rafmacalaba/datause-encoder-data huggingface dataset:
protection of creditor and property rights compared to their Macedonian counterparts (see Table 9).

**<u>Table 9: Ranking on 2008 Doing Business indicators</u>**

||SR|MK||SR|MK|
|---|---|---|---|---|---|
|Doing Business|32|75|Getting Credit|7|48|
||||Protecting Investors|98|83|
|Starting a Business|72|21|Paying Taxes|122|99|
|Dealing with Licenses|50|76|Trading Across Borders|90|72|
|Employing Workers|75|128|Enforcing Contracts|50|84|
|RegisteringProperty|5|91|Closing aBusiness|36|127|

In addition, a better skills structure of the Slovak labor force could explain the ability of Slovak firms to expand output faster and cheaper. Even though Macedonia has a much higher unemployment rate (standing at around 34% in 2007, compared to around 13%<sup>18</sup> in Slovakia), an unemployed Macedonian is much less skilled and educated compared to his counterpart in Slovakia. Around 55% of the unemployed in Macedonia were first-time job seekers (considerable number aged 35 and above) compared to only 25% in the Slovak Republic. Above 40% of unemployed in Macedonia have less than secondary education compared to around 28% in the Slovak Republic. This suggests that for Macedonian businesses it is more difficult and expensive to find and hire skills that are in demand compared to Slovak businesses. This is also reflected in the relatively lower score in the Doing Business survey concerning the legal framework governing employment of workers (see Table 9, Employing Workers).

# _(vi) Why could the exchange rate pass-through be lower in Macedonia?_

The estimate that Slovak CPI reflects exchange rate changes more than Slovak CPI can be explained by differences in functioning of product markets as well as trade integration of the two countries.

Functioning competitive product markets imply that changing prices reflect developments in costs. Administered prices can break this link. Most of the prices are set liberally in both economies; however, the small set of administered prices in Macedonia

> 18 Data is for 2006

24
the Moldovan sample are MBOs. Note, however, that the ownership structure in each country is not significantly different between MBOs and voucher-privatized firms. This is especially true in Georgia, where the enterprises belonging to the former group actually have lesser share _(54.3%_ as compared to 59.7%) of management ownership. There is a larger difference in management ownership of privatized firms in the Moldova sample (37.8% vs. 17.6%). If one were to sum up the ownership stakes by managers and employees, as suggested in Blanchard and Aghion (1996), the ownership structure of the two groups of privatized firms in Moldova becomes very similar.

**Table 1: Ownership Structure of the Sample Firms**

The shares are unweighted averages. State ownership includes property under local and municipal administrations. Outside Local Investors include investment funds.

|Country|Number<br>of Firms|Managers|Employees|The State|Outside Lo-<br>cal Investors|Outside For-<br>eign Inves-<br>tors|Individuals|
|---|---|---|---|---|---|---|---|
|Georgia||||||||
|State|8|0.0|0.0|100.0|0.0|0.0|0.0|
|MBOs|9|54.3|2.6|8.6|25.6|0.4|8.5|
|Vouchers|75|59.7|9.1|12.8|16.9|0.6|0.9|
|Moldova||||||||
|State|12|0.0|0.0|100.0|0.0|0.0|0.0|
|MBOs|33|37.8|5.2|19.4|20.7|4.3|12.6|
|Vouchers|104|17.6|21.6|16.9|34.8|1.9|7.2|

Source: Own calculations.

The lack of substantial variations in the stakes held by insiders in MBOs and voucherprivatized companies is helpful in the empirical analysis, as it can be used to argue that the differences in the degree of restructuring between the two groups of firms (were we to find such differences) are primarily due to the method of privatization and not to the concentration of control that resulted from the privatization process. There has been significant evidence in both transition and market economies that shows that the level of ownership concentration may in itself be an important determinant of the speed of productivity growth.

We next compare the unweighted averages of the three restructuring indicators suggested in Section 2 across the types of company ownership in each country (Table 2). We choose to consider unweighted averages since this method removes the bias that may arise from comparing enterprises with different sizes (either measured by the value of total assets or employment).

