rafmacalaba/datause-encoder-data huggingface dataset:
translog gross output production function specification allows for firm-time variation in the estimated output elasticities through differences in input use.

Due to restrictions in the data set, we restrict the markup and productivity analysis to firms in the manufacturing industries. because the proxy variable for materials (cost of sales) is missing for a sizable number of non-manufacturing firms in the sample, particularly those in the services sectors such as financial and insurance activities, real estate activities, and professional activities like legal and accounting services.<sup>7</sup>

# 2.2. Data

We use the SARS-NT firm-panel data set based on tax administrative data. This firm-level panel data are created using the South African Corporate Income Tax (CIT) information sourced from SARS. Although data are available for the years 2008-2017, we start our sample from 2010 given that there is a considerable proportion of firms with missing sales revenue information in 2008 and 2009 relative to the other years - a key variable underpinning this analysis. We have therefore conducted our analysis using data for the years 2010-2017. However, due to observed issues with some variables (including sales and employment) for 2015, we have chosen to present analysis for 2010-2014 as our primary set of results. Nevertheless, it is worth noting that estimated effects using data for 2010-2017 are in line with those using a sample up to 2014, albeit of different magnitude. Finally, since the data are derived from tax administrative data, only formal firms are included in the data set and the results that follow must be read in this context.

In order to improve our estimated competition indicators, in treating the data, we carefully considered a number of issues, considering principles of Industrial Organization. As an example, we made important decisions with respect to missing values of the core variables of interest and unreliable wage data in some years for some firms. In addition, we selected variables for the analysis based on how well they capture the underlying microeconomic concepts required to gain an accurate estimation of competition dynamics. For instance, we selected a wage measure that

> 7 In addition, there are various conceptual and empirical problems relating to the measurement of output, prices, and productivity in the non-manufacturing sectors (see https://www.brookings.edu/research/productivity-in-the-services- <u>sector/).</u>

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Poisson fixed effects estimator, which does not involve the inclusion of a large number of dummy variables to account for the time-varying country-pair specific effects; the fixed effects are conditioned out from the model estimation and are therefore not treated as parameters to be estimated. Standard errors are clustered at the country-pair level to deal with potential correlation of errors over time and across sectors.

Of the observations in our sample, 85% take the value of zero.<sup>47</sup> They correspond to cases where no crossborder investment between two countries in a given sector is recorded in the FDI database that we use.<sup>48</sup> Despite this large proportion of zeros in our sample, there is no reason to believe that zero and non-zero values come from different data generating processes. When we estimate a simply gravity equation, using as the dependent variable the number of projects and including a small number of explanatory variables (GDP and GDP per capita on both source and destination sides, bilateral distance, common language dummy variable, shared colonial link dummy variable), we find that average predicted probabilities of zeros and positive values are very similar to the corresponding proportions observed in the sample with all zero observations. Santos Silva and Tenreyro (2011) show that the Poisson quasi-maximum likelihood estimator (QMLE) is well behaved even in the presence of a large number of zeros in the sample.

Other methods have been suggested in the literature to deal with zero values, e.g. ln(FDI+constant) by OLS or different variants of the Tobit model. However, the Monte-Carlo simulations of Santos Silva and Tenreyro (2006) and Head and Mayer (2013) indicate that all these alternative estimators perform poorly in presence of heteroskedasticity, which is present in our data. The main reason is that log-linearization of multiplicative models induces a correlation between the transformed error term and the explanatory variables. On the other hand, the Poisson QMLE is robust to various patterns of heteroskedasticity.<sup>49</sup> We also use a Poisson fixed effects estimator to model the extensive and intensive margins of FDI.<sup>50</sup> It is

> 47The reliance on sectoral variation in FDI within country-pair-year groups to identify the coefficients of interest implies that groups for which no FDI takes place in any sector are dropped from the sample. The dropped zero observations contain no information for estimating the parameters.

> 48A large prevalence of zero values is not unusual in data on international transactions. Helpman et al. (2008) report that more than 50% of the observations in their country-level trade dataset are zeros. This percentage is naturally higher at the sector-level. In the CEPII Trade and Production database, which covers 26 manufacturing sectors, 75% of the observations are zeros during the period 2003-2006.

> 49In addition, their simulations suggest that the Poisson QMLE does not suffer from an incidental parameters problem.

