies neither in changing the structure of the portfolio nor in controlling credit expansion; first and foremost, efforts must be made to revive lending, and the government should be involved in this process (e.g. interest rate subsidies, guarantee etc.). The difficulty is that the room for fiscal maneuver is fairly limited due to Hungary‘s high public debt 19
# **Table 8: Summary of Results, initial capture of rents in services by Armenians**

# **(results are percentage change from initial equilibrium, unless otherwise indicated)**

|**Scenario definition**|Benchmark|**EU FTA**|EU<br>Discriminatory<br>Services|EU Tariffs|Border Costs|EU Standards|EU FTA<br>Steady-state|**CIS FTA**|**EU-CIS FTA**|
|---|---|---|---|---|---|---|---|---|---|
|Reduction of discriminatory barriers on EU services firms|No|**Yes**|Yes|No|No|No|Yes|**No**|**Yes**|
|Reduction of discriminatory barriers on CIS services firms|No|**No**|No|No|No|No|No|**Yes**|**Yes**|
|Reduction of discriminatory barriers on ROW services firms|No|**No**|No|No|No|No|No|**No**|**No**|
|Reduction of regulatory barriers for all services firms|No|**No**|No|No|No|No|No|**No**|**No**|
|Removal of tariffs on EU sourced goods|No|**Yes**|No|Yes|No|No|Yes|**No**|**Yes**|
|Removal of tariffs on ROW sourced goods|No|**No**|No|No|No|No|No|**No**|**No**|
|Reduction in border costs|No|**Yes**|No|No|Yes|No|Yes|**No**|**Yes**|
|Reduction in standards for EU exports|No|**Yes**|No|No|No|Yes|Yes|**No**|**Yes**|
|Steady-state capital stock|No|**No**|No|No|No|No|Yes|**No**|**No**|
|**Aggregate welfare**||||||||||
|Welfare (EV as % of consumption)||**1.4**|0.3|-0.1|1.0|0.1|1.7|**0.0**|**1.4**|
|Welfare (EV as % of GDP)||**1.0**|0.2|-0.1|0.7|0.1|1.2|**0.0**|**1.0**|
|**Government budget**||||||||||
|Tariff revenue (% of GDP)|1.0|**0.6**|1.0|0.5|1.0|1.0|0.6|**1.0**|**0.6**|
|Tariff revenue||**-40.9**|0.3|-44.2|5.0|0.7|-40.5|**0.0**|**-40.9**|
|**Aggregate trade**||||||||||
|Real exchange rate||**0.6**|0.2|0.5|0.2|-0.3|0.9|**0.1**|**0.6**|
|Aggregate exports||**13.9**|-0.1|2.5|9.3|1.8|15.0|**0.2**|**14.1**|
|**Factor Earnings**||||||||||
|Capital||**2.0**|0.4|0.5|0.8|0.2|1.0|**0.1**|**2.0**|
|Labor||**2.0**|0.5|0.4|0.9|0.1|2.9|**0.1**|**2.1**|
|**Factor adjustments**||||||||||
|<br>Capital||**0.4**|0.1|0.2|0.1|0.2||**0.0**|**0.4**|
|Labor||**0.5**|0.2|0.2|0.1|0.1|0.6|**0.1**|**0.5**|
|**Capital stock and investment**|||||||1.8|||
|Source: Authors' estimates.||||||||||

48
# **5.1.1 Dependent variable:** ∆ **_ij_**

We used the cleaned data series for imports and exports in the EFI Database, i.e. the series QUANTITY, to construct _I_<sup>_R_</sup> _ij_<sup>–</sup><sup>_ER_</sup> _ij_<sup>. As described in section 2.2, we expressed it in relative terms:</sup> One reason for dividing by _I_<sup>_R_</sup> _ij_<sup>+</sup><sup>_ER_</sup> _ij_<sup>was to reduce the potential for heteroskedasticity. In the</sup> regression results, ∆ _ij_ is named DELTA.