9
measures of robot adoption in (initial) import partners (model 5), as well as initial developed import partners (model 6), predict robot adoption in the home country, and their inclusion substantially weakens the overall first-stage identification. In addition, constructing an instrument from the adoption of import partners, i.e. suppliers, is less plausibly exogenous than export customers, the instrument we use. The FDI literature shows multinationals often have substantial market power and influence over their suppliers. Therefore, robot adoption in suppliers could potentially be an endogenous decision by the home firm.

For the remainder of the paper, the baseline instrumental variable specification will be a single instrument, weighted average robot stock in export partners reflected in Model 1.

# **_iii. Instrument validity_**

In constructing our instrument, we undertake a number of steps to assess its plausible exogeneity. As discussed in the preceding section, the instrument measures robot diffusion in export partners rather than import partners, with export partners defined in the initial year and we only consider initial export partners in other world subregions (to exclude common regional shocks).<sup>21</sup> We conduct a strict exogeneity test to assess the validity of the instrument, following Wooldridge (2002) and Baier and Bergstrand (2007). We repeat the baseline first-stage estimation, but also include leads (t+1 and t+2) and lags (between t-1 to t-3) of our instrument. If the diffusion of robots among export partners is strictly exogenous, then future values of the instrumental variable (at t+1 and t+2) should not be correlated with our treatment, contemporaneous robot diffusion.

The results in Table A4 cannot reject that the instrument is strictly exogenous. The coefficients for future values of the instrument (at t+1 and at t+2) are both statistically insignificant and close to zero (see models 2, 3 and 7). Moreover, the standard errors are noticeably small signifying the precision of the estimates. In addition, we find that lagged values of the instrument (models 4 to 6) do not typically increase the predictive power compared to the baseline (model 1).

> 21 Sub-regions are defined using UNSTATS classification.

22
Next, pre-tax profits are the ratio of a bank’s pre-tax profits to assets. Banks that are more profitable are expected to have a higher market-to-book ratio. Earning assets, in turn, is the ratio of earning assets to total assets, which proxies for a bank’s business model. Specifically, a bank with a high earning assets variable may derive a large share of its income from traditional lending activities, rather than from fee-generating activities, such as advisory services, and trading on its own account. At a time of depressed values for traditional bank assets such as mortgage loans, this variable could negatively affect the market-to-book ratio, while the impact on the CDS spread may be positive. An additional bank-level variable is leverage, defined as the ratio of total bank liabilities to total assets. The market-to-book ratio may be positively related to leverage due to higher implicit subsidies from the financial safety net or alternatively because of the deductibility of interest from the corporate tax base, but a negative relationship may also exist since higher leverage may increase expected bankruptcy costs. Similarly, the CDS spread may be positively related to leverage, if high leverage implies relatively large expected credit losses on bank liabilities.

A country’s past experience with banking crises may affect the financial support that will be available to banks in any future financial crisis. Therefore, we control for the occurrence and fiscal cost of past banking crises. Specifically, past crisis is a dummy variable that equals one if a country has emerged from a previous banking crisis, and it is zero otherwise. In the table, we see that the mean value of this variable is 0.79, which implies that 79 percent of banks are located in a country that has suffered a banking crisis. In addition, past fiscal cost represents the ratio of the fiscal cost - relative to GDP - of resolving the most recent past banking crisis. This variable is zero, if the country has not emerged from any past banking crisis. The information used to construct these variables is taken from Laeven and Valencia (2008).

The table also provides summary statistics on the government debt and fiscal balance variables. The fiscal balance is the net fiscal balance, computed as revenues minus expenses and depreciation of public capital. The mean government debt and fiscal balance ratios in the sample are 49.4 and -2.1 percent, respectively.

To represent bank risk, we construct the bank stock risk variable as the annualized standard deviation of weekly returns on bank stock holdings, based on returns information from 11
- Fearnside, P.: 2000, Global Warming and Tropical Land-use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation, Climatic Change 46(1), 115158.

- Geroski, P.: 2000, Models of Technology Diusion, Research Policy 29(45), 603625.

- Gillingham, K., Smith, S. and Sands, R.: 2008, Impact of Bioenergy Crops in a Carbon Dioxide Constrained World: an Application of the MiniCAM EnergyAgriculture and Land Use Model, Mitigation and Adaptation Strategies for Global Change 13(7), 675701.

