rafmacalaba/datause-encoder-data huggingface dataset:
perpetrators. Thus corruption by public officials may also have an adverse externality on the use of the formal financial system by innovating firms, with potentially additional implications for revenue collection and development.

To summarize, we explore four main questions in our empirical analysis below:

1. Do innovating firms pay more bribes than non-innovators?

2. Do firms that pay bribes also evade more taxes?

3. Considering the net burden of corruption, are innovating firms more likely to be victims who pay bribes but do not underreport revenue to tax authorities or are they perpetrators who don’t pay bribes but underreport revenue?

4. Are bank financed firms more likely to be victims than perpetrators?

# **3. Data and Empirical Methodology**

We use the World Bank Enterprise Surveys (ES) that use standardized survey instruments to benchmark the investment climate of individual economies across the world and to analyze firm behavior and performance. The surveys sample from the universe of registered businesses in each country using standardized survey instruments and follow a stratified random sampling methodology.<sup>13</sup> All the surveys in our sample were administered during 2002-2005.

The ES surveys have two unique advantages that make them suitable for investigating the relation between innovation, corruption, and tax underreporting. First, the surveys contain information on both types of illegal activities – bribe payments by firms to public officials as well as the share of income not reported for tax purposes by the firms. The information on bribe payments helps us understand the extent to which firms are victimized and the information on tax avoidance helps us explore the role of firms as perpetrators. We focus on the variables used to measure bribe payments and tax evasion in the following sub-section.

> 13 The ES surveys and their precursor, the World Business Environment Survey (WBES) have  been used to investigate a series of questions in developmental economics including the relation between property rights and contracting institutions (e.g. Acemoglu and Johnson, 2005),  investment climate and business environment obstacles to growth (e.g. Beck et al., 2005; Ayyagari et al., 2008), firm financing patterns (e.g. Beck et al., 2008; Cull and Xu, 2005, Ayyagari et al., 2009) and dispute resolution via courts (e.g. Djankov et al., 2003).

10
Bank Branch Deregulation.” _The Quarterly Journal of Economics 111_ (3), 639–70.

- Kindelberger, C. P. 1978. _Manias, Panics and Crashes_ . New York: Basic Books.

- King, R. G., and R. Levine. 1993. “Finance, Entrepreneurship and Growth: Schumpeter Might be Right.” _Quarterly Journal of Economics 108_ , 717–37.

- Kneer, C. 2013. “Finance as a Magnet for the Best and Brightest: Implications for the Real Economy.” Working Paper 392, De Nederlandsche Bank.

- Koetter, M., and M. Wedow. 2010. “Finance and Growth in a Bank-Based Economy: Is it Quantity or Quality that Matters?” _Journal of International Money and Finance 29_ (8), 1529–45.

Koop, G. 2003. _Bayesian Econometrics_ . New York: Wiley.

- Law, S. H., and N. Singh. 2014. “Does Too Much Finance Harm Economic Growth?” _Journal of Banking and Finance 41_ , 36–44.

- Levine, R. 2005. _Handbook of Economic Growth_ , Chapter Finance and growth: theory and evidence, 865–934. Amsterdam: Elsevier Science.

- Levine, R. and S. Zervos. 1998. “Stock Markets, Banks, and Economic Growth.” _American Economic Review 88_ (3), 537–58.

- Ley, E. and M. F. Steel. 2007. “Jointness in Bayesian Variable Selection with Applications to Growth Regression.” _Journal of Macroeconomics 29_ , 474– 93.

- Ley, E. and M. F. Steel. 2009a. “Comments on ‘Jointness of Growth Determinants.’” _Journal of Applied Econometrics 24_ , 248–51.

- Ley, E. and M. F. Steel. 2009b. “On the Effects of Prior Assumptions in Bayesian Model Averaging with Application to Growth Regression.” _Journal of Applied Econometrics 24_ , 651–74.

- Liang, F., R. Paulo, G. Molina, M. A. Clyde, and J. O. Berger. 2008. “Mixtures of G Priors for Bayesian Variable Selection.” _Journal of the American Statistical Association 103_ (481), 410–23.