> 50We are fortunate to know the number and size of the FDI projects. In the context of international trade, some studies without

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(2002) also analyze the determinants of differences in Internet usage across countries and use their results to provide some policy recommendations for the Latin America region. Some other papers have focused on Internet diffusion. For example, Chong and Micco (2003) study the spread of Internet in Latin America and argue that in spite of being latecomers, Latin American countries have the advantage of lower costs of adoption and could easily catch up with technological leaders. They also find that a country’s capacity to innovate helps explain the extent to which Internet is adopted. In a similar framework, Beilock and Dimitrova (2003) find that per capita income is one of the most important factors behind these differences. Their results also suggest that this effect is non-linear, with income differences having a larger effect in the use of Internet at lower income levels. Guillén and Suárez (2005) focus on the effect of economic, political and sociological factors on Internet usage.

The study of how the diffusion of the Internet has affected various markets has received substantial academic attention (a few examples include Goolsbee 2000 examining how taxes affect online versus offline channel selection, Goolsbee 2002 examining how the Internet can make markets more competitive, Brynjolfson and Smith 2003 examining how e-commerce can generate sales distributions with long tails, Zentner 2006 examining how piracy reduces the sales of recorded music, and Liebowitz and Zentner 2012 examining how Internet use affects the consumption of television). In this literature some studies have examined how digital content has affected the demand for printed content, which is more related to our study because printed content readership affects paper consumption. For example, Gentzkow (2007) estimated the substitution of print and online papers using micro data from Washington, DC. Similarly, Filistrucchi (2005) estimated the relationship between online newspapers and print papers using data from the main national daily newspapers in Italy, documenting that newspapers reduce the sales of print papers when they begin offering their content online. Simon and Kadiyali (2007) examined how offering digital content affects the demand for print magazines, finding that the provision of digital content cannibalizes the sales of print magazines.

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Figure A1: Countries doing fiscal adjustment <!-- Start of picture text --> Number of countries doing fiscal adjustment<br>Advanced economies EMDEs<br>1990 2000 2010 2020 1990 2000 2010 2020<br>Year Year<br>Primary gap Debt/revenue Primary gap Debt/revenue<br>20 30<br>15<br>20<br>10<br>10<br>5<br>0 0<br><!-- End of picture text -->

- 46 -
# **3.3 Evaluation**

# **3.3.1 Is the Size of Employment in the Public Sector too Large?**

The public sector in Ghana consists of two distinct types: government services; and public commercial enterprises. Leechor has tried to put together the available data on employment in the different types of public employment in 1987 and 1992. These data are reproduced in Table 3.2 below.

Table 3.2: Number of Public Sector Employees, 1987 **and 1992**

|Group|1987|1992|
|---|---|---|
|Core civil services'|131,089|102,173|
|Education services|_159,000_|167,370|
|Subvented organizationsb&c|81,574|69,574|
|Security organiationsd|29,000|29,000|
|Government services|400,663|368,117|
|Net retrenchment, 1987 to 1992|--|--|
|Public enterprisese|250,000|227,000|
|Net retrenchment, 1987 to 1992|--|--|
|Total public sector|650,663|595,117|
|Net retrenchment, 1987 to 1992|--||

-- Not applicable

- a. Between 1987 and 1972 a few organizations were added to the civil service, including the highway authority and the audit service.

- b. Some employees of Subvented organizations were transferred to the core civil service between the two periods.

- c. Includes estimates of agencies not covered inn the controller and accountant general's payroll, such as the internal revenue service and customs services.

- d. Includes the police, arned forces, and civil defense organizations

- e. Data are derived from partial coverage of public enterprises.

_Source: Government of Ghana State Enterprise Commission (1992); Adaptedfirom Leechor (1994)._ The statistics show retrenchment of the order of 10 per cent during these five years of adjustment. But considerable doubt exists about the large size of the sector in Ghana's economy.

**Public Civil Services:** Leechor has pointed out that in spite of several rounds of retrenchment Ghana's civil service remains one of the largest in Africa relative to the

11
Tables A.4 and A.5 report, without comment, the analogous benchmark results for Tables 4 and 5, but with the aggregate growth polarity measure computed with migration flows (and using real GDP growth rates). By and large, the results presented in these two tables are qualitatively similar to the measure using real growth rates.