- Golub, A., Hertel, T. and Sohngen, B.: 2009, Land Use Modeling in RecursivelyDynamic GTAP Framework, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 235 278.

- Gurgel, A., Cronin, T., Reilly, J., Paltsev, S., Kicklighter, D. and Melillo, J.: 2011, Food, Fuel, Forests and the Pricing of Ecosystem Services, American Journal of Agricultural Economics 92(2), 342348.

- Gurgel, A., Reilly, J. and Paltsev, S.: 2007, Potential Land Use Implications of a Global Biofuels Industry, Journal of Agricultural & Food Industrial Organization 5(2), 134.

- Hanoch, G.: 1975, Production and Demand Models with Direct or Indirect Implicit Additivity, Econometrica 43(3), 395419.

- Hertel, T., Tyner, W. and Birur, D.: 2010, The Global Impacts of Biofuel Mandates, The Energy Journal 31(1), 75100.

- Hocking, M., Stolton, S. and Dudley, N.: 2000, Evaluating Eectiveness: A Framework for Assessing the Management of Protected Areas, IUCN, Gland, Switzerland, and Cambridge, UK.

- Houghton, R.: 2003, Revised Estimates of the Annual Net Flux of Carbon to the Atmosphere from Changes in Land Use and Land Management 18502000, Tellus B 55(2), 378390.

41
results that are likely to be obtained from varying different elements of the institutional environment. It should not however be taken to imply greater accuracy than is actually possible at the present stage of development of this methodology. Given the imprecision of the measure, IRRs are used to rank order expected payoffs by type of intervention or change in institutional arrangements or practices, rather than to predict exact performance improvements.

# **Stage 3 - Identify anticipated performance pay offs**

As noted, IRRs are intended to draw attention to the varying results that are anticipated from different interventions, but given their probable imprecision it is the general magnitude of the pay off rather than the exact value that is relevant for policy makers.

The following table presents the potential reform interventions to improve institutional performance in the public sector of Bolivia. Only those reform interventions where IRRs were statistically significant for one-tailed test are identified. (For details, see Appendix 6.) Reform interventions with statistically significant IRRs are classified according to whether they are likely to impact on one, two or three areas of performance.

30
58 Table 4

**Istimat*9** of **Private ates, ef Return** to Scheoling in Thailand in **l001** by **Sax.** With and Without Smple Selection **Cereetien**

||Private<br>Internal<br> <br>tt stet4s<br>**Primary**|Rateof**Returns**b<br>tiss beneatb coo<br>Seceodcry|y Sbeeooling LOevl<br>t.foeent)<br>ligher|
|---|---|---|---|
|**Females:**<br>without<br>Correction<br>for samPle selection<br>of**wage earners**(OLS)|<br>3.22<br> <br>(2.70)|34.22<br>(22.1)|1.62<br>(-55)|
|with correction for<br>sample selectionof<br>**wage**earners<br>(two *tage Beckman)|10.02<br>(5.00)|24.73<br>(6.77)|10.6l<br>(M.04|
|males:||||
|Without Correction<br>for sample selection<br>of wage earners (OLS)|<br>15.95)<br> <br>(5.56)|2.)(.6<br>20.3)|**5**23.**2**<br>.4<br>(2.16)|
|With correction for<br>sample selectionof<br>**wage *arneras**<br>(two stage Beckman)|14.3S<br>(8.05)|13.12<br>(9.04)|4.12<br>(1.66)|

&The privte trate of return is approximated by the coefficient estimotod on the years of schooling variable in a logarithmic wage rate regression that also includes **as** regressorc the individual's years of postecbooling experience and *xperience squared, and four regional dumy variables, The sample includes all persons 15 to 49 in the 1901 Socieoaonomic Survey of Thailand. The Beekman (1979) lambda correction terms **are** included from probit equations for labor force porticipation and for **wage** earner status. identified by the inclusion of own nonearned incme and irriasted and sonirrigatod land.