- Long, J. B. D., and L. Summers. 1991. “Equipment Investment and Economic Growth.” _Quarterly Journal of Economics 106_ (2), 445–502.

- Minsky, H. 1991. _The Risk of Economic Crisis_ , “Chapter: The Financial Instability Hypothesis: A Clarification,” 158–66. Chicago: University of Chicago Press.

- Moral-Benito, E. 2012. “Determinants of Economic Growth: A Bayesian Panel Data Approach.” _The Review of Economics and Statistics 94_ (2), 566–79.

49
**Table 15: Short reform period (2007-2010)—By model design size—Random effects— regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|0.00181|0.00738|-0.504|-0.0663|
||(0.13)|(0.57)|(-0.79)|(-0.61)|
|Reform Period|-0.0450***|-0.0464***|0.0362|-0.0265|
||(-3.02)|(-3.00)|(0.70)|(-1.05)|
|Constant|-0.000932|0.00263|-1.171|-0.177|
||(-0.01)|(0.02)|(-1.07)|(-1.14)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 099 578|1 066 831|27 665|5 643|

**t-statistics shown in parentheses, treatment variable bolded *p<0.1          **p<0.05          ***p<0.01**

26
Table 3: ITT Impacts on Primary Export Outcomes

||Outcom|e in year:||Outcom|e in year:|
|---|---|---|---|---|---|
||2019|2020||2019|2020|
|**Panel A: Export at all**|||**Panel E: Export Innovation**|||
|<br>Assigned to Treatment|-0.005|-0.065|<br>Assigned to Treatment|0.012|-0.077|
||(0.043)|(0.042)||(0.056)|(0.059)|
|Control Mean|0.54|0.57|Control Mean|0.40|0.41|
|_P-values:_|||_P-values:_|200|200|
|Beta = 0|0.902|0.125|Beta = 0|0.838|0.191|
|Beta = 0.13 (policy median)|0.002|0.000|Beta = 0.13 (policy median)|0.037|0.001|
|Beta = 0.06 (academic median)|0.134|0.004|Beta = 0.05 (academic median)|0.496|0.032|
|Beta = 0.10 (firm median)|0.016|0.000|Beta = 0.06 (firm median)|0.391|0.021|
|Beta = 0.09 (literature median)|0.028|0.000|Beta = 0.086 (literature median)|0.188|0.006|
|**Panel B: Number of Products**|||**Panel F: Export Value**|||
|Assigned to Treatment|-0.142|-0.519|Assigned to Treatment|-0.375|-0.895*|
||(0.377)|(0.345)||(0.487)|(0.512)|
|Control Mean|4.16|4.05|Control Mean|7.01|7.28|
|_P-values:_|200|200|_P-values:_|||
|Beta = 0|0.708|0.134|Beta = 0|0.443|0.083|
|Beta = 1.0 (policy median)|0.003|0.000|Beta = 0.12 (policy median)|0.311|0.049|
|Beta = 0.5 (academic median)|0.091|0.004|Beta = 0.12 (academic median)|0.311|0.049|
|Beta = 1.5 (firm median)|0.000|0.000|Beta = 0.09 (firm median)|0.342|0.056|
|Beta = 1.3 (literature median)|0.000|0.000|Beta = 1.37 (literature median)|0.000|0.000|
|**Panel C: Number of Countries**|||**Panel G: Export Productivity**|||
|Assigned to Treatment|0.082|-0.179|Assigned to Treatment|-0.337|-0.671**|
||(0.184)|(0.184)||(0.327)|(0.338)|
|Control Mean|2.33|2.37|Control Mean|4.71|4.87|
|_P-values:_|200|200|_P-values:_|||
|Beta = 0|0.657|0.331|Beta = 0|0.305|0.049|
|Beta = 0.5 (policy median)|0.024|0.000|Beta = 0.09 (policy median)|0.194|0.026|
|Beta = 0.5 (academic median)|0.024|0.000|Beta = 0.09 (academic median)|0.194|0.026|
|Beta = 0.5 (firm median)|0.024|0.000|Beta = 0.06 (firm median)|0.227|0.032|
|Beta = 0.66 (literature median)|0.002|0.000|Beta = 0.38 (literature median)|0.030|0.002|
|**Panel D: Number of Product-Countries**|||**Panel H: Export Outcome Index**|||
|Assigned to Treatment|-0.027|-1.687**|Assigned to Treatment|-0.012|-0.112**|
||(0.960)|(0.757)||(0.056)|(0.056)|
|Control Mean|10.10|9.98|Control Mean|0.03|0.08|
|_P-values:_|200|200|_P-values:_|||
|Beta = 0|0.978|0.027|Beta = 0|0.831|0.048|
|Beta = 1.5 (policy median)|0.114|0.000|Beta = 0.28 (policy median)|0.000|0.000|
|Beta = 1.0 (academic median)|0.287|0.001|Beta = 0.13 (academic median)|0.012|0.000|
|Beta = 1.5 (firm median)|0.114|0.000|Beta = 0.19 (literature median)|0.000|0.000|
|Beta = 4.5 (literature median)<br>Notes: Robust standard errors in parent<br>Sample size is 200 for all regressions. Se|0.476<br>heses. *, *<br>e appendix|0.002<br>*, *** denote<br>for variable d|significance at the 10, 5, and 1 percen<br>efinitions.|t levels.||

