rafmacalaba/datause-encoder-data huggingface dataset:
where _ln_ ( _σ_ ( _wagekct_ )) and _ln_ ( _agekct_ ) are respectively the log of the standard deviation of the wage distribution and the age of workers with of age _l_ in country _c_ in year _t_ ; _γct_ are country _×_ year fixed effects and _εkct_ is the error term. A positive correlation between wage dispersion and age would imply _α_ positive. Figure 1 plots the conditional correlation between _ln_ ( _σ_ ( _wagekct_ )) and _ln_ ( _agekct_ ). The correlation is positive and statistically significant, with _α_ ˆ equal to 0.7 and very precisely estimated. Similarly, one can look at the dispersion of wages across sectors and regions. For each survey, we compute the variance of wages across these two dimensions. Results are shown in figure 2. The horizontal axis represents the dispersion of wages across sectors and the vertical axis represents the dispersion of wages across regions. Both are computed as the log standard deviation of the sample distribution. Each point in the figure represents a survey. Points to the right of the 45 degree lines indicate surveys for which the dispersion of wages across sectors is greater than the dispersion of wages across regions. This is the case for 80% of the surveys in our data and suggest that sector mobility costs may be larger than mobility costs across regions.

# **4 Results**

## **4.1 Baseline results: With correction**

The estimation of equations (7) and (8) is performed at the country _×_ level, using worker level information, and three mobility costs are estimated each time for each of the 235 country-year surveys separately. This means that we have a total of 705 estimates. To avoid flooding the text with long tables, we present summary statistics of the estimates in table 2. The upper panel of table 2 presents the average, median and standard deviation for each mobility costs based on the estimates where we correct for the self-selection of workers into sectors and regions. The lower panel restricts the summary statistics to the mobility costs which are statistically significant at the 10% level.

Columns (1) reports the estimates for the sector mobility costs. On average, across the 235 country _×_ year surveys we find that an average cost of moving across sectors (while remaining within the same region) is about 1.4 times the average annual real wage. The median mobility cost is slightly smaller, at around 1.3 times the average annual wage. Using only the significant estimates produces a larger average sector mobility cost.

Columns (2) reports the estimates for the region mobility costs. These costs are lower than the sector mobility costs. On average, region mobility costs represent about 86 percent of the average annual wage, while the median mobility cost is about 80 percent of the average annual wage.

9
**Figure 12: State control and barriers to entrepreneurship**

a. Barriers to entrepreneurship b. State control <!-- Start of picture text --> 4.0 4.0 3.5 3.6<br>3.5 3.5 3.2 3.2 3.3<br>3.0 3.0<br>2.5 2.3 2.5 2.1 2.2 2.3 2.4 2.5<br>1.9 2.0<br>2.0 2.0<br>1.5 1.1 1.2 1.3 1.3 1.4 1.4 1.5 1.5 1.4<br>1.0 1.0<br>0.5<br>0.5 0.5<br>0.0 0.0<br>Romania Bulgaria Slovak Republic Albania 2007 EU15 Croatia 2007 Hungary OECD Czech Rep. MICs Poland Slovak Republic OECD Albania 2007 EU15 MICs Czech Rep. Bulgaria Romania Hungary Croatia 2007 Poland<br><!-- End of picture text --> _Source: see Figure 3. Note: Other MICs are Brazil, Mexico, Turkey. Values refer to 2007 for Croatia and Albania, 2006 for Bulgaria and Romania,2004 for Brazil, and 2003 for all other countries. For full data set see Annex I._

**Croatia performs quite well with respect to barriers to entrepreneurship.  Priorities concerning inward policies clearly lie with state control.** The legacy of central planning is still visible in CEE countries, since they perform comparatively worse than all other groups, although Euro zone countries, with a tradition of heavy state involvement in the economy, present not much lower values.<sup>4</sup> As a general trend, most of the OECD countries that had relatively restrictive policies in 1998 have succeeded in reducing the extent of state control in 2003 by removing price controls and reducing reliance on coercive - as opposed to incentive-based - regulations.

# **4.1 Barriers to entrepreneurship**

**Barriers to entrepreneurship affect firm rivalry entry and exit.** They include barriers to competition, regulatory and administrative opacity and administrative burdens on start-ups as well as barriers to closing a business. These are discussed in turn below.