Source: See Appendix **Tables** A-7 end A-S.
**Table 8: Brazil Exporters by State**

|**State**|**Export**|**Percentage**<br>**of Exporting**<br>**Firms (by**<br>**State)**|**Never**<br>**Export**|**Percentage of**<br>**Firms that**<br>**Never Export**<br>**(by State)**|
|---|---|---|---|---|
|**São Paulo**|75|35.7|135|64.3|
|**Rio de Janeiro**|14|19.4|58|80.6|
|**Minas Gerais**|30|22.9|101|77.1|
|**Santa Catarina**|33|31.4|72|68.6|
|**Rio Grande do sul**|54|49.5|55|50.5|
|**Paraná**|25|28.1|64|71.9|
|**Goiás**|7|13.7|44|86.3|
|**Mato Grosso**|2|15.4|11|84.6|
|**Ceará**|10|17.2|48|82.8|
|**Paraíba**|4|16|21|84|
|**Maranhão**|0|0|6|100|
|**Bahia**|4|9.3|39|90.7|
|**Amazonas**|4|22.2|14|77.8|
|**Total**|262|28.2|668|71.8|

# **<u>Comparing China and Brazil</u>**

There are some interesting comparisons between China and Brazil in terms of logistics services performance. Inventory levels are often taken as indicators of logistical sophistication, as more efficient transport and logistics services supporting “just-in-time” production and delivery systems allow firms to streamline their input inventories. Inventory levels may also be related to the capacity and quality of transportation infrastructure, as firms with access to poor infrastructure may need to hold higher inventories to offset unreliable shipments.<sup>7</sup> Figures 1a and 1b show the average level of inventory in days for firms in China and Brazil respectively. They show that inventories are much higher in China. For example, Apparel and Leather Good manufacturers in China maintain inventory levels that are almost twice as high as apparel manufacturers in Brazil. The low level of inventory in Brazil firms could partly be explained by high real interest rate.<sup>8</sup>

> 7 A recent US study showed that inventories are negatively related to highway capital (Shirley and Winston, 2004).

> 8 See World Bank 2002.

11
The Connection Between Wall Street and Main Street: Measurement and Implications for Monetary Policy<sup>_∗_</sup> Alessandro Barattieri<sup>_†_</sup> Maya Eden<sup>_‡_</sup> Dalibor Stevanovic<sup>_§_</sup> October 16, 2013

# **JEL classification** : E44, E52, G20

**Keywords** : Connection, financial sector, real economy, monetary policy transmission mechanism

> _∗_ We would like to thank Steve Ambler, Susanto Basu, Ben Eden, Patrick F`eve, Jeffrey Frieden, Marc Giannoni, Alain Guay, Michel Juillard, Sydney C. Ludvigson, Jean-St ́ephane M ́esonnier, Kevin Moran, Louis Phaneuf, Thomas Philippon, Franck Portier, Federico Ravenna, Luis Serven, and seminar participants at ESG UQAM,<sup>`</sup> Bank of France, EUI, Atelier en macro ́economie CIRPEE-DEEP-TSE<sup> ́</sup> 2013 and 2013 CEA annual conference for useful comments and suggestions. Elisa Martin provided outstanding research assistance. This paper does not necessarily reflect the views of the World Bank, its Executive Directors or the countries they represent.

> _†Corresponding Author_ ESG UQAM and CIRP ́EE. Mail: Case Postale 8888, succursale Centre-ville Montreal (Quebec) H3C 3P8. Tel: +1-514-987-3000 (0850#). Fax: +1-514-987-8494. E-mail: barattieri.alessandro@uqam.ca

> _‡_ The World Bank. Macroeconomics and Growth Research Group. 1818 H st. NW. Washington, DC. Tel: +1-857-246-9722. E-mail: meden@worldbank.org

> _§_ ESG UQAM, CIRP ́EE and CIRANO. Mail: Case Postale 8888, succursale Centre-ville Montreal (Quebec) H3C 3P8. Tel: +1-514-987-3000 (8374#). Fax: +1-514-987-8494. E-mail: dstevanovic.econ@gmail.com