# **5.1.2 Explanatory variables related to measurement error: IM_ADJUST, EX_ADJUST**

We used information in the EFI Database to construct variables that reflect _potential_ measurement errors in reported imports and exports. Recall that the database includes the original COMTRADE data, the series QORIG, in addition to the cleaned series QUANTITY. Since _I_<sup>_R_</sup> _ij_<sup>and</sup> _E_<sup>_R_</sup> _ij_<sup>denote the cleaned data, denote the original, “dirty” data by</sup> The following variables express, in relative terms, the adjustments made by EFI: As with ∆ _ij_ , the value of these variables ranges from –1 to +1. IM_ADJUST is negative if EFI adjusted the COMTRADE quantity downward, with an extreme value of –1 of EFI reduced it all the way to zero. It is positive if EFI adjusted the COMTRADE quantity upward, with an extreme value of +1 if the QORIG series included no imports by country _i_ from country _j_ but EFI had evidence to the contrary. It is zero if EFI made no correction. EX_ADJUST is interpreted the same way.

We hypothesized that data points with large adjustments were ones where measurement error was more likely to persist even after EFI’s corrections. That is, we interpreted a larger value of IM_ADJUST (or EX_ADJUST) as an indicator of greater uncertainty about the quantity imported (or exported). We have no information on whether the residual error is positive or negative. That is, we have no way of knowing whether EFI’s adjustments were too large or too small. For this reason, we included positive and negative values of IM_ADJUST and EX_ADJUST as separate variables: 13
to credit, rather than subjective measures of financing obstacles, because the former is more comparable across countries and it does not depend on cultural biases that might influence individuals’ perceptions; plus it is more reliable and easier to interpret. Appendix A1 gives a detailed description of the process used to construct our measure of access to finance.

We also include several firm-level variables that may influence the extent of firms’ access to finance, such as firm size, measured as log of the number of employees, a dummy for manufacturing industry (the omitted category is service and other industries), a dummy for exporting firms, a dummy for foreign-owned firms, a dummy for government-owned firms, and the log of firm age in years.

The bank-level data come from Bankscope, a commercial database by Bureau Van Dijk including annual balance sheet and income statement information for banks across the world. Only banks classified as commercial, cooperative, Islamic, savings, and bank holding companies are considered in the analysis. We leave out central banks and investment banks, because they are not directly involved in providing loans to firms.

We use bank-level data to construct the _Lerner index_ , a direct measure of pricing behavior by banks, which captures the markup in prices – i.e., the difference between prices and marginal costs, measured as a ratio of prices. Higher values of the index indicate higher markups or lower levels of competition.<sup>11</sup> We start with annual bank-level data and estimate a translog cost function using all available data for each country. We then calculate the marginal cost equation (by taking the derivative of the translog cost equation) and finally the Lerner index for each bank, which we then average for each country and year. Appendix A2 describes in detail the process we use to calculate the Lerner index.

We also use bank-level data to construct two commonly used measures of concentration: _Concentration 3_ is the share of banking system assets held by the three largest banks and

> 11 There is an extensive literature measuring bank competition using the Lerner index. See Fernandez de Guevara et al. (2005, 2007), Berger, Klapper and Turk-Ariss (2009), Carbó et al. (2009), Turk-Ariss (2009), Anzoategui, Martinez-Pería and Rocha (2010), Beck, de Jonghe, and Schepens (2011), Anzoategui, Martinez-Pería and Melecky (2012), and Delis (2012), among others.

6
16 (SITC 520). Preshipment inspection is required for almost<sup>all Madagascar's</sup> imports of products classified in SITC 5 and<sup>6 (some specialty chemicals are</sup> excluded--see footnote 7) so the data do not suggest that extreme prices<sup>occur</sup> primarily where PSI is absent.