# **_4.1.1 Barriers to competition_**

**License and permit requirements or antitrust exemptions are more pervasive in Croatia than on average across the EU15.** Croatia scores worse than the EU15 average in 2003 in terms of barriers to competition (as measured by license and permit requirements or antitrust exemptions), whereas it outperforms other MICs (Figure 13).  This is largely due to Croatia’s progress in incorporating EU and WTO rules and best practices in national legislation.  This resulted in the elimination of most antitrust exemptions for state-owned enterprises, with the exception of rules providing for exclusion or exemption from liability under the general competition law for provision of public services that is required or authorized on the basis of special legislation.

Croatia’s score is around the EU15 and OECD averages in terms of other legal barriers to competition, such as explicit legal limitations on the number of competitors allowed in certain business sectors. Croatia still maintains some legal restrictions to entry in network and utilities

> 4 See Tables in the Annex.

13
Table 3 provides a summary of these measures and their availability across the countries covered in this paper. Note that while the indicator _registering a property_ was available across all countries, we did not use it in the analysis due to limited variation within some countries.

Table 3: Key business environment variables from Subnational Doing Business

|Concept / Indicator|Description / Motivation|Country<br>|coverage|||
|---|---|---|---|---|---|
|||Italy|Spain|Poland|Romania|
|Starting a business|Procedures, time, cost and paid‐in<br>minimum capital to start a limited<br>liability company; shown to increase<br>firm formalization, entry, and profits<br>(Motta, Oviedo and Santini, 2010;<br>Klapper and Love, 2010; Fritsch and<br>Noseleit,2013).|||||
|Dealing with<br>construction permits|Procedures, time and cost to<br>complete all formalities to build a<br>warehouse and the quality control<br>and safety mechanisms in the<br>construction permitting system;<br>proxy for regulations impacting<br>investment forgrowth.|||||
|Registering a<br>property|Procedures, time and cost to<br>transfer a property and the quality of<br>the land administration system;<br>property owners with registered<br>titles shown to be more likely to<br>invest and have a higher likelihood of<br>getting credit (Galiani and<br>Schargrodsky,2009).|||||
|Enforcing contracts|Time and cost to resolve a<br>commercial dispute and the quality<br>of judicial processes; associated with<br>more rapid growth of small firms<br>(Islam, 2003) higher investment and<br>higher innovation (Esposito, Lanau<br>and Pompe 2014), and higher<br>participation in value chains<br>(Cattaneo et al,2013).|||||

SDB collects data at the city‐level, typically covering the primary and secondary cities across the country – in Italy, the report covers 13 cities across 13 regions; in Spain, 17 autonomous communities and 2 autonomous cities across 19 regions; in Poland, 18 cities in 16 voivoideships (provinces); and in Romania, 9 cities in 8 regions. These cities were then mapped to their corresponding NUTS‐2 level region for the analysis. In two regions in Poland and one region in Romania, more than one city was covered in the Subnational DB.<sup>14</sup> For these regions, we created new indicators based on the simple average between the two reported city results. The list of cities and mapping to regions is provided in Annex 2.

> 14 In Romania, Cluj‐Napoca and Oradea in North West region; in Poland Bydgoszcz and Torun in Kujawsko‐Pomorskie, and Gorzów Wielkopolski and Zielona Góra in Lubuskie.

8
In the late 1990s, Vietnam still had 70 percent of its workforce in agriculture, producing a third of the country’s GDP. This discrepancy between agriculture’s claim on the economy’s resources and its contribution to output reflected the large differential in labor productivity across activities. The typical worker in manufacturing produced four times more output than the typical worker in agriculture. The typical worker in services such as construction or wholesale and retail trade produced even a bigger multiple than this. But over the next two decades, workers moved from lowerto higher-productivity activities (Figure O.3). In Chapter 2 of this book, McCaig and Pavcnik tell us that agriculture’s employment share declined to 54 percent, while services’ share rose from 18 percent to 32 percent, and manufacturing’s share rose from 8 percent to 14 percent. During the 2000s, jobs in manufacturing grew at an annual rate greater than 10 percent, with the rate exceeding 15 percent in garments and reaching 30 percent in office and computing machines. The growth was particularly rapid in the

