rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 27. Mexico banks concentr**|**Number of**<br>**ation**|**Number of**||**Number of**|||
|---|---|---|---|---|---|---|
||<br>**Checking**<br>**accounts**|<br>**savings**<br>**accounts**|**Number of**<br>**term deposits**|<br>**credit cards**<br>**accounts**|**Branches by**<br>**bank**|**Personnel.**|
|**National number of accounts,**|||||||
|**branches and personnel**|**16,778,808**|**11,533,662**|**3,777,994**|**12,408,703**|**7,801**|**112,229**|
|**Mexico city total**|**4,177,797**|**3,062,459**|**728,762**|**4,774,387**|**1,508**|**43,561**|
|**HSBC**|**1,254,288**|**57,757**|**116,192**|**124,666**|**270**|**6,171**|
|**BBVA**|**351,443**|**2,630,229**|**154,452**|**1,810,223**|**297**|**10,455**|
|**Banamex (Citibank)**|**760,553**|**7,787**|**260,241**|**2,127,747**|**241**|**12,651**|
|**Santander Serfin**|**975,977**|**15,302**|**61,505**|**40,581**|**200**|**4,327**|
|**Share of 4 largest banks (%)**|**80.0**|**88.5**|**81.3**|**85.9**|**66.8**|**77.1**|
|**Scotiabank Inverlat**|**269,113**|**43,039**|**46,513**|**90,980**|**88**|**3,085**|
|**Banorte**|**356,046**|**1,087**|**75,082**|**36,044**|**219**|**3,055**|
|**Inbursa**|**139,196**|**0**|**140**|**1,092**|**13**|**675**|
|**Ixe**|**34,239**|**90**|**3,236**|**2,740**|**17**|**536**|
|**Share of 8 largest banks (%)**|**99.1**|**90.0**|**98.4**|**88.7**|**89.2**|**94.0**|
|**Source: CNBV September 2003.**|||||||

55
# **Appendix Table 17: Children in monetary** **<u>poor households in 2022 at the $3.65 line in 2017 PPP by gender</u>**

|**Gender**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Female children|397.6|39.5|22.7|14.9|
|Male children|421.6|38.9|24.0|16.0|
|Female adults|481.4|20.2|27.5|35.2|
|Male adults|452.6|19.7|25.8|33.9|
|**Total**|1753.3|25.9|100.0|100.0|

# **Appendix Table 18: Children in monetary** **<u>poor households in 2022 at the $6.85 line in 2017 PPP by gender</u>**

|**Gender**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Female children|684.6|68.0|19.4|14.9|
|Male children|732.4|67.6|20.7|16.0|
|Female adults|1076.9|45.1|30.5|35.2|
|Male adults|1037.3|45.1|29.4|33.9|
|**Total**|3531.2|52.1|100.0|100.0|

# **Appendix Table 19: Children in monetary** **<u>poor households in 2022 at the $2.15 line in 2017 PPP by rural/urban location</u>**

|**Location**|**Extreme poor**<br>**(millions)**|**Headcount**<br>**poverty rate (%)**|**Share of extreme**<br>**poor (%)**|**Share of**<br>**population (%)**|
|---|---|---|---|---|
|Urban|54.1|6.5|16.4|40.3|
|Rural|276.4|22.4|83.6|59.7|
|**Total**|330.5|16.0|100.0|100.0|

# **Appendix Table 20: Children in monetary** **<u>poor households in 2022 at the $3.65 line in 2017 PPP by rural/urban location</u>**

|**Location**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of extreme**<br>**poor(%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Urban|167.3|20.0|20.5|40.3|
|Rural|647.8|52.4|79.5|59.7|
|**Total**|815.1|39.4|100.0|100.0|

