rafmacalaba/datause-encoder-data huggingface dataset:
# Figure VIII: Impact of Withholding Rate Increase

<!-- Start of picture text --> DD=.326 (.022)<br>2010m1 2011m1 2012m1 2013m1<br>Month<br>Treatment Group (Rate Increase) Control Group<br>2.2<br>2<br>Change 1.5<br>1<br>.7<br><!-- End of picture text --> Notes: The gure displays the results of the dierence-in-dierence estimation of Equation 2, with total tax payment as outcome variable. It considers the overall impact of the reform, pooling all rms with a predicted rate increase in the treatment group. The control group includes rms experiencing no increase in the predicted withholding rate and rms not subject to withholding. The black solid line marks 08/2011, when the increase in withholding rates entered into eect. The data is winsorized by the 99th percentile, and scaled by the pre-reform average. The text displays the coecient _β_ (marginal eect compared to pre-reform average) from estimating Equation 2.

# Figure IX: Aggregate Impact of Withholding Rate Increase

<!-- Start of picture text --> Aggregate Sales Tax Revenue Aggregate Simplified Regime Tax Revenue<br>Pre-Reform Mean: RD: 24.9 (3.19) ~ 8.4% Increase Pre-Reform Mean: RD: .026 (.053)<br>294.838 Bio. CRC .5 Bio. CRC<br>2008h2 2009h2 2010h2 2011h2 2012h2 2013h2 2008h2 2009h2 2010h2 2011h2 2012h2 2013h2<br>Semester Semester<br>Tax Revenue Linear Fit Tax Revenue Linear Fit<br>50 .5<br>40 .4<br>30 .3<br>.2<br>20<br>.1<br>10<br>Revenue Demeaned (Bio. Col) Revenue Demeaned (Bio. Col) 0<br>0<br>-5 -.1<br><!-- End of picture text --> Notes: This gure shows the impact of the withholding rate reform in August 2011 on aggregate sales tax revenue (upper panel), and on aggregate revenue from the quarterly simplied regime tax, as a counterfactual (bottom panel). The sales tax data is based on ocial statistics from the Ministry of Finance, and the simplied regime data is based on rm-level tax declarations. The dots represent semester-wise tax revenue in billions of Costa Rican colones demeaned by the pre-reform semster-level means. For the upper panel, semesters are dened to t exactly around the time of the reform. For each year, the rst semester includes February to July, and the second semester includes August to December, and January of the following year. The red line is a linear t, allowing for a discontinuity at the time of the reform. The text displays the pre-reform semester-level mean revenue from the tax (in blue), and the coecient and standard error on the post-reform dummy (in red). The results are robust to running the analysis on monthly or quarterly data, using shorter or longer time series, and adding controls for the months of December and January (in the monthly data).

45
- _Diverse methodologies_ **.** Some of the data collected by the WBGES 2008 might differ from official data published by individual countries.  The reason for this is that local statistical agencies might use a different methodology.  For instance, the European Statistical Agency (Eurostat) methodology is based on the minimum number of employees to measure entrepreneurship. As a result, in some cases the data published by Eurostat and the WBGES, while accurate in all cases, differs.

# **3. Data on new firm creation**

The WBGES 2008 gathered data on the number of new and total businesses for 101 countries for the time period 2000 to 2008. In order to make this data comparable across countries, three different indicators were used, each displaying a different dimension of entrepreneurship (Figure 1):

**-** **_Entry density rate_ :** This is the indicator used to measure new firms (those that were registered in the current year) as a percentage of working age population (ages 18-65). The data collected for 101 countries shows significant disparities across regions, ranging from 0.05% in Africa & the Middle East to 0.58% in industrialized countries.

**-** **_Business density_ :** This is the indicator used to measure the number of businesses per capita.  It is calculated as the number of total businesses (those that existed at the beginning of the given year) as a percentage of working age population (age 18-65). Similar to the entry rate density, this indicator shows a remarkable gap between industrialized countries, with an average of 5.48%, and developing countries, with an average of 1.39%.