**FIGURE O.3** Vietnam’s workers move to higher-productivity sectors

<!-- Start of picture text -->
1.4 Relationship between initial productivity and changes in employment share, 1990–2008<br>1.2<br>fire<br>1.0<br>0.8 pu<br>0.6 min<br>wrt<br>0.4 con<br>0.2 tsc man<br>0.0 cspsgs<br>–0.2<br>–0.4 agr<br>y = 3.0091 x + 0.4296<br>–0.6 R 2 = 0.343<br>–0.8<br>–0.30  –0.25  –0.20  –0.15  –0.10  –0.05  0.00  0.05  0.10 0.15<br>Change in employment  share<br>1990<br>in<br>iittcyv<br>du<br>orp<br>tea<br>g<br>e<br>gr<br>/iittagcyv<br>u<br>odrp<br>ltaroc<br>e<br>s<br>fo<br>og<br>L<br><!-- End of picture text -->

Source: McCaig and Pavcnik, Chapter 2 in this book. Note: The size of the circle indicates employment in 1990. agr = agriculture; cspsgs = community, social, personal services, and government services; con = construction; fire = finance, insurance, real estate, and business services; man = manufacturing; min = mining and quarrying; pu = public utilities (electricity, gas, and water supply); tsc = transport, storage, and communications; wrt = wholesale and retail trade.

15
<!-- Start of picture text --> 25%<br>20% \ “fn<br>\ 4eN<br>=> 15% \ oN<br>s \ / \<br>6= 10% \~7] \\ ‘\<br>ac£ 5% Kt“ Wi 1 or eS<br>0% “~fiu-- .<br>s ~e= o<br>fe) \<br>Q<br>© -10% \<br>‘<br>=<br>&-15%<br>-20%<br>-25%<br>2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>eee Acriculture, forestry and fishing Mining and quarrying<br>Manufacturing Construction<br>@ee Wholesale and retail trade<br><!-- End of picture text -->
<!-- Start of picture text --> 0.33<br>Male °0.46—e—e<br>-0.96<br>Co) Informal Employees ° -0.39..<br>£<br>an<br>fa] Education groups groups<br>0.30<br>= Primaryii Education"" 02see<br>0.36<br>Secondary Education i —*oa7<br>© 0.72<br>4 -2 Log0 hourly labour income2 4 6 Higher education education —e— 1g9<br>Formal 2007 Informal 2007 -1.00 -0.50 Point estimate0.00 estimate0.000.00 and 95%0.50 95%0.500.50 Cl 1.00 1.50<br>Formal 2012 Informal 2012<br>———_ 2007 ——_ 2012<br><!-- End of picture text --> <!-- Start of picture text --> 0.33<br>Male °0.46—e—e<br>-0.96<br>Informal Employees ° -0.39..<br>Education groups groups<br>0.30<br>Primaryii Education"" 02see<br>0.36<br>Secondary Education i —*oa7<br>0.72<br>Higher education education —e— 1g9<br>-1.00 -0.50 Point estimate0.00 estimate0.000.00 and 95%0.50 95%0.500.50 Cl 1.00 1.50<br>———_ 2007 ——_ 2012<br><!-- End of picture text -->
<!-- Start of picture text --> Meee eee - -<br>Stace Gant 6 cen Aree | oo **<br>Cant Ace 8 Peat | oo “ff<br>heros ere a ad<br>Gee + Coren Ane a **<br>Seu ee ae **<br>a re ane ae . *<br>> ' z :<br>Le ee<br>© ce ee | CED ere<br>Saiticeae: 4 Xft aaa<br><!-- End of picture text -->

<!-- Start of picture text --> (as moore * =<br>Loew FI) wore ae se<br>poe ad es<br>Upper matte moore od *-F*<br>' 2 ' e z a<br>emersonne COVE perce<br>Ome «6 SCTE aero<br>© St ae 4 Olt eomeew<br><!-- End of picture text -->
To some extent, this continued enthusiasm may be counterintuitive in view of recurring international financial and liquidity crisis over the last 10-15 years. These crises should have reduced the interest in project finance to finance new toll roads, new airports, new ports, or new railways in emerging markets. Although after the financial crises in East Asia, Russia, Mexico, Brazil or Argentina during the 1990s, project financing almost systematically slowed down but it has also systematically recovered. This is because new sources of money continue to appear. From pension assets to emerging bond markets to new types of bank debt, liquidity is not lacking. Private capital flows to emerging markets reached a new peak in 2006, US$ 623 billion<sup>2</sup> . Even if credit to some actors may be tighter, the global financial markets continue to be liquid and investors are still looking for predictable sources of revenue which most transport infrastructures are potentially capable of providing. Spreads may increase to hedge against increased credit risk and as a result increased de-leveraging but the market will not disappear. Transport infrastructure where the end-user is represented by corporate or commercial clients tends to be less risky given their higher payment capacity of tariffs and charges (i.e., airports, ports, cargo railways, etc.). Conversely, transport infrastructure where the end-user is represented by consumers tends to have more affordability issues and therefore higher risks (i.e., urban transport, toll roads, etc.).