# **Appendix Table 21: Children in monetary** **<u>poor households in 2022 at the $6.85 line in 2017 PPP by rural/urban location</u>**

|**Location**|**Children in**<br>**poverty (millions)**|**Headcount**<br>**poverty rate(%)**|**Share of poor (%)**|**Share of**<br>**population(%)**|
|---|---|---|---|---|
|Urban|391.5|46.9|27.7|40.3|
|Rural|1019.7|82.5|72.3|59.7|
|**Total**|1411.1|68.1|100.0|100.0|

16
**Table 2: Sources of data used in this study**

|**Data**|**Description**|**Source**|
|---|---|---|
|District<br>boundaries|Politically defined district boundaries – shape<br>files|USAID, FEWS, EDC-International<br>Program and the U.S. Geological<br>Survey under the Africa Data<br>Dissemination Service (ADDS) and<br>Yale University.|
|||Ghana and Kenya map provided by<br>UNESCO (1997) through<br>UNEP/GRID-Sioux Falls and Yale<br>University.|
|Land use|13 land classes - 0.5<sup>o</sup>grid.|International Institute for Applied<br>Systems Analysis (IIASA) - obtained<br>directly by personal comm.|
|Rivers of<br>Africa|Digitized map of African rivers – polylines|**http://www.fao.org/geonetwork/srv/**<br>**en/metadata.show?id=12&currTab=**<br>**summary**|
|Irrigation<br>potential|Global map of irrigated areas – 0.5<sup>o</sup>grid|**http://www.fao.org/waicent/faoinfo/**<br>**agricult/agl/aglw/aquastat**|
|Maximum soil<br>moisture<br>capacity|Values between 0 and 1,000 mm – 0.08<sup>o</sup>grid.|**http://www.fao.org/geonetwork/srv/**<br>**en/metadata.show?id=5018&currTa**<br>**b=summary**|
|Precipitation,<br>temperature,<br>vapor, wind<br>speed,<br>pressure and<br>cloudiness|CRU-CL 1.0 dataset. A dataset of mean<br>monthly surface climate over global land areas,<br>excluding Antarctica. Interpolated from station<br>data to 0.5 degree lat./long. for a range of<br>variables: precipitation and wet-day frequency,<br>mean temperature and diurnal temperature<br>range (from which maximum temperature and<br>minimum temperature can be determined),<br>vapor pressure, sunshine, cloud cover, ground-<br>frost frequency and windspeed – 0.5<sup>o</sup>grid (New<br>et al. 1999)|**http://ipccddc.cru.uea.ac.uk/asres/b**<br>**aseline/climate_download.html**|
|Drainage<br>network|Topographically generated drainage lines<br>(polylines) for each 0.5<sup>o</sup>grid cell. Named STN-<br>30p|University of New Hampshire CD -<br>UNH/GRDC Composite runoff fields<br>V1.0.|
|Calibration<br>runoff|Simulated runoff (average monthly totals in<br>mm) 0.5<sup>o</sup>grid|University of New Hampshire CD -<br>UNH/GRDC Composite runoff fields<br>V1.0.|

38
<!-- Start of picture text --> $5,000 98%<br>4,514 86.0%<br> $4,500<br>78%<br> $4,000<br> $3,500<br>58%<br> $3,000<br> $2,500 38%<br>25.4%<br> $2,000<br>1,624<br>11.5% 10.0% 18%<br> $1,500<br> $1,000 9.2% 813 812 809 809<br>-2%<br> $500 -15.6%<br> $- -22%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Source: author’s calculations<br>Millions<br>Growth (in %)<br><!-- End of picture text --> A complete convergence of gender gaps was estimated to increase GDP by 23.6 percent (Figure 7). Given the endogenous relationships between the sub-modules developed, it was not appropriate to simply sum the effects of the individual convergences. The model calculated that Liberia’s GDP would be USD 5.01 billion if all convergences would occur simultaneously. This specification maximizes the potential of women across the inputs of production in the model as their contribution would be 1.87 billion or 37.4 percent of GDP and 50.2 percent of the aggregate production of labor. A 10.6 percent growth and 11.1 percent growth from the initial estimates, respectively. The modern sector would generate 49.2 percent of the economic wealth and women would contribute 50.1 percent of it. The average wage of women would grow by 7.3 percent in the modern sector and 26.4 percent in the traditional sector. The average wage of men would grow too, by 0.7 percent in both sectors of production. Similar to the abovedescribed relationship between GDP and poverty, the increase in aggregate output led by simultaneously closing the gender gaps modeled would allow poverty to reduce by 17.4 percent and reach a poverty headcount of 36.7 percent from the initial 44.4 percent.