1
**Table 16. Significance of legal and regulatory obstacles: Joint venture requirements**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.115*|0.0992|||0.201*|0.181*|||
||(0.0637)|(0.0631)|||(0.107)|(0.108)|||
|Export % of sales|0.000226|-0.000229|0.000237|-0.000161|0.000421|-0.000439|0.000484|-0.000328|
||(0.000852)|(0.000854)|(0.000851)|(0.000849)|(0.00144)|(0.00148)|(0.00148)|(0.00150)|
|Share of sourcing via imports|0.00278***|0.00167*|0.00282***|0.00165*|0.00480***|0.00312*|0.00517***|0.00332*|
||(0.000932)|(0.000972)|(0.000938)|(0.000974)|(0.00157)|(0.00167)|(0.00164)|(0.00173)|
|High-income source country|-0.0650|-0.0448|-0.0637|-0.0281|-0.0988|-0.0915|-0.0920|-0.0543|
||(0.0940)|(0.0939)|(0.0941)|(0.0925)|(0.158)|(0.162)|(0.165)|(0.167)|
|>250 employees|-0.0511|-0.0296|-0.0609|-0.0453|-0.105|-0.0667|-0.107|-0.0772|
||(0.0803)|(0.0800)|(0.0814)|(0.0816)|(0.136)|(0.140)|(0.141)|(0.147)|
|Cumulative inv. >US$10M|-0.0426|-0.0909|-0.0615|-0.106|-0.0631|-0.158|-0.105|-0.195|
||(0.0764)|(0.0786)|(0.0754)|(0.0771)|(0.129)|(0.136)|(0.130)|(0.137)|
|Years in country|-0.00460*|-0.00428*|-0.00485**|-0.00471*|-0.00688|-0.00660|-0.00761|-0.00761|
||(0.00239)|(0.00245)|(0.00239)|(0.00247)|(0.00445)|(0.00456)|(0.00463)|(0.00480)|
|Level of foreign ownership (%)|-0.00189|-0.00171|-0.00189|-0.00132|-0.00346*|-0.00336|-0.00356|-0.00280|
||(0.00125)|(0.00133)|(0.00124)|(0.00131)|(0.00209)|(0.00226)|(0.00217)|(0.00234)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.018|0.047|0.048|0.077|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Requirements to foreign investors to enter into joint ventures with local company in your sector]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

33
10

# DEBT DYNAMICS: BANK LOANS AND INTER FIRM CREDIT

While in any market economy the banking function is crucial for efficient resource allocation, there are also other checks and balances such as might be exercised by owners and other creditors. In the Polish system of self-governed SOEs with little or no oversight function performed by the founding organs and ministries, the role of banks is even more critical.<sup>This</sup> section contains evidence on what firms and banks actually did, on the evolution of debt service and the pattern of bank intermediation in relation to an important sample of firms. It also discusses the record on enforcement of bankruptcy triggers such as non-payment of dividends. The key findings are: (i) Reluctance of profitable firms to borrow means banks are going to be saddled with the worst enterprises. There is a clear adverse selection problem with loans going mainly to firms in poor shape.

(ii) Enforcement of bankruptcy triggers has been lax and lacks credibility.

The evidence pertaining to the above findings is now presented.

# Bank loans

Fig. 8 captures the big picture based on the sample from the third quarter of 1989 to the first quarter of 1991. It contains three graphs. The first, labeled "DebtBurd", portrays a measure of debt burden (average bank debt divided by sales proceeds minus all non-financial costs, or equivalently, pre-tax profit plus interest). The advantage of focusing on the stock of debt rather than actual interest paid is that this eliminates any bias caused by a reduction in interest due through interest capitalization, which was widespread over the sample period. The second graph, "Debt/CoS", captures dependence on banks for conducting operations (bank debt / cost of sales, or bank debt per unit of operating costs). The third graph, "FG/Sal", is a demand indicator, picking up ability to sell (finished goods/sales Five phases may be distinguished in Fig. 8: (a) debt erosion as a result of the hyperinflation of 1989; (b) cushioning effect Figure 8 <!-- Start of picture text --> Bank Debt Dynamics<br>-00<br>ISO b<br>so<br>A B C D E<br>lir89 IV9S ro iro iirao iWoo rin<br>DebtBurd t DebtICCo8 -*- PGI<br><!-- End of picture text --> of dollar deposits in first quarter 1990, when debt burden stayed within moderate levels despite the big interest rate shock; (c) rising trends in all three indicators over the first three quarters of 1990, with a particularly sharp increase in the debt burden; (d) an improvement in the fourth

**sAverage** debt is **the averagc** of monthly debt, defined as beginning plus end of month debt divided by two.<sup>**Finrs**</sup> reported **the sum** of investment and working capital credits.
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