rafmacalaba/datause-encoder-data huggingface dataset:
20

**Figure 17:  Evolution of Leasing and Factoring (1990-2003)**

<!-- Start of picture text --> Outstanding stock of Independent factoring cies Factoring and leasing<br>factoring and leasing Bank factoring clientele breakdown<br>Total factoring industry Mega<br>Leasing<br>700 3000 100% Large<br>90% Medium<br>600 2500 80%<br>70% Small<br>500<br>2000 60%<br>Micro<br>400 50%<br>1500 40%<br>300 30%<br>200 1000 20%<br>10%<br>100 500 0%<br>0 0<br>Sources: Asociacón Chilena de Leasing, Asociación Chilena de Empresas de Factoring<br>19901991199219931994199519961997199819992000200120022003<br>Factoring Leasing<br>Leasing, US$ millions Factoring, US$ million<br><!-- End of picture text --> As regard micro firms, bank penetration is relatively high. If one assumes that 80 percent of Chile’s 535,000 formal micro firms are bankable and 20 percent of its 550,000 informal micro firms, then there are about 538,000 bankable micro firms in Chile.8 This compares with about 270,000 micro firm clients of banks. Since micro firms usually have access to only one bank, it can be assumed that 270,000 micro firms are indeed bankarized, which represent 50 percent of bankable micro firms. This number is quite high compared to other countries in the region, although it is difficult to conduct precise comparisons for lack of data in other countries.

**Figure 18:  Bank Penetration with Micro Firms**

<!-- Start of picture text --> Banking system penetration with micro firms Cooperatives<br> Cono Sur<br>1,200,000 Banco del Estado<br> Bancefe<br>1,000,000 Banco del Desarrollo<br>800,000 Informal micro firms<br>Formal micro firms<br>600,000<br>400,000<br>200,000<br>0<br>Number of firms Bankable Bank clients<br>Source: SBIF, Banco del Desarrollo, Banco del Estado<br><!-- End of picture text --> 8 Being bankable meaning here having a sufficient profitability or sufficient profitability prospects to pretend accessing bank loans if they were awarded on a cash-flow basis.
67 Table 12

## Czech Republic Basic Indicators

||1989|1990|<br>1991|1992|<br>1993|1994|
|---|---|---|---|---|---|---|
|GDP (billion koruny/constant prices)<br>|505.7<br>|503.7<br>|432.1<br>|403.6<br>|402.3<br>||
|GDP (1989 = 100)|100|99.6|82.0|79.8|79.5||
|Industrial output|100|96.5|73.0|63.0|58.3||
|_In thousands_|||||||
|Population (end-year)|10300|10300|<br>10300|10300|10300|10300|
|<br>Labor force (end-year)||5626|5369|5184|5292|5121|
|<br>Employment (end-year)<br>||5586|5147|5049|5107|4957|
|Unemployment (end-year)|||||||
|<br>Persons||39379|221749|134788|185216|163877|
|Unemployment rate||0.7|4.1|2.6|3.5|3.2|
|Fiscal balance (% of GDP)|-2.4|0.1|-2.0|-0.5|1.4||
|<br>Net domestic publ. Debt (% of GDP)|||5.0|7.0|7.0||
|<br>Net external debt (% of GDP)||||17.6|17.8||
|Net external debt (billion US$)||3.5|5|4|2.7||
|Net debt-export ratio (%)<br>||50|61|36|19||
|Current account (million US$)<br>||-337.8|1142.7|52.9|580.0||
|FDI (million US$)||120.4|510.7|982.9|516.6||
|Portfolio investment (million US$)||0|-35.8|1062.3|||
|Rate of inflation (annual average) %|||56.7|11.1|20.8|10.0|
|Average wage inflation in industry %|||16.7|22.2|23.8||

_Source:_ EBRD, Econ. Com. for Europe (1994), own calculations.

Table 13

# **Stock of private firms with 0-5 dependent employees and -** - **unemployment rate in Czech Republic by region April 1994**

|Region|Frequency|Percent|Freq./reg. LF|u-rate(%)**|
|---|---|---|---|---|
|Other|378614|<br>51.55|<br>0.13|4.6|
|"Austria"|45963|6.26|<br>0.13|4.4|
|"EX-GDR"|63098|8.59|<br>0.1 3|4.3|
|"Bavaria"|24132|<br>3.29|<br>0.13|2.2|
|Prague|144200|<br>19.63|<br>0.19|<br>0.4|
|BPO*|78416|<br>10.68|<br>0.13|<br>3.3|
|Total|734423|<br>100.00|<br>0.14|<br>3.7|

*BPO = Brno, Plzen and Ostrava. **September 1994 Source: Own calculations.