35
**<u>Table 12: Average MWRs for Annuities Issued in March 1999, 2002, 2003, 2004, and 2005</u>**

||**March 1999**|**March 2002**|**March 2003**|**March 2004**|**March 2005**|
|---|---|---|---|---|---|
|All cases|0.978|1.080|1.036|1.064|1.062|
|- maximum|1.148|1.222|1.181|1.276|1.223|
|- minimum|0.755|0.872|0.872|0.876|0.706|
|Male, Age 55|0.981|1.081|1.056|1.036|1.042|
|Male, Age 65|0.996|1.098|1.066|1.042|1.067|
|Female, Age 55|0.994|1.105|1.056|1.060|1.064|
|Female, Age 60|1.021|1.120|1.066|1.074|1.083|
|Joint Life (Male 65, Female 60)|0.998|1.089|1.058|1.062|1.069|
|Premium up to UF1,000|0.980|1.078|1.045|1.068|1.067|
|Premium above UF3,000|0.997|1.099|1.047|1.075|1.071|
|Non-Guaranteed|0.990|1.092|1.045|1.071|1.073|
|Guaranteed|0.974|1.076|1.033|1.062|1.059|
|Without deferment|0.979|1.079|1.035|1.063|1.061|
|With deferment|0.974|1.080|1.036|1.067|1.064|

Source: Rocha and Thorburn (2007), Annex 1 As shown in Table 12, the average MWR was slightly lower than 1 in 1999 and has been above 1 since then, indicating that Chilean annuitants have generally received good value for their premiums. However, the dispersion between the maximum and minimum ratios has been wide, amounting to 40 percent in some years and suggesting that some annuitants may have obtained less favorable terms. Average MWRs are lower for younger retirees, consistent with the greater investment and longevity risks involved in annuities with longer durations. MWRs are lower for joint annuities by comparison with single annuities consistent with their longer duration as well.

MWRs are higher for larger premiums, indicating that insurance companies are willing to pay higher annuity rates for larger balances, just like banks pay higher interest rates for larger deposits, because unit costs are lower and profit margins are higher in these cases. MWRs of single female annuities are higher than those of single male annuities, despite their longer expected duration. This result may be partly due to the higher average premium in the case of single females. MWRs of guaranteed annuities are lower than those of non-guaranteed annuities, because long guarantee periods change the time path of payments and increase duration. Finally, the MWRs of deferred annuities are higher than those of non-deferred annuities, but the difference is marginal.

