rafmacalaba/datause-encoder-data huggingface dataset:
shocks, fluctuations in the macroeconomic growth rate significantly affect the probability of renegotiations, i.e. recessions increase it while booms reduce it, and this probability also goes up in years following national elections, although this last effect is only weakly significant.

Table 6: Random effect probit panel Dependent variable: Dummy variable indicating the occurrence of renegotiation initiated by the firm

||(1)|(2)|(3)|(4)|_(5)_|(6)|
|---|---|---|---|---|---|---|
|Existenceof<br>regulatorybody|-1.09*<br>(-5.07)|-1.15*<br>_(-5.05)_|-1.07*<br>(4.98)|-1.08*<br>(-5.09)|-1.10*<br>_(-5.09)_|_-1.51"_<br>(-5.82)|
|Pricecap|0.55***<br>(1.68)|0.63***<br>(1.88)|0.58***<br>(1.75)|0.60***<br>(1.85)|0.45<br>(1.25)|0.81**<br>(2.46)|
|Durationsinceaward|0.19*<br>(3.97)|0.18*<br>(358)|0.20*<br>(4.07)|0.20*<br>(4.03)|0.18*<br>(3.53)|0.15*<br>(2.72)|
|Investment|0.78**|0.74***|0.83**|0.60|0.79**|0.65***|
|requirements|(1.99)|(1.87)|(2.09)|(1.57)|(1.99)|(1.71)|
|Privatefinancing|0.51**<br>(1.90)|0.40<br>(1.38)|0.48***<br>(1.80)|-0.11<br>(-0.23)|0.55**<br>(2.00)|0.15<br>(0.53)|
|Bureaucraticquality|-0.35**<br>(-2.54)|-0.29***<br>(-1.82)|-0.36*<br>(-2.60)|-0.39*<br>(-2.76)|-0.32**<br>(-2.19)|-0.21<br>(-1.47)|
|Biddingprocess||-0.27<br>(-0.91)|||||
|Durationofcontract|||-0.01<br>(-1.00)||||
|Arbitration process||||0.73<br>(1.32)|||
|Minimumincome<br>guarantee|_||||0.16<br>(0.67)||
|Corruption||||||o0.43*<br>(-3.73)|
|Election-I|0.29<br>(1.53)|0.30<br>(1.59)|0.28<br>(1.50)|0.27<br>(1.44)|0.29<br>_(1.55)_|0.40***<br>(1.95)|
|GDP growth-I|-0.06*<br>(-.3.38)|-0.06*<br>(-3.24)|-0.06*<br>(-3.17)|-0.06*<br>(-3.21)|-0.06*<br>(-3.36)|-0.07*<br>(-3.26)|
|GDPgrowth-2|-0.14*<br>(-6.15)|-0.14*<br>(-6.18)|-0.14*<br>(-6.06)|-0.14*<br>(-6.15)|-0.14*<br>(-6.14)|-0.15*<br>(-6.07)|
|Transport sector|0.75**|0.80**|0.70**|0.59|0.69***|_1.05*_|
|dummy|(2.21)|(2.32)|(2.03)|(1.53)|(1.93)|(2.94)|
|Numberofobs.|1132|1132|1128|1100|1127|1132|
|LogLikelihood|-132.49|-132.08|-131.95|-130.97|-132.26|-125.21|

Coefficients significant at the 1% (*), _5%_ (**) and 10% (***) level.

