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## Policy Research Working Paper 1712

# **Abstract**

This study investigates gender disparities in the tax burden in Addis Ababa, Ethiopia, using data on 2,320 taxpayers for 2011 and 2012. A quantile regression analysis is employed to control for firm characteristics such as sector, size, and age. The results show that women-owned businesses are more likely to operate in low-profit sectors and report lower sales and tax liabilities than men-owned businesses. However, women-owned businesses pay as much as men-owned businesses in taxes, suggesting that they are subject to a higher effective tax rate. This, in turn, may lead to women-owned businesses exiting the tax net at a higher rate. These findings suggest that gender disparities in tax compliance are not simply due to differences in firm characteristics but may also be due to biases in tax declaration and enforcement processes.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aambel@worldbank.org ; w.firew@gmail.com. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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# **Small Firm Death in Developing Countries**<sup>**#**</sup>

David McKenzie, _Development Research Group, World Bank_ Anna Luisa Paffhausen, _University of Passau and Development Research Group, World Bank_ _Keywords:_ firm death; survival; microenterprise dynamics _JEL codes:_ O12, O17, D22, L26

> # We gratefully acknowledge funding for this work from the Knowledge for Change Program (KCP) and Strategic Research Program (SRP) of the World Bank. We thank our collaborators and funders on the many different surveys which this work draws on; and Lorenzo Casaburi, Markus Goldstein, and participants at the 1<sup>st</sup> TDC/LSE/CEPR workshop in Development Economics for helpful comments.
_S_ , although at a declining rate. Increases in the perceived returns, _ν_ , increase _Y_ at all levels of _S_ .

The individual chooses effort at time _t_ maximizing the future earnings discounted at the time discount rate _β_ , minus the cost of effort ( _C_ ( _e_ )). In this very simple setting, information about returns to education will increase the optimal level of effort to the extent that: a) the student’s perceived returns are significantly lower than the actual returns; and b) the student places a significant value to future consumption (high _β_ ). However, an increase in effort does not necessarily generate an increase in student performance. In fact, if the complementarity between _e_ and _μ_ 0 does not hold and/or the student does not know how to translate increased effort into better outcomes, an increase in effort might not lead to improved performance.

# **3.2 Baseline Data**

In order to measure students’ characteristics at the baseline, we use two sources. First, we rely on the survey administered to the students in November 2009, just before the treatment was rolled out. In addition to providing information about the perceived returns to schooling, students were asked, among others, questions about the source of information about the returns, household income,<sup>13</sup> parents’ education and work status. The baseline survey also included a module that elicits students’ inter-temporal preferences. The respondent was asked to consider a hypothetical situation in which he/she wins a certain amount of money that can be cashed in now, or can be cashed in later for a larger sum.

Second, we use administrative data on 9th grade ENLACE scores in math and language to measure students’ ability before entering high school. From 2007 to 2013, ENLACE was administered to all students in 3rd to 9th and 12th grades. The test is voluntary and bears no consequences either on graduation or a student’s GPA. The score is normalized to have a mean of 500 and a standard deviation of 100. Mexican citizens have a unique personal identifier, known as _Clave Única de Registro Poblacional, CURP_ , formed by an algorithm combining name, surname, date of birth, sex, state of birth, plus 2 randomly generated digits. Using the student’s personal information collected during the baseline survey we could generate a quasi- _CURP_ that only differs from the original one for the lack of the last

> 13Information regarding household income was reported in brackets as follows: 1) less than $1,500 MX, 2) between $1,501 and $3500, 3) between $3,501 and $7,000, 4) between $7,001 y $10,000, 5) between $10,001 and $15,000, 6) $15,001 y $25,000, 7) more than $25,000 MX. Information about household income was not reported by 14% of the students, but the attrition rate is not statistically different for the treatment and control group.