The availability of a large dataset on individual annuities in Chile has allowed the formal testing of these relationships.<sup>30</sup> Annex 1 of Rocha and Thorburn (2007) contains a detailed analysis of MWRs. This shows that MWRs have been positively and significantly related to the age of the annuitant and the size of the premium- and negatively and significantly related to the length of the guarantee period. It also shows that annuities with longer expected durations have lower MWRs than annuities with shorter expected durations and that larger premiums get better value on average than smaller ones. This is consistent with the view that insurers are concerned with

> 30Other researchers have computed MWRs for other countries based on a smaller number of annuity quotes rather than actual annuities issued.

20
was to draw a sample of enterprises with less than 100,000 LKR (US$1000) in capital stock, excluding land and buildings. A screening survey eliminated enterprises hiring paid employees, those owning a motorized vehicle, and those engaged in professional services, fishing, and agriculture. After reviewing the baseline data, we eliminated 18 of the 227 enterprises either because they exceeded the 100,000 LKR maximum size ceiling we had set, or because a follow-up visit could not verify the existence of the enterprise. The remaining 209 firms constituted the baseline sample. These firms almost evenly split across two broad industry categories, with 107 in manufacturing or services and 102 in retail, and by gender of the owner, with 107 firms owned by females, 98 by males, and 7 jointly owned.

After the baseline survey, we randomly selected some of the enterprises and gave them either 10,000 LKR (about US$100) or 20,000 LKR (about US$200) either in cash or in-kind grants. In the latter case, the items to be purchased were selected by the owner, and purchased by research assistants working for the project. Cash treatments were given without restrictions; recipients were told they could purchase anything they wanted with the cash. The treatment was framed as compensation for participating in the panel survey, and enterprise owners were told that they would be eligible to win the grant only once.

The aim of our experiment was to provide firms with an exogenous shock to capital stock, and to measure the impact of this on business profits. Within the affected zone 120 firms were assigned to treatment (57 percent), with 90 firms assigned to receive treatment after the baseline survey in May 2005 and a further 30 firms assigned to receive treatment after the third survey round in November 2005. This split frontloaded treatments so that more of the randomly allocated aid could reach tsunami victims sooner. The 120 treatments were made up of 77 of the 10,000 LKR treatments (39 cash, 38 inkind) and 43 of the 20,000 LKR treatments (21 cash, 22 in-kind).

Our initial plan was to survey firms for five quarterly waves only. Receipt of further funding enabled us to continue the panel, with four additional quarterly waves 17
particular SMEs, have been very limited. For some countries, commercial vendors such as Bureau van Dijk or government statistical offices provide data on private firms. However, differences in availability over time or in data collection methodologies make it difficult to obtain consistently collected data for a panel of geographically diverse firms across income categories.

To some extent, these problems with data have been mitigated by large scale multi-country firm surveys conducted by the World Bank. In particular, the World Bank’s Enterprise Surveys program takes a stratified random sample of firm-level data from the universe of registered businesses in each country for over 100 countries. The core survey uses standardized survey instruments to benchmark the investment climate of individual economies across the world and to analyze firm performance. The surveys are quite exhaustive, providing information about firm financials, the obstacles they face, the level of competition and their interactions with the government and regulatory authorities. They have been used in many applications in finance, accounting and management research.

In this section, we use data from 135 countries, where each of the countries was surveyed over the period 2006-2014, to understand the financing patterns of small and medium enterprises.<sup>4</sup> The survey defines firms of three different sizes – small (<20 employees), medium (20-99 employees), and large firms (≥100 employees) and is largely dominated by small (57% of the sample) and medium sized firms (32% of the sample).

> 4 The data used in this paper were downloaded in August 2015. Several countries have more than 1 year of survey data and we use the most recent data on each country for our analysis.