"Austria" = regions in CR bordering at Austria; "Ex-GDR" = regions in CR bordering at Ex-GDR; "Bavaria" = regions in CR bordering at Bavaria.
than the PMG estimates for the following reasons: first, it deals with the issue of heterogeneity of the long-run parameters across countries in our real exchange rate equation. Second, even if the Hausman tests of the PMGE fail to reject the null of homogeneity, this result could be driven by very large standard deviations in some countries. We should also point out that although the measures of misalignment calculated using the time series and panel date cointegration techniques may go in the same direction (indeed, they are positively correlated –especially, among industrial countries), there may be some large quantitative differences. These differences may be attributed to the fact that, in fact, the regression may be a better fit for average countries rather than countries that deviate from this average.

# _Baseline Results_

Tables 19 through 24 present our Tobit analysis of RER undervaluations. The dependent variable measures the size of the undervaluation (in absolute value) whenever the actual rate weakens relative to the equilibrium real exchange rate by more than 5%. The baseline results in Table 19 (with RER misalignments calculated using the time-series Johansen cointegration estimates) show a negative and significant coefficient for the lagged level of RER misalignment. This implies that the degree of RER misalignment in the previous period would affect the extent of undervaluation in the current period. For instance, regression [1] in Table 19 implies that if the RER misalignment index deteriorates by 50% (ln(1/2)=-0.69) in period _t-1_ , the probability of affecting the level of RER undervaluation in period _t_ by 15% (=0.229 x -0.69).

Interestingly, either policy or outcome measures of financial openness fail to explain the magnitude of RER undervaluation. An analogous result is found for trade openness. Liability dollarization did not seem to matter either. In contrast, the central government budget balance has a negative and significant coefficient. This shows that fiscal policy may play a role in determining the extent of undervaluation in the exchange rate market. It also shows that fiscal discipline may reduce the size of the undervaluation.

Finally, the coefficient estimate of intervention in the FOREX market is not robust. While controlling for fiscal balance we find a statistically insignificant coefficient whereas it becomes positive and significant when we do not control for the 25
However, not all studies find agriculture to be the dominant force in poverty reduction. Controversially, the Indian case study by Besley, Burgess and Esteve-Volart (2006) argues on the basis of an enormously rich data set that agriculture has played a minimal role at best in India’s reduction of poverty, directly contradicting earlier findings by Ravallion and Datt (1996, 1998, 2002). These results are missing an important part of the historical story. The analysis by Smith and Urey (2002) of the relationship between agricultural growth and poverty reduction in India since 1950 shows very clearly the important investments and policy attention to reaching India’s rural poor through institutional and technical change in agriculture. This policy attention _before_ the Green Revolution established an environment in which the new technologies could have widespread impact on both the rural and urban poor.

The second pathway out of poverty in Figure 1 is productivity growth in the nonfarm sector. All four of the Asian country studies mentioned above as well as the Ravallion-Chen study of China, note that the rural non-farm economy has been (or in the case of Vietnam, could be) an important mechanism for connecting the poor to economic growth. The growing size and, in many countries, faster growth of the rural non-farm sector, implies that this may be a pathway out of poverty of growing importance in the future. However, this depends on the nature of the rural non-farm sector.

Lanjouw and Lanjouw (2001) argue that rural non-farm enterprises are likely to be pro-poor, as they tend to use factors of production at their real opportunity costs to the economy, so they are often labor-intensive in nature, reducing underemployment, help smooth income seasonally and bid up local wages. Additionally, small and medium enterprises generate more employment per unit of capital than big firms; in general, wages in the non-farm sector are higher than in agriculture, so the low productivity residual activities do not seem to dominate this sector. Furthermore, they often produce low quality goods that are consumed by the poor, which benefit by obtaining local goods at lower prices, rather than expensive imports. The non-farm sector seems to be mostly inequality reducing, as it might be the only possibility for low-skilled workers to increase their incomes and to acquire non-agricultural skills from on-the-job experience.<sup>4</sup>

> 4 See also the reviews of the rural non-farm sector in Bangladesh (World Bank, 2004b), Indonesia (World Bank, 2006a) and a multi-country synthesis (World Bank, 2006c).