25
10

**Figure 3(a) and 3(b): ATMs and ATM transactions per capita with cash to GDP ratios, 1993**

**Figure 3(a)**

<!-- Start of picture text --> Figure 3(a) Figure 3 (b)<br>10 40<br>. Canada<br>Japan .<br>9 . . Finland<br>35 Japan<br>80<br>8 Finland Netherlands<br>D 30<br>13 7 - 8 25 U.S. * Sweden<br>6 Canada 5<br>t t U~~~~~~~~~~~~~~~~~~~~.K.<br>a 5 E 20 -<br>eZ 4 U.S. Switzerland<br>France 15 No<br>o UK.. * Netherlands Fac<br>Germany France<br>Denmark ISw ten 'y 10 Switzerland<br>2 Belgium 0<br>7 * Belgium 5<br>1 Germany Italy<br>Denmark<br>0 l l l 0 I l I<br>0% 2% 4% 6% 8% 1091 0% 2% 4% 6% 8% 10%<br>Cash to GDP ratio Cash to GDP ratio<br><!-- End of picture text --> _Source: BIS: Payment System in the C- 10 Countries, 1994_ This analysis supports the conclusion that in most major developed countries except Japan, cash is being increasingly replaced by card-based electronic point-of-sale payment methods-such as credit and debit cards-brought about by the increased availability of EFT-POS terminals. Econometric analysis supports this contention and also suggests that a heavy reliance on cash for POS transactions, as in Japan, is associated with having a low rate crime rate. As well, the substitutability of credit cards for cash is viewed as also being dependent upon pricing and a country's cultural attitude toward credit. Since per transaction credit card fees are largely borne by retailers that accept them for payment, credit cards are Werceived by consumers as a low cost, delayed payment substitute for an immediate cash payment. Electronic cash loaded on chip cards or transmitted through open _Countries that have the highest incidence of credit card use per person (the U.S. and Canada) also have a culture in which personal credit is quite acceptable. The cultural acceptability of credit cards is lower in Europe in part because of a history of relying on savings (rather than credit) for many consumer expenditures. This tradition is reinforced by a historical European reliance on giros-a method of immediate and final payment-for consumer bill payments. Giro transactions rarely involve the extension of credit while payments by check-a_
pre-registration surveys and inspections by authorities. Overall, the time taken to start a business would fall to around 9 days, compared to estimates of “15 days” (Jornal S’Passo, 2011) to “months” (Gxp, 2011) in some municipalities previously.

In some municipalities, there was also a reduction in the fees required for registering. The licensing process requires a feasibility consultation (Consulta de Viabilidade) to determine whether the firm can operate under the desired name at the desired location. In the absence of Minas Fácil Expresso, this consultation was conducted by the local authorities for a fee that varied between 15 and 100 Reais (US$7 and 48), depending on the municipality. With Minas Fácil Expresso, the consultation is done online for free. In addition, many municipalities had previously required that owners pay back any debt relating to unpaid property taxes before registering, and this was now no longer required.

The State Government set a target of opening 50 Minas Fácil Expresso offices by the end of 2011, and then expanding further to the rest of the state. An initial list of 50 municipalities were selected based on the volume of registrations occurring in 2010, as well as whether the municipalities had expressed interest in having the program (through the Mayor, Professional Associations, Legislative Hall, or State Legislature). In practice actual implementation occurred for 32 of these 50 municipalities, beginning in September 2011, along with a further 23 municipalities for a total of 55 municipalities by the end of August 2012. Figure 1 shows the number of municipalities receiving a Minas Fácil Expresso unit each month.

Municipalities wishing to implement the Minas Fácil Expresso unit had to implement a General Municipal Law (Complementary Law 123) which relates to starting a business, as well as support the implementation of the project. Deploying a unit costs approximately 40,000 Reais (US$19,000) for the first year, which includes the cost of training, computer equipment, furniture, and running costs.

The opening of the Minas Fácil Expresso offices was covered by local newspapers in at least some of the municipalities (Jornal S’Passo, 2011; Gxp, 2011), although it is unclear how widespread advertisement of the new system to firms was.