Table A.4: Benchmark regressions for proximate correlates of growth polarity (with migration), 1968–2008, with GDP growth rates<sup>*</sup>

||**BP1**|**BP2**|**BP3**|**BP4**|**BP5**|**BP6**|
|---|---|---|---|---|---|---|
|Population|-2.246<br>|-0.233<br>|-4.173<br>|2.958<br>|1.465<br>|-0.917<br>|
|growth|(0.75)<sup>_∗∗∗_</sup>|(0.54)|(1.68)<sup>_∗∗_</sup>|(1.00)<sup>_∗∗∗_</sup>|(0.88)<sup>_∗_</sup>|(1.25)|
|Investment|0.061|-0.050|-0.001|0.047|0.091|0.269|
||(0.12)|(0.11)|(0.14)|(0.13)|(0.10)|(0.19)|
|Enrollment|-0.003|-0.039|0.004|-0.060|-0.028|-0.043|
||(0.02)|(0.04)|(0.05)|(0.03)<sup>_∗∗_</sup>|(0.04)|(0.07)|
|Income per||||0.040***|0.052|0.093|
|capita||||(0.01)|(0.02)<sup>_∗∗∗_</sup>|(0.03)<sup>_∗∗∗_</sup>|
|Life||||-0.082|-0.212|-0.197|
|expectancy||||(0.07)|(0.10)<sup>_∗∗_</sup>|(0.29)|
|Dependency||||-0.287|-0.224|0.017|
|ratio||||(0.07)<sup>_∗∗∗_</sup>|(0.09)<sup>_∗∗_</sup>|(0.16)|
|Government||||-0.629|-0.820|-1.154|
|size||||(0.19)<sup>_∗∗∗_</sup>|(0.24)<sup>_∗∗∗_</sup>|(0.40)<sup>_∗∗∗_</sup>|
|Adjusted _R_<sup>2</sup>|0.051|||0.179|||
|_F_|||2.602<sup>_∗∗∗_</sup>|||3.233<sup>_∗∗∗_</sup>|
|Hansen _J_|||74.043|||57.330|
|AR(2)|||-0.626|||-0.527|
|Estimator|OLS|RE|S-GMM|OLS|RE|S-GMM|
|Instruments|||74|||74|
|_N_|673|673|673|628|628|628|

> * Growth rates calculated from GDP data in constant 2005 international purchasing power parity-adjusted dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are five-year averages. All variables are in log form. Heteroskedasticity and autocorrelation-robust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

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The method applied in this paper is quantile regression. The idea is that one can choose any quantile and thus obtain many different parameter estimates. In this manner the entire conditional distribution can be explored. By testing whether coefficients for a given variable across different quantiles are significantly different, one implicitly also tests for conditional heteroskedasticity across the wage distribution.

The method has many other virtues apart from being robust to heteroskedasticity. When the error term is non-normal, for instance, quantile regression estimators may be more efficient than least squares estimators.<sup>6</sup> Furthermore, since the quantile regression objective function is a weighted sum of absolute deviations, one obtains a robust measure of location and, as a consequence, the estimated coefficient vector is not sensitive to outlier observations on the dependent variable.<sup>7</sup> The main advantage, though, is the semi-parametric nature of the approach, which relaxes the restrictions on the parameters to be fixed across the entire distribution. Intuitively, quantile regression estimates convey information on wage differentials arising from nonobservable characteristics among individuals otherwise observationally equivalent. In other words, by using quantile regressions, we can determine if individuals that rank in different positions in the conditional distribution (i.e., individuals that have higher or lower wages than predicted by observable characteristics) receive different premiums to education, tenure, or to other relevant observable variables.

Labor market studies usually make use of conditional mean regression estimators, such as Ordinary Least Squares<sup>8</sup> . This technique is subject to criticism because of several, usually heroic assumptions underlying the approach. One is the assumption of heteroskedasticity in the distribution of the error terms. If the sample is not completely homogenous, this approach, by

> 6 Ibid.

> 7 That is, if _y i_ − _x i_ ′β ˆ θ > 0 , then yi can be increased toward + ∞, or if _y i_ − _x i_ ′β ˆ θ < 0 , yi can be decreased toward -∞, without altering the solution β<sup>ˆ</sup> θ .  In other words, it is not the magnitude of the dependent variable that matters but on which side of the estimated hyperplane the observation is.  This is most easily seen by considering the FOC to

> This can be seen both as a strength and a weakness of the method.  In the present context, with data from Côte d’Ivoire, the advantages seem to outweigh the disadvantages, since the reliability of data from LDCs generally is questionable.  However, to the extent that a given outlier represents a feature of the "true” distribution of the population, one would prefer the estimator to be sensitive to such an outlier—at least to a certain degree. 8 See Velenchik (1997) and Verner (1999a,b).