A second possible cause of industry differences in relative<sup>import prices</sup> is collaborative false invoicing by foreign<sup>exporters and Madagascar importers.</sup> This **potential** explanation recognizes that the<sup>incentive to over- or underinvoice</sup> depends on the relative height of the foreign<sup>exchange black market premium and</sup> the tariff rate. If the black market premium is relatively<sup>high this encourages</sup> overinvoicing to facilitate capital flight,<sup>while a relatively high tariff</sup> encourages underinvoicing to minimize import duties.17<sup>Expressed algebraically,</sup> if t1 is the nominal tariff, p is the Dlack market<sup>premium (measured as a</sup> percentage above the official exchange rate),<sup>V, is the true value of imports,</sup> and Vf is the falsified invoice price, then<sup>the importer's net gain (or loss) on</sup> product i (Ni) will equal, or, 1<sup>7</sup> For example, if the tariff is 70 percent and<sup>the value were underinvoiced</sup> by 25 percent, the importer actually pays<sup>a 52.5 percent duty.</sup> This assumes, however, that the importer can obtain foreigr<sup>exchange to finance that part of</sup> the import bill which is underinvoiced.<sup>If exchange ccntrols exist, the extra</sup> foreign exchange must be purchased on .he black<sup>market at a premium over the</sup> official rate. In this situation underinvoicing is profitable<sup>if the tariff</sup> exceeds the black market premium. It follows<sup>that goods with very high tariffs-</sup> -say 100 percenc or more--are the most likely<sup>to be vehicles for tax evasion by</sup> underinvoicing. It should also be noted that the statistical<sup>tests presented</sup> in this paper, which are based on French export<sup>unit values, assume that</sup> exporters and importers collaborate on the false invoicing.<sup>It may be that the</sup> misirvoicing is done solely by importers (if<sup>it occurs) and the French export</sup> data accurately reflect relative prices charged.
# **III. Why is inequality so high in Latin America?**

_III.1 Asset inequality in Latin America_ The existing differences in terms of development and more concretely in inequality between the region and the developed world did not appear overnight. In fact, they are likely to be the result of historical processes that in go back at least to the colonial period. Several authors (see among others Engerman and Sokoloff, 2000 and Acemoglu, Johnson and Robinson, 2001) have argued that in order to understand the high inequality levels observed in Latin America today it is important to understand the institutional framework created by the colonial powers which allowed a small group of elites to protect the large rents they were enjoying and excluded most of the population from access to land, education and political power.

**Figure 9 Composition of the population in the New World**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1570 1650 1825 1935 1570 1650 1825 1935 1570 1650 1825 1935<br>Spanish America Brazil US & Canada<br>White Black Indigenous<br><!-- End of picture text --> Source: Engerman y Sokoloff (1997) Sokoloff and Robinson (2005) synthesize much of this literature. They argue that the combination of high settler mortality rates and the availability of rich factor endowments (mineral riches and indigenous labor in Mexico, Mesoamerica and the Andes; and land adequate for sugar plantations in Brazil, the Caribbean and the South of the US<sup>14</sup> , coupled with the availability of slave labor imports) determined a colonization strategy that led to highly exclusionary institutions in much of Latin America and the Caribbean, where a large fraction of the population (of indigenous or African origin) remained for a long time excluded from access to land, education and political power. In contrast, the lack of such initial endowments and lower settler mortality rates led in the north of North America to the progressive dominance of large settlements of Europeans (see Figure 9) and to the establishment of more inclusive institutions.

> 14 The South of the US evolved similar institutions than most of the Caribbean Bassin, but there was rapid convergence with the North after the South lost the civil war

13
<!-- Start of picture text --> El Salvador<br>"caro"<br>90<br>80 Series Average<br>70 A,N,N<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>50<br>40 Unsystematic Part<br>30<br>20<br>10<br>0<br>10<br>20<br>30<br>40<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 4: Smoothed results for the average of the “caro” keyword in El Salvador. The top pane contains the original series and the smoothed, in-sample forecasted series. The forecasted series is labeled _A, N, N_ indicating additive errors, no trend, and no seasonality according to the Hyndman et al. [2002] taxonomy. The bottom pane contains the unsystematic of “surprise” component of the series.