**Figure 7. Selected macro-economic changes due simultaneous convergence of all gender gaps (in USD, growth in %)**

22
<!-- Start of picture text --> 0.65<br>0.60<br>a<br>a<br>me}<br>& 0.55<br>3<br>v<br>c=]<br>q<br>a<br>0.50<br>0.45—<br>1950 Cohort (which1960 decade individuals1970 are born in) 1980<br>=—e— Developing economies == High-income economies<br><!-- End of picture text --> <!-- Start of picture text -->
1.0<br>0.8<br>c<br>S<br>£06<br>Qa<br>°<br>a<br>xe)<br>oO50.4<br>x=<br>ao<br>0.2<br>0.0<br>0 1 2 3-4 5-8<br>Max parental education (ISCED category)<br>mmr 1950 = 1960 = 1970 = 1980<br><!-- End of picture text -->
The first and second observations confirm the expectations anchored in the descriptive analyses discussed in Section 2. Plots that are further from the dwelling are inherently different from the ones that are closer. Thus, as missingness increases, the pool of plots with similar characteristics (and thus comparable areas) to choose from gets smaller, and it is understandable that the distribution differs substantially. The third observation is also foreseen: land area being the denominator of the formula for yield, a small deviation of the imputed values from the observed land values brings about a relatively more important deviation in the yield estimates obtained from them. Consequently, the yields calculated from the imputed land areas differ substantially from the true yields at lower rates of missingness.

We now compare the results obtained in the different panels depicted in Figure 1. For convenience, the tolerable rates of missing GPS-based plot areas are summarized in Table 7. Along with the tolerable rates in terms of the percentages of plot areas observations that could go missing beyond a given distance threshold, we report the corresponding overall rates of missingness in parentheses. In the discussion that follows, we focus on the discussion of the results pertaining to plot-level agricultural productivity, given the policy relevance of the outcome and its lower tolerance to missingness vis-à-vis plot area.

_Table 7: Tolerable Rates of Missingness in GPS-Based Plot Areas Above a Given Distance Threshold for Plot Area & Plot-Level Yield Analysis_

|||**Plot Area**|**Yield**|
|---|---|---|---|
|||_Tolerable_<br>_rate (%)_|_Tolerable_<br>_rate (%)_|
|||93|82|
|**Mli**|**1.0 km**|(26)|(23)|
|**aaw**||52|45|
||**500 m**|(24)|(21)|
|||73|56|
|**Ethii**|**1.0 km**|(18)|(13)|
|**opa**||48|36|
||**500 m**|(20)|(15)|

**Note:** The overall rates of missingness implied by the tolerable rates of missingness above a given distance threshold are noted in the parentheses.

23
# **Tax Compliance Perceptions and Formalization of Small Businesses in South Africa**

# **Introduction**

**Background and motivation** : The Government of South Africa is committed to encouraging the growth of entrepreneurship in the country, including the formalization of informal firms. To this end, the National Treasury (NT) and the South Africa Revenue Service (SARS) have been developing tax policy and tax administration reforms, including a “tax amnesty” for many previous informal firms and a concerted effort to reduce the tax compliance burden on Small, Medium, and Micro Enterprises (SMMEs).<sup>1</sup> For the purposes of this study, and in line with current tax legislation in South Africa, SMMEs are defined as those with an annual turnover under R 14 million (about US$ 2 million).