# **3. Administrative Data**

We use three forms of administrative data, all provided by the _Junta Comercial de Minas Gerais (JUCEMG)_ , the Minas Gerais Chamber of Commerce. The first is a list of the municipalities in which the express office was planned to be implemented, and of the municipalities in which it was actually 5
which were surveyed in the first phase in 2008 and the subsequent reforms are reflected in the database. As indicated above, we invite feedback on the accuracy of the data, and suggested changes will be incorporated in the database after verification. Ideally these updates would be systematic and would eventually lead to a panel of policy data across countries and over time which would also support more rigorous policy analysis. To this end, we propose to collaborate in future with the Trade in Services Division of the World Trade Organization (WTO) which is also launching a policy data collection initiative.

# **2.2 Relationship to other databases**

It is useful to clarify how this database fits into the existing data landscape. While no other database collects services trade policies for a similarly wide range of countries and sectors, there exist related datasets such as the World Bank‘s Investing Across Borders data, the World Bank‘s Doing Business data, and the OECD‘s Product Market Regulation (PMR) database.<sup>9</sup> These databases are largely complementary to our database, and together they provide an increasingly comprehensive perspective on policy regimes for trade in services.<sup>10</sup> Two important dimensions along which existing datasets differ are: whether the focus is on foreign providers only or on all firms; and whether the focus is on policies and regulations or on administrative/implementation requirements and, in some cases, outcomes. At the risk of oversimplification, Table 2 positions the Services Trade Restrictions Database and other related data sources along those two dimensions.<sup>11</sup>

> 9  There are other, sector-specific databases, such as the World Bank‘s FinStats, a world-wide financial database which covers 40 key financial indicators, the World Bank‘s Logistics Performance Index, an interactive benchmarking tool to help countries assess their performance on trade logistics, or the International Telecommunication Union‘s World Telecommunication/ICT Indicators Database.  The primary focus of these databases is on outcomes in the respective sectors, and less so on policy affecting foreign services or service providers.  These databases are invaluable for sector impact analysis of the kind that we illustrate in Section 5. 10  Francois, Pindyuk and Wörz (2009) have compiled a unified dataset of services trade flows encompassing cross-border and FDI data from various sources (Trade in Services Database – TSD version 4).  Trade flow data is likewise highly complementary to trade policy information.

> 11  Table 2 cannot do justice to the richness of information contained in each database listed but merely intends to clarify the difference in focus.

10
|**Table 6: Impact on Dealings with Custom**|**ers**<br>Weekly|Gained new|Lost regular|Sales per|Open Business|<br>Introduced|
|---|---|---|---|---|---|---|
||Customers|Customer|Customer|Customer|at set time|New Product|
|**Panel A: Impact of Assignment to Trainin**|**g, Allowing**|**for Spillover**|**s**||||
|Assigned to Training*1 Year|1.7|0.023*|‐0.038**|25.5||0.116***|
||(3.8)|(0.013)|(0.017)|(30.0)||(0.022)|
|Assigned to Training* 3 Years|14.5***|0.034**|‐0.041***|38.7|0.062***|0.089***|
||(4.4)|(0.016)|(0.015)|(25.9)|(0.024)|(0.021)|
|Spillover Group * 1 Year|‐2.2|0.009|‐0.020|0.7||0.007|
||(3.9)|(0.014)|(0.018)|(20.6)||(0.021)|
|Spillover Group * 3 Years|‐0.7|0.008|‐0.009|2.2|0.030|0.008|
||(4.6)|(0.018)|(0.016)|(17.1)|(0.023)|(0.021)|
|Mean of Pure Control Group|100|0.767|0.804|134|0.342|0.269|
|Sample Size|12867|12977|12978|11186|2398|6216|
|P‐value: Training 1 year=3 years|0.004|0.586|0.878|0.734||0.327|
|P‐value: Spillover 1 year=3 years|0.753|0.964|0.666|0.951||0.970|
|P‐value: Training effect zero both years|0.003|0.049|0.003|0.247||0.000|
|**Panel B: Impact of Mentoring Compared**|**to Trainin**|**g Alone, Allow**|**ing for Spillo**|**vers**|||
|Assigned to Mentoring|12.1*|0.004|‐0.048***|50.3|0.103***|0.102***|
||(6.1)|(0.021)|(0.017)|(38.0)|(0.033)|(0.028)|
|Assigned to Training Alone|12.0**|0.054***|‐0.054***|24.6|0.030|0.084***|
||(5.2)|(0.020)|(0.017)|(24.6)|(0.031)|(0.028)|
|Spillover Group to Mentoring|0.8|0.000|‐0.035*|‐5.3|0.026|0.021|
||(6.4)|(0.022)|(0.020)|(19.1)|(0.030)|(0.026)|
|Spillover Group to Training Alone|‐4.3|0.009|‐0.002|‐0.0|0.039|0.022|
||(6.0)|(0.023)|(0.019)|(20.5)|(0.032)|(0.029)|
|Sample Size|5602|5636|5636|4801|2090|2636|
|P‐value: Mentoring= TrainingAlone<br>Notes: Robust standard errors in parent<br>*, **, and *** indicate significance at the<br>All regressions control for randomization<br>Opening at set time only asked in round|0.992<br>heses, clust<br>10, 5, and<br>strata fixe<br>4 survey an|0.020<br>ered at the m<br>1 percent lev<br>d effects.<br>d is condition|0.758<br>arket level.<br>els respective<br>al on busine|0.558<br>ly.<br>ss operatin|0.071<br>g.|0.597|