To some extent, this continued enthusiasm may be counterintuitive in view of recurring international financial and liquidity crisis over the last 10-15 years. These crises should have reduced the interest in project finance to finance new airports, new ports, new railways or new ports in emerging markets. Although after the financial crises in East Asia, Russia, Mexico, Brazil or Argentina during the 1990s, project financing almost systematically slowed down but it has also systematically recovered. This is because new sources of money continue to appear. From pension assets to emerging bond markets to new types of bank debt, liquidity is not lacking. Even if credit to some actors may be tighter, the global financial markets continue to be liquid and investors are still looking for predictable sources of revenue which most transport infrastructures are potentially capable of providing. Spreads may increase to hedge against increase credit risk and as a result increased de-leveraging but the market will not disappear.

> 2 World Bank: Global Financial Markets

2
- 22 -

£ more capital and thus generate more domestic rents.<sup>10</sup> Finally. there may be terms of trade effects associated with capital inflows, as has been stressed "in the trade literature."<sup>1</sup> In each of these cases, wasteful ("beggar-thy-neighbor") tax competition will be the result. Investment incentives are obviously the prime policy instrument for attracting foreign investment into a country. As mentioved, the. effectiveness is contingent upon foreign tax crediting systems not merely causing them to transfer tax revenues abroad.

Informational asymmetries may also characterize international capital flows. Thus, foreign invtstors may not have full information about which countries are high profit countries and which **2!e** not. This is especially true if there is some uncertainty about future government policies. In this case, high return countries may want to "signal" their quality by offering special incentives to potential investors. Bond and Samuelson (1986) have used this as an argument for tax holidays **La** developing countries, a phenomenon which appears to be quite widespread. Since much of the uncertainty about investment returns in developing countries might come from uncertainty about future government policies and its effect on after-tax cash flows, incentives which improve cash flows up-front would probably be the most effective signals. It is not clear that tax holidays fall into this category, especially when marginal effective tax rates are low to begin with as discussed below. Refundable investment incentives, such as investment tax credits, would be more effective.

# _8. Distortions on Other Markets_

So far our discussion has been entirely about market failure on capital markets. However, in developing economies, other markets may be significantly distorted and this may be relevant for evaluating the effects of investment incentives. Two markets in particular are liable to be distorted-labor markets and foreign exchange markets. The literature on project evaluation in developing countries deals largely with the issue of investment criteria when there are distortions on these markets.<sup>12</sup> On the basis of first principles, we know that distortions should be dealt with directly on the markets involved. However, where this is not possible, their implications for investment decisions should be dealt with on 0 **'eczlassic** paper in **this** area is MacDougapl (1960).

_11_ See the _survey_ in Harris (1989).

12 **For** a **general treatment, see** Boadway and Bruce (1984b) and **Drdze** and **Stern** (1987). See also **the** variou!s manuals for project evaluation in developing **countries.**
22 We study next the properties of the ECM on different sample periods in order to answer the following questions: Can we quantify the response of POLDIS to a shock to MEXDIS? To which extent the volatility of POLDIS is due to the volatility of MEXDIS?

These questions can be answered through what is generally referred to as _innovation accounting,_ i.e. the _impulse response function_ and _the forecast error variance decomposition._ The impulse response function uses the estimated parameters of the model to trace out the effects of one standard deviation (SD) change in the errors on the time path of MEXDIS and POLDIS sequences; the forecast error variance decomposition uses the estimated parameters and indicates the proportion of the forecast errors on a variable which is due to its own shocks (residuals) as opposed to shocks (residuals) to the other variable. 22 <!-- Start of picture text --> 600<br>400<br>200<br>0,<br>-200,<br>.400 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>Long Run Residuals<br><!-- End of picture text --> _Figure 8_ The impulse response function in figure 9 shows that: 1) a one standard deviation innovation to MEXDISI (the SD of the residuals on MEXDIS I being of 91 bs. pts.) has a permanent effect of _36 bs. pts_ on MEXDIS I and of _35 bs. pts._ on POLDIS; 2) a one standard deviation innovation to POLDIS (the SD of the residuals on POLDIS being of 44 bs. pts.) has a permanent effect of _29 bs. pts._ on MEXDIS I and of _27 bs. pis._ on POLDIS. We can conclude that a 22 The only problem with this methodology is that an estimated VAR is underidentified: an additional restriction must be imposed on the two variable VAR system in order to identify the impulse responses and variance decomposition. The usual restriction is given by the assumption that one error has a contemporaneous effect on both variables, while the other one has a contemporaneous effect only on its own variable. This assumption is said to imply an _ordering_ of the variables.