**-** **_Entry rate_ :** This is the indicator used to measure the number of new businesses as a proportion of existing businesses.  It is calculated as newly registered firms as a percentage of total registered firms.  The data collected for 100 countries shows that this indicator presents fewer disparities across regions, ranging from 6.6% to 10%.

6
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|---|---|
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44
# **Appendix Figure A1: Actual and Predicted External Finance Need of Exports, by Country and Time Period**

<!-- Start of picture text --> Algeria Argentina Australia<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>Austria Bahrain, Kingdom of Bangladesh<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>Belgium Bolivia Brazil<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>Burkina Faso Burundi Cameroon<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>Canada Central African Rep. Chile<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>China,P.R.: Mainland China,P.R.:Hong Kong Colombia<br>1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999 1969 1974 1979 1984 1989 1994 1999<br>20 25 25<br>20 20<br>0 15 15<br>40 40<br>40<br>20 20<br>20 0 0<br>40 40 30<br>20 25<br>20 0 20<br>25 100 30<br>20 20<br>15 0 10<br>40 40 40<br>30 20 20<br>20<br>0 0<br>40 60<br>30<br>30 40 25<br>20<br>20 20<br><!-- End of picture text --> Legend: ●: Actual EFNX; : Predicted EFNX 34
The percentage point contribution made by entry is actually lower in Russia and about the same in the Ukraine as in the U.S., while the percentage point contributions made by exit are higher than in the U.S.

Russian accession and separation rates from Brown and Earle (2003) are displayed in Figure 2. Worker flows are considerable during the Soviet period, and total worker flows rise further during the transition. Somewhat surprisingly, hiring has continued at a high rate despite the downsizing. Separations have risen more than hiring has fallen, suggesting that firms have adjusted employment more through increased separations than hiring freezes. It also means that total worker flows have been higher than they would have if the hiring rate had been the main adjustment tool.

Figure 3 shows the relationships between job and worker flows. During the late Soviet period, job flows made up a small proportion of worker flows. This continued to be the case for job creation until the economic recovery began in 1999. Job destruction, however, became a quite high proportion of separations, causing total job reallocation to be a larger fraction of worker flows. The job destruction fraction of separations approaches the levels found in Western countries (see Davis and Haltiwanger, 1999), while the job creation fraction of accessions remains less than half Western levels throughout the period. Nevertheless, the rise in the absolute level and the relative importance of job flows do suggest increased dynamism of Russian labor markets compared to their behavior under socialism.

An important set of questions concerns the determinants of job and worker flows. Table 3 summarizes the main results from Brown and Earle (2002, 2003) for Russia and Ukraine. Product market competition is associated with more excess job reallocation, especially when adjustment costs are low. Thus, more changes in market shares are occurring in competitive industries. Churning is also higher in such sectors, particularly when adjustment costs are low. This may be because of the larger number of alternative job opportunities for workers in competitive sectors. The higher churning could also be due to greater pressure to restructure the workforce.

Firms are slower to reduce employment when the labor market is dominated by a few employers. In Russia excess job reallocation is lower in such markets. This lack of labor market flexibility could be a symptom of extra political pressure to avoid unemployment in these regions. Surprisingly, workers are just as likely to quit in concentrated labor markets as in more dispersed ones, despite having fewer outside options.

20
Arvis, Jean-François, Gael Raballand, and Jean-François Marteau, 2007, “The Cost of Being Landlocked: Logistics Costs and Supply Chain Reliability”, Mimeo, The World Bank.

Baldwin, Richard E. and Daria Taglioni, 2006, “Gravity for Dummies and Dummies for Gravity Equations”, Working Paper No. 12516, NBER.

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De Groot, Henri L.F., Gert-Jan Linders, Piet Rietveld, and Uma Subramanian, 2004, “The Institutional Determinants of Bilateral Trade Flows”, _Kyklos_ , 57(1), 103-123.