5
# **6. Additional Heterogeneity in Firm Behavior**

To provide additional information on the performance of issuing and non-issuing firms along the FSD, we split these firms by (1) type of securities issued (equity versus bonds), (2) issuing market (domestic versus foreign), and (3) the level of development of the firms’ home market (developed versus emerging countries, the former divided in bank-based and market-based countries).<sup>18</sup> In unreported tests, we compared developed versus emerging countries and bank-based versus marketbased countries (pooling developed and emerging countries) and obtained qualitatively similar conclusions. We provide results based only on assets, but the findings are robust to using sales or the number of employees. We do not report all these results to reduce the number of tables.

The results show that bond issuers are much larger than equity issuers at all deciles of the FSD (Table 7, Panels A and B). For example, equity issuers at the 1<sup>st</sup> decile of the distribution were 17% larger than non-issuers in 2003, whereas bond issuers were 1,391% larger. Moreover, equity issuing firms grew relatively faster than bond issuing firms between 2003 and 2010. For example, after taking into account the initial differences in size between issuers and non-issuers, equity issuers at the 5<sup>th</sup> decile of the distribution grew 73% more than non-issuers, whereas bond issuers at the same decile of the FSD had an expansion in assets of 41% more than non-issuers. These differences are statistically significant according to unreported tests. In sum, firms that issue equity are smaller than bond issuers and tend to experience faster growth. These results are consistent with the view that high-growth firms use relatively more equity financing to cover their funding needs (i.e., Stulz, 1990; McConnell and Servaes, 1995; Hovakimian et al., 2001, 2004; Gatchev et al., 2009).

The core findings that issuers are larger and grow faster than non-issuers hold across markets

> 18 The control group of non-issuing firms is the same in all these different cases, namely, the firms that did not issue either equity or bonds between 2003 and 2011. As in the previous section, we focus the analysis in this section on the 2003-2010 sample due to data availability on firm-level balance sheets in Orbis. Hence, the issuing firms with capital raising activity only in 2011 are excluded from the sample.

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<!-- Start of picture text --> Reform in Laqqed OB Indicator and Change in ES S0th-10th spread<br>MDA_5_6<br>Te<br>=<br>on<br>Cc<br>a=)—a -"<br>oa<br>°,<br>ti Ls ALB_2 2 Ecu_i_3<br>eo<br>i<br>ao<br>uo<br>Cc<br>o<br>2 NIC_2_3 SLV_41_3<br>oo<br>°,<br>cA<br>a ZAR2 4<br>co<br>o DE Gplicenseo t0/DB_OpLicense_tt100 150<br>n==20<br>Labels are: ‘country code’ ‘years since OB reform’ ‘years between ES surveys’<br><!-- End of picture text -->
Table E1: Share of Significant Rainfall Coefficients in Ethiopia

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.58|0.67|0.67|0.50|0.83|0.50|
|Median Daily Rainfall|0.08|0.83|0.58|0.25|0.42|0.50|
|Variance of Daily Rainfall|0.58|0.42|0.58|0.50|0.67|0.42|
|Skew of Daily Rainfall|0.58|0.50|0.58|0.75|0.17|0.25|
|Total Rainfall|0.58|0.67|0.67|0.50|0.83|0.50|
|Deviation in Total Rainfall|0.25|0.58|0.83|0.00|0.75|0.17|
|Z-Score of Total Rainfall|0.17|0.42|0.50|0.17|0.92|0.25|
|Rainy Days|0.75|0.67|0.25|0.83|0.83|0.92|
|Deviation in Rainy Days|0.25|0.58|0.17|0.42|0.50|0.83|
|No Rain Days|0.75|0.33|0.33|0.83|0.50|0.75|
|Deviation in No Rain Days|0.25|0.58|0.17|0.42|0.50|0.83|
|% Rainy Days|0.75|0.67|0.25|0.83|0.83|0.92|
|Deviation in % Rainy Days|0.25|0.58|0.17|0.42|0.50|0.83|
|Longest Dry Spell|0.33|0.67|0.42|0.50|0.42|0.25|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Ethiopia.