6
# **Appendix Table 1: Financial Attitudes - IV Specification**

||(1)|(2)|(3)|
|---|---|---|---|
||Financial Attitudes<br>Composite Score|Q1: Saving for<br>Child's Education|Q2: Take Out<br>Smaller Loan|
|Takeup in Autonomous Trainer Selection (T1)|0.088**|0.108<br>|0.068|
||(0.044)|(0.066)<br>|(0.044)|
|Takeup in Fixed Trainer Selection (T2)|0.045|0.049<br>|0.041|
||(0.039)|(0.062)<br>|(0.041)|
|R-squared|0.748|0.468<br>|0.753|
|N|7885|7885<br>|7885|
|Dependent Variable Mean in Control Group|<br>0.443|0.446<br>|0.440|
|F-test(p-value): T1 = T2|0.333|0.413|0.491|

This table presents instrumental variable analysis for financial attitudes using household survey data across two endline rounds. All regressions control for survey round dummies as well as stratification controls. Standard errors, clustered at the SACCO level, are reported in parentheses. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
efficient (see e.g., Aldy et al. 2010; Timilsina, 2022; Timilsina et al. 2022). At present, more than 75 economies (regional, national and sub-national) have already introduced carbon pricing instruments – carbon tax or emission trading scheme (ETS) or both (World Bank, 2023). More than 100 countries, including developing countries, have considered carbon pricing as one of the policy options to achieve their NDC targets (World Bank, 2023).

A large number of studies have been carried out on the economic and distributional implications of carbon pricing in both developed and developing economies.<sup>3</sup> For example, Timilsina et al. (2022) investigates the impacts of a carbon tax to meet the NDC in China considering nine schemes for utilizing the carbon tax revenues. It finds that a pricing instrument, carbon tax with tax revenue recycled as an output rebate to export-intensive non-energy industries, would cause nine times less GDP loss as compared to a command-and-control policy to meet the NDC target. In a study for Côte d’Ivoire, Timilsina et al. (2021) show that the impacts of a carbon tax depend on how the carbon tax revenue is used in the economy. Under certain revenue recycling schemes (e.g., cutting existing labor tax in formal firms), a carbon tax could increase GDP. Usually, a carbon tax is regressive, as reported by many existing studies included in the review study by Timilsina (2022). This is because the share of energy expenditure in the total household expenditure is normally higher in low-income households than in high-income households (Williams et al., 2015; Jiang et al., 2015). A carbon tax, therefore, faces political reluctance and social resistance. However, a carbon tax can be made progressive through a targeted transfer of carbon tax revenues to low-income households (Timilsina and Sebsibie, 2023). The proposed study will also refer to existing studies, such as Rausch et al. (2011) and Steckel et al. (2021) for the distributional analysis.

The use of fossil fuels is the primary source of GHG emissions in Egypt. While the consumption of fossil fuels, particularly petroleum products and natural gas, is subsidized to lower the energy expenditures of households and production sectors, it causes increased use of fossil fuels and thereby associated GHG emissions. Removals or reductions of fossil fuel subsidies not only lower the fuel subsidy burden of the government, but also help reduce GHG emissions. The fossil fuel subsidy is one of the major expenditures of the Government in Egypt, which was 3 Please see Timilsina (2022) for in-depth review of carbon tax studies conducted over the last three decades.
_Ln(Cit) = a0i + b0 ln(Qit) + b1 0.5 [ln(Qit)]_<sup>_2_</sup> _+ a1 ln(W1it )+ a2 ln(W2it) + a3 ln(W3it)+ +b2 0.5 ln(Qit)*ln(W1it) + b3 0.5 ln(Qit)*ln(W2it) + b4 0.5 ln(Qit)*ln(W3it) +_ where _i_ denotes banks and _t_ denotes years. _C_ is total operating plus financial costs, _Q_ is total assets, _W1_ , _W2_ , and _W3_ are the same input prices used in equations (1) and (2) and defined above. _D_ and _α0i_ denote time effects and bank-level fixed effects, respectively. As in most papers, our estimations impose the restrictions of symmetry and degree one homogeneity in the price of inputs.<sup>6</sup> We compute the H-statistic and the Lerner index using bank-level balance sheet and income statement data from Bankscope for the period 1994-2008. The sample for MENA includes data on 250 commercial and Islamic banks, operating in 12 MENA countries, namely: Algeria, Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, and United Arab Emirates. Table 1 shows the number of banks and observations for each country in our sample.