8
discrepancies emerge when comparing certification on other quality standards (see Table 6).<sup>14</sup>

**<u>Table 6: Number of ISO certified firms</u>**

||Dec.01|Dec.02|Dec.03|Dec.04|Dec.05|Dec.06|
|---|---|---|---|---|---|---|
|ISO 9001:2000|||||||
|Slovakia|144|768|1,148|2,008|2,050|2,195|
|Macedonia|1|7|47|133|154|217|
|ISO 14001|||||||
|Slovakia|73|70|165|184|222|305|
|Macedonia|1|1||5|6|8|

_Source_ : International Standards Organization 2006 Survey Finally, the sensitivity of net exports to changes in REER may be qualitatively different due to the different exchange rate regime applied in the two countries. Under a flexible exchange rate regime (the Slovakia’s case) the exchange rate adjusts to the domestic economic activity and external environment, while under an exchange rate peg (the Macedonia’s case), the domestic economic activity and prices have to adjust to the external developments – especially in the case of small open economies. Therefore, under the peg, changes are bound to happen mostly through adjustments in prices. This will obviously change relative prices in the economy and hence the production structure, because price adjustments will vary across different sectors due to varying price rigidities and export orientation. Hence, also the pegged exchange rate contributes to the explanation of the relatively lower output gap elasticity to exchange rate changes.

_(iv) Why could the portion of the rule-of-thumb consumers (non-Ricardian households) be relatively higher in Macedonia?_ Ricardian households will increase their savings in face of increased government consumption (spending) to retain funds for accommodating future increases in taxes. They thus buy government bonds in response to fiscal impulse so that there is no actual effect on aggregate demand. The presence of significant portion of non-Ricardian

> 14 The inability to meet quality standards has translated into growing reliance on low-value added exports in Macedonia reflected in deteriorating (at best not-improving) terms-of-trade (2% decline over 2000-2005). On the other hand, Slovakia has witnessed a considerable improvement in its terms-of-trade, 23.3% over 2000-2005.

20
13 the government. Therefore, who has the authority in deciding and monitoring the specifics of the fiscal rules and debt limits will depend on the country’s constitutional and legal framework.

**III­B Subnational debt indicators – Further considerations for subnational government general budgets**

Subnational government general budgets could be monitored using indicators (i), (ii), and (iii), which are in line with international practices (see Box 1 below), and the guarantees indicated in (v) should be adequately quantified and monitored. Some further considerations on the definition and operationalization of the indicators are in order, concerning measures of repayment capacity, fiscal risks associated with land financing, arrears, and guarantees.

# **Box 1: International practice on debt indicators and limits**

**US** states mainly follow golden rules constraining the operating balance to be zero, thus debt must be used to finance capital expenditures. Rules governing subnational borrowing vary across states and depend on the type of debt issued, the revenues used as collateral, and the type of government entity issuing debt. Markets play a key role in fiscal surveillance.

**Brazil** ’s Fiscal Responsibility Law sets a limit of 200 percent on the debt-to-net current revenue ratio (current revenue net of certain national government transfers), so there is a legal limitation on the amount (not the use) of new debt flows. It also imposes a limit of 11.5 percent on the debt service-to-net current revenue. Debt restructuring agreements between federal and state governments established a comprehensive list of fiscal targets.

**Colombia** ’s extensive legislation on government debt established a traffic-light system to regulate subnational borrowing. States in the red-light zone, with the debt-to-current revenue ratio above 80 percent and the interests-to-operational savings ratio exceeding 40 percent, are prohibited to borrow.

**India** imposes a ceiling on budget deficit-to-GSDP ratio of 3 percent for all states and requests them to gradually close the current deficit. In addition, those whose debt service exceeds 20 percent of revenue are deemed to be at ‘debt stress’ and the national government closely monitors all new borrowings.

**Poland** sets a limit of 60 percent on the debt-to-revenue ratio, restricting the amount of indebtedness, and a limit of 15 percent in the debt service-to-revenue ratio. As soon as the local government debt exceeds 55 percent of revenue, however, the debt service must be reduced below 12 percent of revenue.