Table E2: Share of Significant Rainfall Coefficients in Malawi

||CHIRPS|CPC|MERRA-2|ARC2|ERA5|TAMSAT|
|---|---|---|---|---|---|---|
|Mean Daily Rainfall|0.75|0.67|0.33|0.67|0.67|0.75|
|Median Daily Rainfall|0.17|0.42|0.92|0.42|0.50|0.58|
|Variance of Daily Rainfall|0.33|0.33|0.42|0.17|0.25|0.33|
|Skew of Daily Rainfall|0.92|1.00|0.42|0.67|0.08|0.58|
|Total Rainfall|0.75|0.67|0.42|0.67|0.67|0.75|
|Deviation in Total Rainfall|0.17|0.50|0.33|0.25|0.67|0.33|
|Z-Score of Total Rainfall|0.25|0.42|0.33|0.25|0.33|0.17|
|Rainy Days|0.67|0.58|0.42|0.50|0.42|0.50|
|Deviation in Rainy Days|0.75|0.50|0.67|0.33|0.58|0.25|
|No Rain Days|0.67|0.58|0.42|0.50|0.50|0.50|
|Deviation in No Rain Days|0.75|0.42|0.67|0.33|0.58|0.25|
|% Rainy Days|0.67|0.58|0.42|0.50|0.42|0.50|
|Deviation in % Rainy Days|0.75|0.42|0.67|0.25|0.58|0.25|
|Longest Dry Spell|0.92|0.58|0.33|0.58|0.58|0.25|
|Observations|168|168|168|168|168|168|

_Note:_ The table presents the share of statistically significant rainfall coefficients in Malawi.

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<!-- Start of picture text --> Total External Debt (Sovereign and Private Sector) to FX reserves<br>4.00<br>——Dom. Debt Welchted Average<br>cs<br>cally<br>2.0<br>7.7m<br>1.50<br>1.00<br>050<br>6.00<br>7001 2007 2008 7001 200% 7006 2007 2008 7009<br>Source: JP Morgan EM Debt and Fiscal Indicators (2009)<br><!-- End of picture text -->
I do not go into detail here as to how the curve was derived or calibrated. Suffice to say that it comes from a model of (corporate) default due to Robert Merton but the curve can also be thought of as “fitting” portfolio models of default risk including Creditmetrics or CreditRisk+<sup>33</sup> . The curve then contains an assumption regarding the covariances within a credit portfolio. Recent papers that show how such a function for each individual claim may approximate the results of a portfolio model include Gordy (2003) and Emmer and Tasche (2003). Basel II IRB calibration is the subject of Section 5 below.

An IRB approach may well be useful for developing countries that lack deep capital markets and deep penetration by external ratings agencies. The issue will be whether the approach as detailed can be calibrated, implemented and monitored effectively in developing countries.

# **2.4.4 Operational Risk**

Apart from different approaches for underlying credit risk measurement, Basel II also includes a specific capital requirement for Operational Risk. As Basel I rested on a minimum capital charge of 8% for most claims and Basel II’s basic requirement is also 8%, at first sight this appears as an additional requirement that will increase banks’ capital requirements overall. However, under the Standardardized Approach only non-rated loans, poorly rated loans or loans in default attract capital requirements of 8% (or higher) and in general loans will receive a lower credit risk capital charge under Basel II than Basel I. Under IRB all loans with a lower probability of default that the “Standard loan” will have lower capital requirements. However, for countries that apply the Simplified Standardized Approach or the Standardized approach but where credit rating penetration is very low, then in those cases the Operational Risk charge, on top of essentially an unchanged 8% requirement, may well imply a <u>significant increase in capital requirements</u><sup>34</sup> <u>.</u>

> 33 CreditMetrics is a model developed by JP Morgan offshoot, Riskmetrics and Creditrisk+ a model developed by Credit Suisse Financial Products of CSFB.

> 34 This is supported by the results of the Basel Committee’s Quantitative Impact Study Number 3 (QIS 3 – see BCBS (2003c) and BCBS (2003d).  The first publication finds a 12 % increase in capital requirements for “other” (non G13, non EU) countries for the standardized approach.  Indeed credit risk capital requirements <u>rise</u> by 2% and operational risk capital requirements are about 11% of the current capital charge.  Curiously, QIS 3 also finds an 11% inccrease for large banks from G13 (and yet only a 6% increase for Eu large banks implying that this comes from non EU G13 countries), but then these banks will most likely not apply the standardized approach and will most likely apply IRB.  In BCBS (2003d), which apparently takes into account certain modfications of the new proposals embodied in Consultative Paper 3, no information is given on banks from “other countries’.

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