33
- Schiff, Maurice, Yanling Wang and Marcelo Olarreaga (2002), ―Trade Related Technology Diffusion and the Dynamics of North-South and South-South Integration,‖ World Bank Policy and Research Working Paper 2861, June.

- Smith, Alasdair, and Venables, Anthony J., "Completing the Internal Market in the European Community: Some Industry Simulations", _European Economic Review_ , 32, 1988, 1501-1525.

- Tarr, David (2002), "On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs,‖ in B. Hoekman, A. Mattoo  and P. English (eds.), _Development, Trade and the WTO: A Handbook_ , Washington: World Bank.

- United Nations Conference on Trade and Development and World Bank (1994), Liberalizing Trade in Services: A Handbook, New York and Geneva: United Nations.

- United Nations Conference on Trade and Development, Division on Transnational Corporations and Investment (1995 and 1996), World Investment Report 1995 and 1996, New York and Geneva: United Nations.

- Wang, Yanling (forthcoming), ―FDI Productivity and Growth: The Role of Inter-Industry Linkages,‖ _Canadian Journal of Economics_ .

- Warren, Tony (2000), ―The Impact on Output of Impediments  to Trade and Investment in Telecommunications Services,‖ in _Impediments to Trade in Services: Measurement and Policy Implications_ , Christopher Findlay and Tony Warren (eds.), London: Routledge.

- World Bank (2007), _Food Safety and Agricultural Health Management in CIS Countries: Completing the Transition_ , Agriculture and Rural Development Department, Washington D.C.: The World Bank.

38
# **Table A10. Descriptive Statistics for Students in Brazil** **_Based on Individual-Level Data for Students Matched to Surveyed Programs_**

|**Variable**|**Mean**|**Std. Dev.**|
|---|---|---|
|**Panel A. Student characteristics**|||
|Age|24.60|5.57|
|Female|0.47|0.50|
|Mother's education:|||
|Less than primary|0.11|0.31|
|Primary school|0.14|0.35|
|High school|0.19|0.39|
|Higher education|0.07|0.26|
|Unknown|0.49|0.50|
|Student administrative variables (PCA Score)|0.00|1.15|
|**Panel B. Peer(average) characteristics**|||
|Age|24.65|1.77|
|Percentage of female peers|0.47|0.28|
|Percentage of peers by mother's education:|||
|Less than primary|0.11|0.05|
|Primary school|0.14|0.06|
|High school|0.19|0.07|
|Higher education|0.07|0.06|
|Unknown|0.48|0.14|
|Peers'administrative variables (PCAScore)|-0.07|1.45|
|**Panel C. Outcomes**|||
|Graduated|0.30|0.46|

_Sources_ : Own calculations using individual-level administrative data for Brazil. For more details on data sources and variable definitions, see Section 3.3 and Appendix 1.