At the request of the National Treasury of South Africa (NT) and the South Africa Revenue Service (SARS) in 2006, the Foreign Investment Advisory Service of the World Bank Group (FIAS) initiated a set of surveys to measure the tax compliance burden for SMMEs. Earlier studies in South Africa (e.g., SBP 2005) had also documented onerous compliance burdens on SMMEs, including tax compliance costs in particular.

Anecdotal evidence in South Africa suggested that many of the smallest firms struggle with tax compliance, as is common elsewhere in the world (see Literature Review, below). Meanwhile, fear of “making mistakes” on taxes and the risk of heavy penalties imposed by SARS (in addition to unwillingness to pay taxes) was said to be a factor deterring formalization of many informal firms. Other research in South Africa suggested that broader attitudes about the fairness of the fiscal system (both spending and taxation) and the quality of public services also had an influence on the willingness of the owners of informal firms to formalize and start paying taxes (Fjeldstein, 2004).

**Brief Overview of the Survey** :  To document the extent of the problem in South Africa, FIAS worked with NT and SARS to design a set of three surveys:

- A survey of professional tax practitioners (TP) about their SMME clients

- A survey of SMMEs registered with SARS

- A survey of informal SMMEs regarding their perceptions of tax compliance costs.

The latter two surveys listed above were both co-financed by USAID and carried out by Citizen Surveys. The surveys focused on firms with turnover between R70,000 and R 14 million to exclude both the tiniest “survivalist” firms (e.g., those whose income would be too low to be assessed for personal income tax if they were employees) as well are large firms. The survey field work for the informality survey included 1,000 small businesses that were not registered with SARS (based on area sampling) and took place in mid 2007. The survey of formal firms included 1,000 businesses as a representative sample drawn from the SARS database of registered business taxpayers and was completed in late 2007. Both reports were finalized in March 2008 (USAID, 2008).

> 1 For details of the new tax regime for micro-enterprises (i.e., those under R 1 million turnover), see <u>http://www.sars.gov.za./home.asp?pid=43122</u>

2
Table 5: Instrumental Variables Identification of Growth Elasticity of NO2 ( _β_ ).

||ln(NO2)<br>(1)|ln(NO2)<br>(2)|ln(NO2)<br>(3)|ln(NO2)<br>(4)|ln(NO2)<br>(5)|
|---|---|---|---|---|---|
|ln(GDP)|0.141***<br>|0.262**<br>|0.198***<br>|0.226***<br>|0.271***<br>|
||(0.031)|(0.116)|(0.068)|(0.071)|(0.070)|
|Instruments||||||
|ln(Lights)||✓|✓|✓|✓|
|ln(Survey)|||✓|✓|✓|
|ln(Electricity)||||✓|✓|
|ln(CO2)|||||✓|
|Relevance||||||
|Kleibergen-Paap _rk_ LM statistic||14.208|16.933|18.491|26.188|
|(_H_0: Underidentifed )||[_<_0.001]|[_<_0.001]|[_<_0.001]|[_<_0.001]|
|Weak Identifcation||||||
|Cragg and Donald (1993) Wald _F_ statistic||200.7|574.1|521.7|496.1|
|Kleibergen and Paap (2006) _rk F_ statistic||16.0|30.9|28.1|33.8|
|Overidentifying Restrictions||||||
|Sargan (1958)-Hansen (1982) _J_ statistic|||1.423|4.204|4.916|
|(_H_0: All Exogenous)|||[0.273]|[0.122]|[0.178]|
|Observations|2,752|2,752|2,303|2,226|2,071|
|Countries|178|178|154|149|146|
|Year|05-20|05-20|05-19|05-19|05-19|
|R-squared|0.049|0.013|0.045|0.039|0.035|

_Notes._ All specifications include country and year fixed effects. When using nights lights, fixed effects for either OLS sensor (average over DMSP satellites; pre-2014) or VIIRS (2014-2020) included. Robust standard errors, clustered by country, are reported in (parentheses) and _p_ -values in [brackets]. R-Squared is adjusted and within all fixed effects, where applicable.