12
**<u>Table 6 Russia: Contribution of Income Sources to Total Inequality, 1989-2004</u>**

||**1989*****|**1992**|**1996**|**1998**|**2004****|**1998-1989**|**2004-1998**|
|---|---|---|---|---|---|---|---|
|||**Income st**<br>|**ructure: p**<br>|**ercent**||||
|Work income|79|67|48|55|62|-24|+7|
|Wages|74|61|34|49|54|-25|+5|
|“entrepreneurial”*|5|6|14|6|8|+1|+2|
|Income from farm|4|8|15|11|8|+7|-3|
|Old age pension|8|10|18|20|17|+12|-3|
|Social transfers|7|6|2|2|2|-5|+0|
|Other income|2|9|17|13|11|+10|-1|
|**Total income**|100|100|100|100|100|||
||**Inequ**|**ality: Con**|**centration**|**Coeffici**|**ents**|||
|Work income|0.285|0.540|0.679|0.540|0.515|+90%|-5%|
|Wages|0.280|0.531|0.644|0.514|0.454|+84%|-12%|
|“entrepreneurial”*|0.360|0.633|0.764|0.750|0.925|+108%|+23%|
|Income from farm|0.300|0.350|0.440|0.573|0.375|+186%|-35%|
|Old age pension|-0.200|-0.140|0.111|0.025|0.094|+113%|+276%|
|Social transfers|0.086|0.317|0.500|0.450|0.150|+425%|-67%|
|Other income|0.200|0.833|0.512|0.492|0.373|+146%|-24%|
||**Decom**|**position: G**|**ini index,**|**contribu**|**tions**|||
|**Gini for income per**||||||||
|**capita**|**0.22**|**0.47**|**0.51**|**0.45**|**0.41**|**+0.206**|**-0.024**|
|Of which:||||||||
|Work income|0.225|0.362|0.326|0.297|0.319|+0.072|+0.022|
|Wages|0.207|0.324|0.219|0.252|0.245|+0.045|-0.007|
|“entrepreneurial”*|0.018|0.038|0.107|0.045|0.074|+0.027|+0.029|
|Income from farm|0.008|0.028|0.066|0.063|0.030|+0.055|-0.033|
|Old age pension|-0.016|-0.014|0.020|0.005|0.016|+0.021|+0.011|
|Social transfers|0.006|0.019|0.010|0.009|0.003|+0.003|-0.006|
|Other income|0.004|0.075|0.087|0.059|0.041|+0.055|-0.018|

Method: decomposition of the Gini coefficient into components, ranking by total per _capita income Source: 1992-1998 from Commander et al based on RLMS, 2004_ ** - own estimates based on RLMS data. _***1989-Milanovic based in HBS,_ * includes in-kind and cash incomes from non-agricultural self-emplyment, informal work and property income.

First, **labor income** is the main source of livelihood, and distribution of earnings the main determinant of overall inequality. But the shape of the distribution is also determined by concentration. Increasing concentration coefficients of wages drove up the overall Gini coefficient in both countries, contributing around 25 points to inequality in Russia, and 18.5 points in Poland. The difference between these contributions, which is 6.5 Gini points, is almost the entire difference between the Gini indices for Poland (0.34) and Russia (0.41). At the same time, the concentration coefficient for wages in Poland (0.39 in 2002) is surprisingly large and not much lower than that for Russia (0.45 in 2004), despite the own Gini indices for wages, as shown in Figure 4, being significantly lower. This is due to different degrees of polarization of labor incomes in the two countries: in Poland a share of households as large as 47 percent (in 2002) did not receive any wage income compared to 35 21
by competition from informal firms in environments where there exist relatively high regulatory obligations and high levels of government capacity to enforce the law so that formal firms comply with these obligations. However, we found that it is only in economies with low government capacity and high regulations that all firms, regardless of how difficult it is or is not to enter their market, find informal competition a threat (see Table 7 for test of statistical significance of other interaction term coefficients). Countries in this category are usually Bolivia, Guatemala, Honduras, Nicaragua, Paraguay and Peru, depending on the specific regulation included as an independent variable in the model. Conversely, it is also the case that firms in industries characterized by low entry costs identify informality as a competitive threat even in the best business environments—where there is high government capacity and comparatively low levels of regulation. Firms with relatively lower entry costs in Argentina, Chile, Colombia, Mexico, Panama, and Uruguay, depending on which regulation is included in the regression, will feel the sting of regulation more than firms in industries characterized by high entry costs. Our hypothesis underestimated the sensitivity to regulatory enforcement and its relation to informal competition; even where there is a light regulatory touch but competent enforcement, firms that escape this enforcement and compete in easy to enter markets can affect the profits of formal firms that fully comply with the law. We had predicted that this would only be the case where there were high levels of regulation and competent government enforcement.