We compare competition in MENA to that in other regions using similar Bankscope data for 3,785 banks operating in 122 countries across 6 regions. Table 2 lists the countries included in each region and reports the number of banks and observations that go into the calculations of the H-statistic and the Lerner index for each region.

# **3. Empirical results on the non-structural measures of competition**

# <u>3.a. H- Statistic</u>

Table 3 shows H-statistics for individual countries in MENA over two periods: 1994-2001 and 2002-2008. Also, this table reports p-values for tests of whether H is equal to 0 and the market can be best characterized as a monopoly or H is equal to 1 and the market operates under perfect competition.

> 6 However, the results do not change if we drop these constraints.

6
||**<1.5m**|**≥1.5m**|**diff**|**<median**|**≥median**|**diff**|
|---|---|---|---|---|---|---|
|**GDP**|-0.15|0.00|0.15**|-0.15|0.02|0.16**|
||0.06|0.04|0.07|0.05|0.05|0.07|
|**GNI**|-0.07|-0.01|0.07|-0.06|0.00|0.06|
||0.04|0.05|0.19|0.09|0.06|0.13|
|**Trade balance/GDP**|-0.15|0.01|0.16|-0.11|0.02|0.12|
||0.10|0.05|0.10|0.08|0.05|0.09|
|**Current account/GDP**|0.10|0.05|-0.05|0.14|0.00|-0.14*|
||0.08|0.05|0.09|0.07|0.05|0.08|
|**Terms of trade**|0.05|-0.02|-0.07|0.08|-0.05|-0.13|
||0.12|0.08|0.16|0.09|0.09|0.14|
|**Fiscal balance/GDP**|0.29|-0.02|-0.31|-0.05|0.00|0.05|
||.|0.05|.|0.14|0.05|0.14|
|**Inflation**|-0.87|-0.68|0.19**|-0.82|-0.69|0.13*|
||0.03|0.06|0.08|0.05|0.06|0.08|
|**Total investment**|0.00|0.01|0.02|0.01|0.01|0.00|
||0.14|0.05|0.12|0.09|0.06|0.10|
|**Employment**|.|0.10|.|0.16|0.10|-0.06|
||.|0.10|.|.|0.11|.|
|**Government consumption**|-0.01|0.05|0.06|0.02|0.05|0.03|
||0.06|0.05|0.11|0.05|0.06|0.09|
|**Private consumption**|0.00|-0.03|-0.03|-0.05|-0.02|0.03|
||0.13|0.06|0.14|0.11|0.06|0.11|
|**Exports**|-0.09|-0.07|0.03|-0.15|-0.04|0.11|
||0.08|0.05|0.10|0.06|0.05|0.09|
|**Imports**|0.03|-0.05|-0.08|-0.02|-0.04|-0.02|
||0.12|0.05|0.11|0.09|0.05|0.10|
|**Private investment**|-0.30|-0.04|0.27**|-0.28|-0.02|0.27**|
||0.08|0.06|0.10|0.07|0.07|0.10|
|**Public investment**|0.17|0.02|-0.15|0.04|0.09|0.05|
||0.05|0.14|0.24|0.14|0.16|0.23|
|**Public expenditures**|0.11|0.05|-0.05|0.16|0.04|-0.13|
||.|0.05|.|0.15|0.05|0.12|
|**Public revenues**|0.42|0.02|-0.40|0.03|0.03|0.00|
||.|0.06|.|0.18|0.06|0.16|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01 Turning to the regression analysis of these correlations, column (i) of Table **7** reports the results from the regressions of correlation coefficient of real interest rate with various variables on a constant and a variable reflecting country size ((log) population).

**Table 7. Regressions of correlations with real interest rate on size:** **<u>baseline and various controls</u>**

|**baseline**|**w/**<br>**w/**|
|---|---|
||**developing=1 **<br>**all controls**|

23