22
of standard controls including experience (and experience-squared), marital status, _i_ gender, industry, and occupation. Finally,  _jk_ is the error term at the individual level, which is correlated between individuals in the same cohort. We adjust standard errors by accounting for cluster effects within cohorts.<sup>8</sup> We use two main data sources: the 2006 Labor Force Survey (LFS) and the nationally representative migration survey undertaken in 2006 by CBS-AXA (2006). The CBSAXA survey collects detailed demographic and socio-economic information on every member of the household, including information on whether there are members of the households who are currently abroad, their labor market and remittance activities, etc. We construct education-experience emigration cohort using CBS-AXA data. The rest of the individual-level data are from the LFS. The analysis includes only the working-age, wage-employed individuals, following standard practice. We also use supplemental summary information from the Annual Survey of Enterprises to report relevant labor demand trends and discuss the implications of the main results. The descriptive statistics are in Appendix Table 1. Wages are top-coded and we address this in our robustness tests.

We define as emigrants those who are of working age who are currently abroad and who may or may not have existing ties to households in Moldova.<sup>9</sup> The inclusion of emigrants who are considered ex-members of households in Moldova is an improvement over the existing literature, which has tended to discuss Moldovan emigration only with respect to those with existing ties to Moldovan households. Nonetheless, our measure of emigration may still be subject to measurement error due to sampling error and because households whose members have all left Moldova will not be captured in the survey.

> 8 This was done using the _cluster_ option in Stata. We also ran our baseline model correcting for cluster effects due to the multi-stage stratified design of the LFS and found similar results.

> 9 Other studies (e.g., International Organization of Migration, 2007) restrict their analyses to those emigrants who are still considered members of households in Moldova. In contrast, we also counted among the emigrants those who (a) are of working age and are currently abroad, (b) are considered ex-members of households in Moldova, and (c) may have since formed their own households abroad.

7
for orchestras by exploiting the introduction of screens (or similar devices) that would hide the auditioning musician from the jury. Their findings suggested that gender-blind auditions increase the relative probability that women will advance from the preliminary round or will ultimately be hired in the final round by more than 50 percent, although such auditions also reduce the relative probability of women advancing from a semifinal round in auditions that include a semifinal.

Audit studies do, however, have certain limitations (see Heckman 1998). For one thing, the requirement that two (real) individuals are identical in all dimensions relevant for an employer with the exception of gender is arguably far-fetched. Moreover, because these individuals are not blind to the research process, the design is also subject to demand effects. To address these issues, researchers have turned to _correspondence studies_ . Instead of relying on real applicants, correspondence studies leverage fictitious applicants. For example, in response to job advertisements researchers send pairs of resumes, while having full control of the applicants’ characteristics as well as the information given to employers. This control is able to generate strict comparability in everything but the group variable of interest, gender. In addition, because of the relatively low marginal cost, researchers can send out a large number of applications to a wide range of employers.

By studying different industries as well as different sets of skills, these studies have questioned the validity of certain theories, such as taste-based models, while also discovering heterogeneous effects that could be interpreted using both statistical discrimination and stereotyping arguments. For example, Riach and Rich (2006) have studied gender discrimination in the English labor market for entry-level jobs. They focused on the so-called type of occupation: male occupations (such as engineer), female occupations (such as secretary), and mixed occupations (such as trainee accountants and computer analysts).