_<u>Source: Liu (2010), Liu and Waibel (2008)</u>_ Own revenue should be the preferred measure of debt service capacity. While the total subnational debt-to-GDP ratio refers to the economy-wide GDP as the relevant measure of aggregate repayment capacity, the debt service-to-own revenue ratio focuses on own revenue of subnational governments. International experience indicates that there are a number of reasons for using own revenues, not local GSDP, as the relevant measure of repayment capacity by local
**Table 1. Selected Financial Literacy Survey Results From Around the World**

|COUNTRY<br>(YEAR OFSURVEY)|Q1.COMPOUND<br>INTEREST*|Q2.INFLATION*|Q3.RISK<br>DIVERSIFICATION*|SURVEY<br>SAMPLE|SOURCE|
|---|---|---|---|---|---|
|High-income||||||
|United States(2009)|65%|64%|52%|1,488|Lusardi and Mitchell(2011b)|
|Italy (2006)|40%|60%|45%**|3,992|Fornero and Monticone(2011)|
|Germany (2009)|82%|78%|62%|1,059|Bucher-Koenen and Lusardi(2011)|
|Sweden(2010)|35%**|60%|68%|1,302|Almenbergand Säve-Söderbergh(2011)|
|Japan(2010)|71%|59%|40%|5,268|Sekita(2011)|
|New Zealand(2009)|86%|81%|27%**|850|Crossan et al.(2011)|
|Netherlands(2010)|85%|77%|52%|1,324|Alessie et al.(2011)|
|Upper-middle-income||||||
|Russia(2009)|36%**|51%**|13%**|1,366|Klapper and Panos(2011)|
|Romania(2010)|24%**|43%|--|2,048|World Bank CPFLprogram|
|Azerbaijan(2009)|46%**|46%|--|1,207|World Bank CPFLprogram|
|Chile(2006)|2%**|26%|46%|13,054|Behrman et al.(2010)|
|Lower-middle-income||||||
|Indonesia(2007)|78%|61%|28%**|3,360|Cole et al.(2010)|
|India(2006)§|59%|25%|31%**|1,496|Cole et al.(2010)|
|West Bank & Gaza(2011)|51%|64%**|--|2,022|World Bank CPFLprogram|

*Percent answered correctly for each of the questions displayed in the box on page six. Questions are similarly worded across surveys except where noted.

Q1:** Swedish and Chilean respondents were asked to give the exact answer in a substantially more difficult question: ―Suppose you have 200 SEK in a savings account. The interest is 10 per cent per year and is paid into the same account. How much will you have in the account after two years?‖ Russian, Romanian, and Azerbaijani respondents were asked a slightly harder question: ―Let’s assume that you deposited 100,000 rubles in a bank account for 5 years at 10% interest rate. The interest will be earned at the end of each year and will be added to the principal. How much money will you have in your account in 5 years if you do not withdraw either the principal or the interest: more than 150k rubles, exactly 150k rubles, or less than 150k rubles?‖ Q2:** In Russia and the West Bank and Gaza the following was asked: ―Let’s assume that in 2010 your income is twice as now, and the consumer prices also grow twofold. Do you think that in 2010 you will be able to buy more, less, or the same amount of goods and services as today?‖ Q3:** This result for Italy is taken from the 2008 survey (which uses a different sample), since a comparable question was not asked in 2006. New Zealanders were asked a much more difficult (and ambiguous) question: ―Which one of the following is _generally_ considered to make you the most money over the next 15 to 20 years: a savings account, a range of shares, a range of fixed interest investments, or a cheque account?‖ Russian respondents were asked: ―Which is the riskier asset to invest in: shares in a single company stock, or shares in a unit fund, or are the risks are identical in both cases?‖ In Indonesia and India: ―Do you think the following statement is true or false? For farmers, planting one crop is usually safer than planting multiple crops.‖ §Gujurat only; not nationally representative.