8
# **2.4 Data on private and municipal preschool centers**

We use administrative data from _Censo Escolar_ on municipal and private preschool centers and their inputs for the years 2000 to 2006. _Censo Escolar_ is a compulsory yearly census conducted by the Ministry of Education, in conjunction with the state-level education departments. This data set gathers information on the universe of public and private preschool centers in Brazil. In each year, it comprises data on enrollment, group size, and teacher qualifications. In addition, it collects information on the infrastructure and equipment of each center, such as whether it has adequate sanitation for preschool, a playground, and a fridge. To ensure that the information is reported accurately, inspections are conducted every year on a random sample of centers. The administrative information from _Censo Escolar_ is widely regarded as highly reliable to monitor developments in the Brazilian public and private child care sectors.

Table 3 reports descriptive statistics on municipal and private preschool centers in different population intervals, using pooled data for the period 2000-2006. We observe that, in the set of municipalities considered, municipal supply accounts for about 88% of enrollment and 84% of the number of centers. We also observe that the proportion of enrollment accounted for by private providers tends to be higher in larger municipalities, reaching about 20% of the total in the largest population intervals. We further observe that participation in preschool among children aged 4 to 6 is at about 49%, of which 43 percentage points is accounted for by municipal enrollment.

It is also important to note that the _Censo Escolar_ data reveal that enrollment in preschool increased considerably over the sample period. In the municipalities that comprise the estimation sample, preschool enrollment rose by about 25%. This overall increase reflects an expansion of enrollment in both the municipal and private sectors (25% and 22.7%, respectively).

Table 4 reports summary statistics on the aforementioned quality indicators of municipal and private providers. We observe that private centers tend to have systematically higher quality indicators: group sizes are considerably smaller, the share of teachers with 10
<!-- Start of picture text --> Net Commitment by IEG Rating and FCS status<br>8<br>ge<br>£<br>E<br>Eo<br>orE:<br>=a<br>o<br>Unsat Sat<br>IEG Outcome Rating<br>HE Always+Partial FCS [I Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> Preparation Time by IEG Rating & FCS status<br>+<br>¢<br>—<br>oe<br>Ex<br>e<br>a2<br>§ =<br>oO<br>=°<br>S<br>o<br>Unsat Sat<br>IEG Outcome Rating<br>HE Always+Partial FCS [EE Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> Project Length by IEG Rating and FCS status<br>cy<br>2<br>5<br>go<br>2<br>Ss<br>2we<br>&<br>N<br>o<br>Unsat Sat<br>IEG Outcome Rating<br>HE Always+Partial FCS [I Never FCS<br><!-- End of picture text --> <!-- Start of picture text --> Preparation Costs by IEG Rating & FCS status<br>Oo3<br>2<br>we<br>=.<br>83<br>a*<br>a)<br>ag<br>§ So<br>28<br>Oo<br>=<br>o<br>Unsat Sat<br>IEG Outcome Rating<br>HE Always+Partial FCS [EE Never FCS<br><!-- End of picture text -->
level corruption’, _International Journal of Law, Crime and Justice_ , 43, pp. 68–87.

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Mahmalat, M. (2020) ‘Balancing Access to the State: How Lebanon’s System of Sectarian Governance Became too Costly to Sustain’, _Lebanese Center for Policy Studies_ , (49).

Mahmalat, M. and Atallah, S. (2019) ‘Recession without impact: why Lebanese elites delay reform’, _Economic Research Forum_ , 8 October. Available at: https://theforum.erf.org.eg/2019/10/07/recession-without-impact-lebanese-elites-delay-reform/.

Mahmalat, M., Atallah, S. and Maktabi, W. (2021) _Public infrastructure procurement in post-conflict power-sharing arrangements: Evidence from Lebanon’s Council for Development and Reconstruction_ , _International Growth Centre_ . F-20016-LBN-1. Available at: https://www.theigc.org/wp-content/uploads/2021/02/Mahmalat-et-al-2021-Final-Report.pdf.