_Notes_ : In this table, the unit of observation is a student. Statistics are weighted using WBSCPS sampling weights. For a given student, her peers are the other students who started her program in 2014. Means of PCA scores are different than zero due to the use of sampling weights. Number of observations is 29,453.

82
Table D.26. Effects of Guerrilla Territorial Control on Expropriation, Invasion, and Non-Democratic Believes

||Invading|Occupying|Overturn|Taking Law in|Non-Democratic|
|---|---|---|---|---|---|
||Property|Buildings|the Government|Own Hands|Engagement (sum)|
||(1)|(2)|(3)|(4)|(5)|
|Guerrilla control|-0.110*|0.0616|-0.00269|-0.0734|0.804|
||(0.0605)|(0.0815)|(0.0721)|(0.136)|(1.922)|
|Observations|248|175|248|245|172|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|2.266|
|Dependent mean|0.0580|0.109|0.0740|0.245|10.69|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to believes regarding how acceptable it is to engage in certain actions against private property or the government. These are measured in a 1-10 scale, thus, we assume that individuals support these behaviors whenever their agreement level is above 5. Columns 1 to 4 show the share of individuals who think that invading property, occupying buildings, overthrowing the government or taking law in their own hands are acceptable. Column 5 shows the effects of guerilla control on an index comprised of the sum of the raw scores . The information was recovered from the LAPOP Surveys. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

94
firms in Mexico City paid 11 percent less. Thus, southern firms paid the lowest wages across all sizes of firms compared to all of the other regions, and northern firms paid the most.

With respect to productivity in 1999, the greatest productivity differentials are found in southern micro and medium firns that were 50 and 61 percent, respectively, less productive than northern firns. Small firms in Mexico City were also found to be much less productive than small firms in the northern region (46 percent). In general, southern firms tend to be less productive.

# **vi. Firm Size**

In 1993 productivity in small, medium, and large firms was higher than micro _frmns_ by 23, 30 and 27 percent, respectively. This productivity gap among firm sizes increased from 1993 to 1999. In 1999, small, medium, and large firms were more productive than micro firms by 38; 93; and 124 percent, respectively.

|||**T**<br>|**able2 **<br>|**4:Firm**|**size(Pooled**|Estimation|)<br>|||
|---|---|---|---|---|---|---|---|---|---|
|Variable||**1**|**993**||||**1**|**999**||
|Size|**Wage**|**P>jzi**|**Prod**|**uctivity**|**P>Izl**|Wage|**P>Izl**|Productivity|**P>Izl**|
|Small|-0.02|0.51||0.23|0.00|0.06|0.01|<br>0.38|0.00|
|edium|-0.04|0.38||0.30|0.00|0.15|0.00|<br>0.93|0.00|
|Lrge<br>I|<br>-0.10|0.06||0.27|0.00|0.25|0.00|<br>1.24|0.00|

_Source_ Authors' calculations based on ENTRAM-ENESTYC In 1999 firm size was positively correlated with wages and productivity (see Table 24). With respect to wages, large firms paid close to 25 percent more than micro firms in 1999. The correlation of firm size with wages and productivity is positive, but the impact on the latter was shown to be far greater.

# **6. Conclusions**

Using linked employee-employer manufacturing sector data (the ENTRAMENESTYC surveys from 1993 and 1999), this paper examined the micro-determinants of wages and productivity in Mexico. We used two kind of variables, those of the supply 25
11 seven countries for which we have consistent data on returns and investability indexes."<sup>2</sup> Since we are not studying unrestricted and restricted shares of the same firm, but rather shares of individual firms which vary in degree of restrictiveness, we cannot calculate the ratio of foreign to domestic prices here.'<sup>3</sup> However, we can study the price-earning ratio of a stock. Similarly, we can use domestic rates of return (instead of the difference between the returns to foreign and domestic shareholders).