Finally, by using the estimated parameters of the linear probability model, and taking tax rate regulation as an example of an enforced regulation, we find that in an economy with relatively onerous tax regulations and a government that poorly enforces its tax code, the percent of firms adversely affected by informal competition will be reduced from 38.8 to 37.7 percent when the government increases enforcement to the level found in the high capacity group of countries. This reduction provides some clues as to what reforms policy makers should look to if they want to reduce the deleterious effects of informality.

# **4 Conclusions**

Using firm level data for 6,466 manufacturing firms across 14 countries in Central and South America, we show that firms that cite informal competition as a top business environment obstacle largely resemble those informal firms that they complain about. Smaller firms, that are more credit constrained, that underutilize their production capacity and that serve smaller customers identify practices of competitors in the informal sector as a serious business constraint. Also, firms operating in capital intensive industries, such as chemical, electronics, machinery and metals, are by and large less likely to see informal competition as a threat.
– Table 8: Export quality Average and size heterogeneity in effects of SPS and TBT provisions Panel A: All and essential SPS and TBT <u>provisions</u>

|||Quality of agric|ultural pro|ducts||Quality of a|ny produc|t|
|---|---|---|---|---|---|---|---|---|
|Prov:|All SPS|Essential SPS|All SPS|Essential SPS|All TBT|Essential TBT|All TBT|Essential TBT|
|Prov|0.0758|0.171|||0.372|0.0482|||
||(0.415)|(0.396)|||(0.912)|(0.575)|||
|1<sup>_st_ </sup>tercile _×_ Prov|||0.625|0.965<sup>_∗∗_</sup>|||0.196|0.0621|
||||(0.464)|(0.444)|||(1.031)|(1.100)|
|2<sup>_nd_ </sup>tercile _×_ Prov|||0.135|0.0652|||-0.161|0.101|
||||(0.370)|(0.382)|||(0.896)|(0.441)|
|3<sup>_rd_ </sup>tercile _×_ Prov|||-0.0340|0.119|||-0.697|-4.769<sup>_∗∗_</sup>|
||||(0.469)|(0.485)|||(1.260)|(2.257)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|ij|ij|ij|
|N|173140|173140|173140|173140|1021249|1021249|1021249|1021249|

Panel B: Different types of integration of SPS and TBT <u>provisions</u>

|||Quality of agricultural|products|Quality of a|ny product|
|---|---|---|---|---|---|
||MR SPS|Harmonization SPS|Transparency SPS|Harmonization TBT|Transparency TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.198<sup>_∗_</sup><br>(0.107)|-0.132<br>(0.194)|0.118<sup>_∗_</sup><br>(0.0612)|-0.475<sup>_∗∗∗_</sup><br>(0.117)|0.551<sup>_∗∗∗_</sup><br>(0.0979)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.00148<br>(0.128)|-0.606<sup>_∗_</sup><br>(0.332)|0.0187<br>(0.0599)|-0.201<br>(0.136)|0.400<sup>_∗∗∗_</sup><br>(0.0954)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.0189|0.357|-0.0420|-0.220<sup>_∗∗∗_</sup>|0.331<sup>_∗∗∗_</sup>|
||(0.222)|(0.278)|(0.0998)|(0.0471)|(0.123)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|ij|
|N|173140|173140|173140|1021249|1021249|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

38