Mahmalat, M. and Zoughaib, S. (2021) ‘Breaking the Mold? Ministerial rotations, legislative production, and political strategies in Lebanon’, _Governance_ , (forthcoming).

Marquette, H. and Peiffer, C. (2018) ‘Grappling with the “real politics” of systemic corruption: Theoretical debates versus “real-world” functions’, _Governance_ , 31(3), pp. 499–514.

Della Porta, D. and Vannucci, A. (2012) _The hidden order of corruption: An institutional approach_ . Surrey: Ashgate.

Ravenda, D. _et al._ (2020) ‘The effects of mafia infiltration on public procurement performance’, _European Journal of Political Economy_ , 64.

Rijkers, B. _et al._ (2014) ‘Which firms create the most jobs in developing countries? Evidence from Tunisia’, _Labour Economics_ , 31, pp. 840–102.

Rijkers, B., Baghdadi, L. and Raballand, G. (2017) ‘Political Connections and Tariff Evasion - Evidence from Tunisia’, _World Bank Economic Review_ , 31(2), pp. 459–482.

Rizkallah, A. (2017) ‘The paradox of power-sharing: stability and fragility in postwar Lebanon’, _Ethnic and Racial Studies_ . Taylor & Francis, 40(12), pp. 2058–2076.

Salloukh, B. F. (2019) ‘Taif and the Lebanese State: The Political Economy of a Very Sectarian Public Sector’, _Nationalism and Ethnic Politics_ . Routledge, 25(1), pp. 43–60.

Sberna, S. (2014) _Conceptualizing Organized Crime: A transaction cost approach to make-or-buy decision and corruption_ . Florence.

23
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<!-- Start of picture text --> q<br>dy<br>Kilometers . ‘ Key<br>; aa<br>.<br>| a Rent relative to Auckland<br>q = 0.55<br>DO 055-070<br>DO 071-085<br>AA Major RSE regions.<br>Kilometers % who moved from Auckland<br>; BP: i 0en100 ee 200 mp—P 2%10%<br><!-- End of picture text -->
<!-- Start of picture text --> Fihure 7: Paved Roads<br>(% of total roads)<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>N/A<br>0<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text -->

# **REFLECTIONS ON REGIONAL INTEGRATION AND REGIONAL INFRASTRUCTURE POLICY**

**The above synthesis shows (and individual country case studies strongly suggest) that there are significant infrastructure shortfalls in SACU countries in several major indicators of** **_access_ to basic infrastructure services (electricity, water & sanitation, ICT, and transport).** Performance in Swaziland and Lesotho lags particularly behind their comparator group of countries. But even in South Africa, despite major gains in recent years in expanding access to electricity to the previously unserviced areas, further extension of access to poor areas is very much needed.

**But how SACU countries decide to go about meeting these comparative shortfalls in infrastructure performance (especially access)––within purely national or wider, regional frameworks––matters for the success of their infrastructure scale-up efforts.** At one extreme, each country could seek to meet this challenge from the narrow view point of its own “national” interest that may include elements of energy security, self-reliance, etc. But this approach may be costly, not only from the point of view of the smaller SACU members but also for South Africa that is increasingly looking to expand its economic, trade, and transportation links into the rest of the broader African continent. At the other extreme, therefore, the SACU countries may be seen as an integrated economic, not only customs, space featuring potentially lower trade, transportation and transaction costs within the region and also vis-à-vis other neighboring countries with significant geographic links (e.g., Mozambique).

**Moreover, there is evidence of an increasingly tight supply-demand balance for basic infrastructure services in the SACU region (e.g., electricity, telecom, and transport and water and sanitation) so that infrastructure is becoming a binding constraint for growth.** In South Africa, for example, recent forecasts of demand for electricity and telecom and associated investment needs in an accelerated growth scenario suggest annual requirements of the order of $5 billion in just these two sectors. In Botswana (World Bank and BIDPA 2005c), Lesotho (World Bank 2005b) and Swaziland (World Bank 2006), in particular, there is also a growing recognition of infrastructure deficiencies and their potential impact on growth.