Evans, Carolyn L., and James Harrigan, 2005, “Distance, Time, and Specialization: Lean Retailing in General Equilibrium”, _American Economic Review_ , 95(1), 292-313.

Fisman, Raymond, and Roberta Gatti, 2006, “Bargaining for Bribes: The Role of Institutions”, Discussion Paper No. 5712, CEPR.

Fisman, Raymond, and Shang-Jin Wei, 2004, “Tax Rates and Tax Evasion: Evidence from ‘Missing Imports’ in China”, _Journal of Political Economy_ , 112(2), 471-500.

> Francois, Joseph F., 2001, “Trade Policy Transparency and Investor Confidence”, _Review of International Economics_ , 9(2), 303-316.

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Francois, Joseph F., and William J. Martin, 2004, “Commercial Policy Variability, Bindings, and Market Access”, _European Economic Review_ , 48(3), 665-679.

Freund, Caroline L., and Diana Weinhold, 2004, “The Effect of the Internet on International Trade”, _Journal of International Economics_ , 62(1), 171-189.

Gatti, Roberta, 1999, “Corruption and Trade Tariffs, or a Case for Uniform Tariffs”, Policy Research Working Paper No. 2216, The World Bank.

Gatti, Roberta, 2004, “Explaining Corruption: Are Open Countries Less Corrupt?”, _Journal of International Development_ , 16(6), 851-861.

Greene, William, 2004, “The Behavior of the Maximum Likelihood Estimator of Limited Dependent Variable Models in the Presence of Fixed Effects”, _Journal of Econometrics_ , 7, 98-119.

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Helpman, Elhanan, Marc Melitz, and Yona Rubinstein, 2007, “Trading Partners and Trading Volumes”, Working Paper, <u>http://post.economics.harvard.edu/faculty/helpman/ papers/TradingPartners.pdf.</u> Hoekman, Bernard M., 2006, “Liberalizing Trade in Services: A Survey”, Policy Research Working Paper No. 4030, The World Bank.

32
-10-

performance and operational efficiency remained unaffected as majority ow,ership allowed government interference to continue.

17. Third, data on gross proceeds are becoming available.19 While these may be large in some cases (Mexico, Philippines, for example), the net financial gain for most governments can be small for several reasons: sale, to date, of the smaller, low-value enterprises; the assumption of the domestic and foreign debt, which often outweigh the market value of the assets, by the governments in most cases; the valuation of enterprises without clear methodology; and the use of proceeds to pay large amounts of severance, particularly where overemployment required significant retrenchment.

18. Factors leading to these divestiture outcomes are discussed below. As will be shown, policical will and country conditions are no doubt important,20 but practical issues related to the process of divestiture, the institutional capacity to execute the program in an effective and transparent manner, and the sequencing of divestiture with other reform areas are also critical.

# B. PRACTICAL ISSUES IN IMPLEMENTATION

# L. Formulation of a Strateev: A Sector-Wide vs Case-by-Case ADoroach

19. Sector-_ide Approach The sector-wide approach is used when rationalization of the size of the PE sector is the major divestiture objective. The majority of the operations under review -- such as in the 19 Some data on gross procoeds are becoming available. In Mexico. for instance, 136 transactions raised Mex$ 796.3 billion (US$ 765 million); and three large transactions in late 1988 generated an additional US$ 1.8 billion in gro!;s revenues. In Togo, ten privatizations yielded direct revenues to the governme1t of CFAF 230 million from the sale of shares and CFAF 1,339 million annually from the leases of privatized companies. The lease arrangements on some of Air Jamaica's equipment resulted in a net foreign cash flow improvement of US$4.58 million in FY87/88. In Guinea, the privatization of cement, beer and central oils alone raised approximately CFAF 8.2 million. And in the Philippines, the divestments to date have raised approximately Pesos 1.2 billion.