7
Table 7. Estimtated Fertility and Mortality Rates by World Bank Region and Cotmtry Department. 1985-2025

|Region and|||1|985-90|||_||20|00|||||20|25|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country<br>departmfent|CBR|CDR|TFR|NRR|<br>e0|IMR|<br>CBR|CD|R TFR|NRR|e0|IMR|CBR|CDR|TFR|NRR|eO|INR|
|World total|27.1|9.8|3.435|1.450|64.6|67|23.2|8.6|2.964|1.300|67.3|51|17.3|8.3|2.266|1.059|71.9|28|
|Borrowers and nan-borrowers|||||||||||||||||||
|Sub-Saharan<br>Africa|47.0|15.3|6.497|2.396|51.5|105|40.0|11.2|5.489|2.196|57.1|80|24.1|6.5|2.747|1.251|66.8|42|
|Asia and Oceania|26.1|8.9|3.232|1.367|64.0|64|21.5|7.9|2.617|1.157|67.3|47|16.0|8.5|2.149|1.008|72.2|25|
|Europe, Middle East,|||||||||||||||||||
|and North Africa|23.5|10.7|3.116|1.365|68.1|60|21.0|9.6|2.900|1.304|69.7|47|16.5|9.2|2.246|1.052|72.9|27|
|America|23.2|7.9|2.827|1.315|70.1|44|19.3|7.3|2.462|1.164|72.2|34|15.1|8.5|2.084|1.000|75.1|21|
|Borrowers only|||||||||||||||||||
|Africa Region|48.0|15.7|6.669|2.442|50.9|106|40.8|11.5|5.649|2.249|56.6|81|24.4|6.5|2.776|1.263|66.7|42|
|1 OccidentaL and Central|47.3|15.4|6.586|2.453|52.0|103|40.0|11.3|5.644|2.279|57.5|77|23.2|6.4|2.618|1.200|67.5|40|
|2 Eastern|48.3|16.1|6.736|2.435|50.1|108|41.1|11.9|5.698|2.240|55.6|84|24.7|6.8|2.820|1.270|65.9|4!|
|3 South-Central and|||||||||||||||||||
|Indian Ocean|46.1|14.8|6.407|2.394|52.0|101|39.5|10.8|5.443|2.203|57.8|77|24.0|6.2|2.759|1.271|67.7|38|
|4 Westerm|48.9|15.3|6.806|2.513|51.2|104|41.0|11.0|5.676|2.280|57.2|78|24.2|6.2|2.734|1.252|67.1|40|
|5Sahelian|47.3|18.8|6.477|2.217|46.8|127|42.3|14.3|5.875|2.198|52.3|99|26.2|7.9|3.031|1.326|63.1|55|
|6 Southern|48.0|14.8|6.689|2.482|52.2|99|40.6|10.7|5.700|2.254|57.9|76|24.3|6.1|2.764|1.271|67.7|39|
|Asia Region|26.9|9.1|3.323|1.392|63.0|66|21.9|8.0|2.653|1.168|66.6|49|16.2|8.3|2.155|1.008|71.9|25|
|1 Other South|38.4|14.3|5.194|1.867|52.4|114|32.6|11.1|4.065|1.598|56.9|90|20.2|7.7|2.432|1.087|65.9|47|
|2 Southeast|29.0|7.8|3.598|1.566|64.2|56|23.5|6.7|2.826|1.290|67.9|40|16.6|7.4|2.123|1.008|72.5|22|
|3 China|21.1|6.7|2.400|1.090|69.5|33|17.9|6.9|2.161|1.001|72.0|25|14.5|8.9|2.099|1.000|75.2|15|
|4 India|31.6|11.4|4.261|1.677|57.1|84|24.0|9.1|2.958|1.256|62.0|62|17.0|8.2|2.159|1.000|69.5|31|
|5 Indonesia<br>ard Pacific|29.0|10.7|3.637|1.475|57.3|84|23.9|8.5|2.760|1.200|63.0|58|16.7|8.1|2.145|1.002|69.8|30|
|EMN Region|34.7|11.1|4.747|1.947|60.9|83|29.0|8.8|3.866|1.659|64.4|62|19.3|7.3|2.362|1.087|70.1|34|
|1Pakistan and Turkey|41.0|12.3|5.559|2.178|56.3|96|31.7|9.3|4.232|1.738|60.5|75|21.1|6.9|2.553|1.166|68.1|39|
|2 North Africa|35.7|9.1|5.056|2.121|61.7|72|29.7|6.8|3.740|1.678|66.0|46|18.1|5.9|2.114|1.016|72.0|23|
|3 Middle East|40.4|11.3|5.740|2.287|58.4|87|32.9|8.4|4.429|1.887|63.1|65|20.0|6.6|2.337|1.066|69.5|38|
|4 Europe|15.0|10.0|2.040|0.964|71.6|25|14.3|10.2|1.993|0.945|73.6|18|13.1|11.6|2.074|0.994|75.9|12|
3 In the beginning of the first wave the interest of the population in the process was fairly low. Only when some funds started aggressive marketing campaigns did people start to buy voucher booklets containing 1,000 investment points each (Brom and Orenstein, 1993, 23). In the end 75% (78% in the Czech Republic) of the eligible population participated. This means that every citizen investing a voucher book received on average 32.4 shares (with a book value of CSK1,000 for each share).