We estimate mimicking portfolios on the basis of the IFC-index for the emerging market and the MSCI-indexes for 13 industrial countries. Specifically, the mimicking portfolios are created through OLS-regressions of (the rates of return on) the indexes on (the rates of return on) the 13 MSCI-indexes. We then use the predicted values from this regression as the rates of return on the mimicking portfolio." As in Stulz and Wasserfallen (1992) and Bailey and Jagtiani (1992), we also include in the estimations a size (or supply) variable, here taken as the log of the market capitalization of each stock, MVi,,. Finally, we use actual instead of expected P/E-ratios or returns.

We thus model the P/E-ratio, or alternatively, the (excess) rate of return of domestic stock i in market j, as:

# **_(9)_**

<!-- Start of picture text --> (P/E)i/ = a0., + al,,8t + C2.tw1}<br>+ 3jAjJf - kj) + " 4;MViJ ++ c<br><!-- End of picture text --> where 6j , indicates the share foreigners can buy of a particular stock i at time t (the investability index), 0,j is the slope coefficient of stock i on the world portfolio (here the MSCI world index), ,B, **i** s the slope coefficient of stock i on the mimicking portfolio A, j;, is the slope coefficient of stock i on the local market index j (note that the as are not stock specific), and where the subscript t for the 1,-coefficients indicates that these are estimates updated every month using the previous three years of data. The difference between j,ij and ,B,j represents the domestic risk that cannot be hedged through positions in foreign assets. When there is no residual risk to bear, OBM - ,,j = 0 and the world CAPM prevails. To estimate the betas, we

# '<sup>2</sup> The results for 7 other countries are available upon request.

'<sup>3</sup> Even though we have some stocks of the same firm (e.g., Telmex shares A, B, C and L) which differ in degree of investability, the sample of such stocks is small. Other foreign prices are available in the form of country-fund and ADRs prices. For an analysis of country-fund prices, see Hardouvelis, La Porta and Wizman (in this volume) and Diwan, Errunza and Senbet (1993a and 1993b).

"The mimicking is, as expected, generally poor as these markets have a low correlation with markets of industrial countries. The residual domestic risks are consequently quite large.
- 60 -

the higher the proportion of eligible beneficiaries (i.e., the lower the percentage of non-poor) with low initial level of incomes, the more difficult it is for them to cross the poverty income threshold, i.e., very poor people are being brought into the program, such that (a) a big income increase still leaves them below the poverty lines, and (b) it is hard for them to overcome poverty; the proportion doing so is small.

A major criticism of IRDP is that the program benefits the households closer to the poverty line income threshold so that the ultra poor are unable to take advantage of the scheme. It is suggested that the poorest households may not be able to hold and manage assets. Recent evidence, however, effectively refutes this criticism. In fact, in an administratively weak and relatively poorer state like Uttar Pradesh panel data for 4 years show that the poorest households not only managed to hold on to assets, but derived income from them on a sustained basis (C.H.H. Rao, et el, and Pulley). Their problem was the continued reluctance of the institutional credit agencies to lend working capital on a regular basis even after the households have promptly repayed their IRDP loans. In other words, IRDP enabled access to institutional credit for the poorest households as a one-shot injection, but failed to open a continuing line of credit for the neediest households notwithstanding their proven creditworthiness.

The scale of investments in the IRDP, in combination with NREP, may give impetus for the development of supportive infrastructure and to raise the incomes of the poor at the margin, provided the choice of assets are matched with the level and structure of demand in the region; the assets supplied are labor-intensive in character; and supporting marketing networks are
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