20 Broad country factors -- such as the nature of the political system and varying macroeconomic environments -- have no doubt been influential; and they have been dealt with in a number of other studies. See Simon Commander and Tony Killick, "Privatization in Developing Countries: A Survey of the Issues,' in Paul Cook and Colin Kirkpatrick, eds., Privatization in Less Developed Countries, New York, 1988; N. N. Susungi, The Caveats on Privatization as an Instrument of Scructural Adiustment in Africa, African Development Bank Research Paper, Cote d'Ivoire, 1988; Samuel Paul, Privatization and the Public Sector: Emerging Issues, CECPS, November 1987;
# 2. Data

The data set used is a survey conducted every year by the Portuguese Ministry of Employment, “Quadros do Pessoal”. It is a longitudinal data fi le matching fi rms and workers. The data is based on a questionnaire that every fi rm with wage-earners operating in Portugal is legally obliged to fi ll out<sup>7</sup> . Records are available at the fi rm and plant level, and have a detailed description of the labor force characteristics<sup>8</sup> .

Among other fi rm characteristics, the share of equity owned by non-residents is reported. Therefore, for every fi rm I am able to identify whether the foreign investment is (1) an acquisition of an existing domestic fi rm, (2) a fully owned subsidiary or (3) a minority or majority holding. I use a 10 percent threshold of foreign participation to classify a fi rm as foreign and also to identify ownership changes<sup>9</sup> . Evidence in panel 5 of Table A1 in the Appendix shows that the choice of a 10 percent threshold is not too restrictive. In manufacturing, more than half of the foreign acquisitions have the full control of the fi rm, while foreign fi rms prefer majority stakes. In non-manufacturing, the foreign participation, in both foreign and acquired fi rms, is preferentially one of full control. At the worker level, I use information on the education level, sex, age, tenure and hours worked. This information is then aggregated, by education groups, at the fi rm level. The three education groups used are: low (up to 6 years of schooling), medium (high school and technical courses) and high (bachelor and college degrees). Gross monthly wages are computed summing up monthly earnings as well as other regular and irregular payments. Hourly wages are gross wages divided by total monthly hours worked (including overtime). Firm average wages are computed excluding the extreme values for hourly wages<sup>10</sup> . Throughout the paper I use

> 7Public Administration is not included.

> 8The survey has information for an average of 180,199 fi rms and of 2,248,076 employees per year, during 1991-1998.

> 9Ownership is related to those who make decisions about resources, but the relevant concept for policy purposes is that of control. Measuring control is difficult and, even if a fi rm acquires more than 50% of the shares of another, it may choose not to exercise its controlling rights. On the other hand, even without majority an owner may have the effective control (McGuckin, 2001, discusses this issue).

> 10Workers with implausibly low earnings (hourly wage lower than 50% of the minimum wage) or implausibly high earnings (irregular payments -like dismissal payments- exceeding twice the other monthly wage components) were excluded from the sample. There were few of these cases.

6
**Table 1: List of variables and descriptive statistics**

|**Variable**|**Definition**||**Descri**|**ptive sta**|**t**|
|---|---|---|---|---|---|
|||All Indust|ries|Garmen|ts/ textiles|
|||Mean<br>|SD|Mean|SD|
|**Business characteristics:**||||||
|Log (exports)|Log of Exports in 1999 US Dollars|3.93|9.39|2.28|10.51|
|Log (employment)|Log of number of employees at the end of the fiscal year|3.76|1.80|3.97|1.84|
|Log (age)|Lgo of number of years since the establishment started operations|2.37|0.91|2.36|0.86|
|Foreign owned|Dummy=1 if the establishment has foreign share holders|0.13|0.34|0.08|0.28|
|**Market and supplier access variables:**||||||
|National supplier access|See text for definition|26.99|1.72|||
|Local supplier access|See text for definition|27.81|1.26|||
|Foreign market access|See text for definition|32.39|3.88|||
|**Geographic (trade cost) variables:**||||||
|International distance|Log km distance to port * national CIF to FOB ratio for 1995|2.42|5.36|||
|Area|Log of country area in square kms.|14.10|1.40|||
|Land locked|Dummy=1 if country is land locked|0.11||||
|**Lagged economic density variables:**||||||
|Country population (lagged)|Log of country population lagged 5 years|4.91|1.65|||
|Local GDP (1995)|Log (Country PPP GDP in 1990* Population share of city in 1995)|21.25|2.41|||
|Percent paved (road network)|Percentage of total road network that was paved by 1990|30.63|20.23|||
|**Institution and policy indicators:**||||||
|Openness to trade|Sachs-Warner index of openness 1965-1990|0.04|0.12|||
|Rule of law|Kaufmann et al rule of law index 1996-2002|-0.28|0.48|||
|Control of corruption|Kaufman et al control of corruption index, 1996-2002|-0.41|0.41|||
|**Sectors of industry:**||||||
|Food||0.10||||
|Textiles||0.13||||
|Garment||0.15||||
|Leather products||0.04||||
|Furniture||0.11||||
|Electronics and electrical equipment||0.11||||
|Pharmaceuticals and chemicals||0.11||||
|Autos and parts||0.05||||
|Machinery||0.03||||
|other||0.18||||