**Table 1: Key figures from the two waves of voucher privatization in the Czech Republic**

||Firs|t wave|Second wave|
|---|---|---|---|
||Former CSFR|Czech Republic|Czech Republic|
|Number of companies involved|1491|943|867|
|Total book value privatized (CSK billion)|278|201|155|
|Eligible population participating (in %)|75|78|80|
|Number of investment funds competing|439 1/|264|353|
|Share of points allocated to IPFs (in %)|71.4|72.5|64|

1/ but only 344 founders since some sponsors founded several funds. Source: Mejstrik et al. (1994); Mejstrik (1994).

The distribution of shares was carried out in five bidding rounds<sup>5</sup> . Before the bidding started, in the so-called zero round, people could decide to either invest on their own or transfer their points to one of the newly-founded investment privatization funds (IPFs). In the zero round, 6.22 million people<sup>6</sup> transferred 6.1 billion voucher points to IPFs. This represents over 71% of all points available in the first wave. The remaining 29% of participating citizens made individual choices about how to invest (see Figure 1).

There are various reasons for the success of funds in Czech voucher privatization:

- funds were thought to have better access to non-public information, and thus, to select more attractive enterprises,

- funds offer a diversified portfolio which limits risk,

5For a more detailed analysis of the Czech privatization see, for example, Mladek (1994) or Kotbra (1993). For an assessment ofthe bidding process see Shafik (1993 and 1994).

> 65.8 million people transferred all and another 420,000 part of their points to IPFs.
Table 6: Robustness II, Average Effect of the PIP on Assisted Firms.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Dif<br>in<br>Dif|Dif<br>in<br>Dif|Dif<br>in<br>Dif|Dif<br>~~i~~n<br>~~D~~if<br>with<br>Matching|Two stage<br>Heckman|BDM<br>Correction|
|treatment_it_|0.533**|0.381|0.666|0.666|0.711|0.477|
||(0.239)|(0.128)|(0.421)|(0.426)|(2.898)|(0.292)|
|ln<br>~~s~~ales_it_||0.529|0.496|0.520|0.518||
|||(0.372)|(0.532)|(0.538)|(0.626)||
|ln<br>~~N~~employee_it_|||-0.181|-0.188|-0.187||
||||(0.262)|(0.266)|(0.265)||
|experience_it_|-0.007|-0.007|-0.218|-0.237|-0.236||
||(0.233)|(0.233)|(0.460)|(0.468)|(0.530)||
|_λ_|||||-0.026||
||||||(1.570)||
|constant|10.07***|0.855|2.193|1.992|2.017|-0.278|
||(0.704)|(7.943)|(11.724)|(11.795)|(12.850)|(0.214)|
|Observations|199|119|85|79|79|16|
|R-squared|0.301|0.380|0.354|0.362|0.362|0.308|
|Number of id|69|38|26|21|21|9|

Robust standard errors clustered at firm level are in parentheses. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

# **5 Concluding Remarks**

By and large, we find no significant impact of the PIP on Senegal’s FFV export flows when taking similar, untreated export flows as the counterfactual. There are two ways of interpreting such a no-impact result.

The naive interpretation is that the PIP simply fails to achieve its objective. That may well be true, but our failure to reject the null of no impact is not sufficient to reach that conclusion. First, as we briefly discussed in the introduction, the choice of Senegal as a testing ground was driven by data availability (Senegal 22