We assume that _E,_ **_M,_** has a contemporaneous effect on both variables, while C p, does not. This is not an innocuous assumption: the correlation coefficient on the estimated residuals is above 0.7, so the ordering matters. Our assumption is based on several considerations. The first one is that, at least for the first year of our sanple (when the evidence of cointegration is stronger), all the relevant volatility was coming from the news on Mexico and not on Poland. Second, we regressed the residuals from the cointegration relation (the "long run" residuals, LRER in figure 8) on the standard deviation of MEXDIS (MSIG) and on the standard deviation of POLDIS (PSIG): not only MSIG is more significant than PSIG and with the right sign, but the negative sign of PSIG suggests that the volatility of MEXDIS dominates. Finally, and consistently with our belief that Mexico is a leader in the Brady market, the IMF (1995) finds evidence of Granger causality from Mexico to other countries using daily bond prices.
- **Bordalo, Pedro, Nicola Gennaioli, and Andrei Shleifer** , “Salience Theory of Choice under Risk,” _Quarterly Journal of Economics_ , 2012, _127_ (3), 1243–1285.

- **, , and** , “Salience and Consumer Choice,” _Journal of Political Economy_ , 2013, _121_ (5), 803–843.

- **, , and** , “Memory, Attention and Choice,” _Working Paper_ , 2015.

- **Campante, Filipe and David Yanagizawa-Drott** , “Does Religion Affect Economic Growth and Happiness? Evidence from Ramadan,” _The Quarterly Journal of Economics_ , 2015, _130_ (2), 615–658.

- **Campbell, John Y., Howell E. Jackson, Brigitte C. Madiran, and Peter Tufano** , “Consumer Financial Protection,” _Journal of Economic Perspectives_ , 2011, _25_ (1), 91–114.

- **Cantoni, Davide** , “The Economic Effects of the Protestant Reformation: Testing The Weber Hypothesis in The German Lands,” _Journal of the European Economic Association_ , 2015, _13_ (4), 561–736.

- **Clingingsmith, David, Asim Ijaz Khwaja, and Michael Kremer** , “Estimating the Impact of The Hajj: Religion and Tolerance in Islam’s Global Gathering,” _The Quarterly Journal of Economics_ , 2009, _124_ (3), 1133–1170.

- **Dal B ́o, Ernesto and Pedro Dal B ́o** , ““Do the Right Thing:” The Effects of Moral Suasion on Cooperation,” _Journal of Public Economics_ , 2014, _117_ , 28–38.

- **DellaVigna, Stefano, Ulrike Malmendier, and John A. List** , “Testing for Altruism and Social Pressure in Charitable Giving,” _Quarterly Journal of Economics_ , 2012, _127_ , 1–56.

- **Einav, Liran, Amy Finkelstein, Stephen P. Ryan, Paul Schrimpf, and Mark R. Cullen** , “Selection on Moral Hazard in Health Insurance,” _American Economic Review_ , 2013, _103_ (1), 178–219.

- **Fellner, Gerlinde, Rupert Sausgruber, and Christian Traxler** , “Testing Enforcement Strategies in the Field: Threat, Moral Appeal and Social Information,” _Journal of the European Economic Association_ , 2013, _11_ (3), 634–660.

- **Frey, Bruno** , _Not Just for the Money: An Economic Theory of Personal Motivation_ , Cheltenham, UK: Edward Elgar, 1997.

- **Gabaix, Xavier** , “A Sparsity-Based Model of Bounded Rationality,” _Quarterly Journal of Economics_ , 2014, _129_ (4), 1661–1710.

- **Gneezy, Uri** , “Deception: The Role of Consequences,” _American Economic Review_ , 2005, _95_ (1), 384–394.

- **Guiso, Luigi, Paola Sapienza, and Luigi Zingales** , “The Determinants of Attitudes towards Strategic Default on Mortgages,” _Journal of Finance_ , 2013, _68_ (4), 1473–1515.

- **Iannaccone, Laurence R** , “Introduction to the Economics of Religion,” _Journal of Economic Literature_ , 1998, _36_ (3), 1465–1495.

21