**Table 2: Sample Average Firm Level Exports by Country**

||Number of<br>establishments|Average firm level annual exports<br> <br> (logof 1999 US dollars)<br>|Proportion<br>that|
|---|---|---|---|
|||All firms<br>Exporters<br> <br>only|export|
|Bangladesh|1,001|7.43<br>18.54|0.40|
|Brazil|1,642|1.74<br>17.72|0.10|
|China|1,661|4.73<br>18.83|0.25|
|Cameroon|199|1.76<br>17.49|0.10|
|Cote d'Ivoire|230|2.23<br>16.73|0.12|
|Ethiopia|422|0.94<br>16.80|0.06|
|Ghana|318|1.94<br>16.55|0.12|
|India|3,015|3.66<br>17.59|0.21|
|Kenya|517|3.70<br>17.19|0.22|
|Malaysia|893|10.51<br>18.53|0.57|
|Morocco|859|6.17<br>18.18|0.34|
|Mozambique|153|1.35<br>16.26|0.08|
|Nigeria|232|0.42<br>16.30|0.03|
|Pakistan|965|3.49<br>17.95|0.19|
|Peru|576|0.35<br>10.49|0.03|
|Tanzania|428|2.16<br>16.83|0.13|
|Uganda|786|2.88<br>16.45|0.18|
|Zimbabwe|200|7.38<br>16.78|0.44|
|Total|14098|3.93<br>17.95|0.22|

28
# **4.3. Instrumental Variables Results, First Stage Results**

As shown in Table 5, the predicted household receipt of aid and predicted household receipt of aid

* shock variables obtained from the zero-stage estimation are strong instruments in the first stage. Since

the equation is exactly identified, no test of overidentifying restrictions is presented.

**Table 5: First Stage IV Results, Predicted Household Receipt of Aid**

||**Aid**|<br>**Aid * Shock**|
|---|---|---|
|Predicted Aid|1.124|***<br>0.029|
||(.079)|(.033)|
|Predicted Aid * Shock|0.311|***<br>1.145<br>***|
||(.087)|(.073)|
|# of Obs.|4412|1999|
|Centered R2|0.2451|0.372|
|Uncentered R2|0.340|0.392|
|Partial R2 of excluded instruments|0.075|0.202|
|F-test of excluded instruments|141.13|124.89|
|Weak identification F statistic||120.19|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses.

# **4.4. Instrumental Variables Results, Second Stage Results**

Table 6 below provides selected results for key co-variates across our six dependent variables.<sup>21</sup> The columns that are labeled as “1” include the results from the regressions with the dichotomous variable that identifies exposure to a shock in the current period (irrespective of potential exposure to past weather shocks). The columns that are labeled as “2” show the results from the regressions with the dichotomous variables that identify exposure to a shock (i) _only_ in the current period and (ii) in consecutive periods. We report the results for each variable using these two specifications, first for the IHPS sample and then for the FIAS sample.

> 21 Full results are found in Online Appendix 5.

28