**The basic geographic and economic structure of SACU––with one very large economy (South Africa) and four small states––suggests considerable benefits from greater**

14
characteristics and no clear comparison group, such as natural gas pipelines and commercial banks, or industries with rapid technological change over the study period, such as wired and wireless telecommunications. There are case studies of interventions in some of these industries (Casta ̃neda Sabido and Elbittar, 2013; Castellanos, Del Angel<sup> ́</sup> and Garza-Garc ́ıa, 2016).

We cover a greater subset of investigated industries than several previous studies of antitrust enforcement, which faced the similar challenge of matching relevant markets to plausibly similar controls. In his analysis of the effects of United States antitrust indictments, Sproul (1993) identifies relevant market control prices for 25 of “roughly 400” price-fixing indictments under the Sherman Act. In his analysis of United States antitrust decisions regarding horizontal mergers, Kwoka (2014) identifies 49 transactions for which rigorous retrospective studies of their effects are available. These transactions span the period 19762009, and during the period 1996-2011 alone, the FTC investigated 264 horizontal mergers.

**The Economic Census** Having defined treatment and control industries in each case, we construct a series of economic variables for these industries using the establishmentlevel micro-data of the quinquennial Economic Censuses covering the calendar years 1993 to 2018. These censuses include every establishment in Mexico in every sector except agriculture, forestry, and hunting, and survey all physical establishments, including sole proprietorships and firms with so-called informal workers that do not contribute to the national social security scheme. The censuses are carried out by Mexico’s national statistics body INEGI ( _Instituto Nacional de Estad ́ıstica, Geograf ́ıa e Inform ́atica_ ), which provided us with access to the establishment-level microdata that we use to construct key variables: sales, employment, wages, operating profit margin, and total factor productivity (TFP). Sales and employment are measured as totals for each industry and year. Wages are measured as total remuneration to employees divided by total employment for each industry and year. The operating profit margin is measured as value-added minus total remuneration, divided by sales for each industry and year. TFP is estimated for each establishment using an estimate of the production function, as described in Section 5.

# **3 Empirical Design and Case Examples**

In this section, we describe the difference-in-differences estimator used to measure outcome changes associated with each antitrust case, and then report the difference-in-differences estimator graphically for two example cases to illustrate the hypotheses that arise.

13
complexities of the tax system since it is based on the actual source of finance and asset structure of individual firms and accounts for the fact that some firms cannot always make use of all their allowances against tax (Chennels and Griffith, 1997), thus providing a measurement for actual overall tax burden of Croatian firms. Although the accounting-based ETR is firm-specific, it does reflect the tax burden changes across the entire corporate sector since the aggregate ETR approximates the statutory tax rate. As shown in Figure 1, the ETR –on average– approximates the statutory tax rate (35% and 20% before and after the reform, respectively) and small variations can be attributed to a variety of tax deductions/credits pertinent to specific incentives, accelerated depreciation, tax loss carry provisions, and tax penalties imposed by the tax authority, among other reasons.

With respect to the empirical methodology, we apply the following panel-data specification with firm fixed-effects: effects, _X_ is a vector of control variables, _ETR_ is the accounting-based effective tax rate, and ε denotes the residuals.<sup>13</sup> Regarding control variables, we utilize a series of variables that have been found previously to influence financing decisions (i.e., non-tax explanations of capital structure). Firm size, in terms of the _natural logarithm of total sales_ , captures the differences in informational costs and ex ante costs of financial distress (Bancel and Mittoo, 2004; Graham, 2000; Klapper et al., 2002). The ratio of _‘fixed assets to total assets’_ influences the cost of debt due to collateral considerations since banks tackle moral hazard and adverse selection by seeking collateral (Graham, 2000; Stiglitz and Weiss, 1981), especially in transition countries where banks

> 13 We interchangeably utilize long-term debt and equity as dependent variables, since Amadeus does not clearly indicate short-term debt (i.e., variable ‘ _Loans’_ under the category ‘ _Current liabilities’_ uniformly contains missing observations) for Croatian firms. Nevertheless, these two dependent variables are sufficient to identify an effect of taxation reform on capital structure, since long-term debt is highly correlated with short-term debt, and the level of equity reflects nondebt liabilities with tax-relief capacity. Notably, in the case of the Croatian private firms included in the BvD Amadeus dataset, “ _Non-current liabilities_ ” with “ _Long-term debt_ ” could be considered equivalent, since the observations for “ _Other non-current liabilities_ ” appear either zero or missing.

7