Their results reveal that female candidates are more likely to receive a callback for occupations that are more female-dominated, which could be attributed to employers holding gender stereotype beliefs. Booth and Leigh (2010) had similar results. After studying a sample of over 3,000 applicants in Australia and focusing on female-dominated professions (wait staff, data entry, customer service, and sales jobs), they estimated a callback of 1.28 in favor of women. A similar yet weaker result was found in the Swedish labor market. Also using a large sample (3,228), Carlsson (2011) found a female-to-male callback ratio of 1.07, with a higher rate in female15
20 Kee, H.L., A. Nicita and M. Olerreaga (2009), ‘Estimating Trade Restrictiveness Indexes’, _Economic Journal_ 119(534): 172–199.

- Lloyd, P.J. (1974), ‘A More General Theory of Price Distortions in an Open

- Economy’, _Journal of International Economics_ 4(4): 365-86, November.

- Lloyd, P.J., J.L. Croser and K. Anderson (2010), ‘Global Distortions to Agricultural Markets: New Indicators of Trade and Welfare Impacts, 1960 to 2007’, _Review of Development Economics_ 14(2): 141-60, May.

- OECD (2006), _Agricultural Policies, Markets and Trade in the Central and Eastern European Countries and the New Independent States: Monitoring and_ Outlook, Paris, OECD.

- OECD (2009), _Producer and Consumer Support Estimates_ (online database accessed at <u>www.oecd.org</u> for 1986–2007 estimates; and OECD files for estimates using an earlier methodology for 1979–85.

Roningen, V.O., ‘VORSIM version 5’, www.vorsim.com.

- Serra, T., D. Zilberman, B. K. Goodwin and A. Featherstone (2006), ‘Effects of Decoupling on the Mean and Variability of Output’, _European Review of Agricultural Economics_ 33(3): 269–88.

- Tyers, R. and K. Anderson (1992), _Disarray in World Food Markets: A Quantitative Assessment_ , Cambridge and New York: Cambridge University Press.

- Valdes, A. and J. Zeitz (1980), ‘Agricultural Protection in OECD Countries: its Cost to Less Developed Countries’, Research Report 21, International Food Policy Research Institute, Washington DC, December.
# **Table 2 Descriptive statistics for the Colombian Stock Exchange**

The table displays yearly market stock returns. For Colombia, returns are calculated using the COLCAP, a value weighted index. The MSCI Latin America index captures large cap and mid cap firms across five countries: Brazil, Chile, Colombia, Mexico, and Peru. On December 2015, the country weight of Colombia in the index was 3.15%. Market capitalization (market cap) is the end-of-year total stock market value. Foreign flows and number of stocks are from the CSE dataset.

|Year|MSCI Latin<br>America|Colombian Stock<br>Market|Market Cap<br>($ billion cop)|Foreign Flows<br>($ billion cop)|Number of<br>stocks|
|---|---|---|---|---|---|
|2006|43.2%|16.0%|11,161|642|68|
|2007|50.4%|-4.3%|10,694|275|67|
|2008|-51.4%|-34.7%|7,561|631|66|
|2009|103.8%|42.8%|11,602|943|62|
|2010|14.7%|28.9%|15,497|122|63|
|2011|-19.4%|-20.2%|12,666|-13|71|
|2012|8.7%|15.0%|14,716|-44|72|
|2013|-13.4%|-11.9%|13,071|479|70|
|2014|-12.3%|-11.6%|11,635|-370|66|
|2015|-31.0%|-30.8%|8,547|-1390|65|

# **Table 3 Passive investors**

This table reports the share of assets under management and proportion of passive investors by investor type. Passive investors/funds are defined as those with an Active Share measure below 60 percent. Sample includes investors with at least 100 trades.

||% assets man|aged by passive|% number|of passive|
|---|---|---|---|---|
|Investor Type|2006-2010|2011-2016|2006-2010|2011-2016|
|Domestic Individuals|35.1|21.9|25.8|12.2|
|Domestic Institutions|53.5|71.7|32.3|26.2|
|Corporations|13.7|15.2|27.9|20.5|
|Pension Funds|97.9|99.0|81.9|89.5|
|Mutual Funds|92.7|80.7|70.3|49.7|
|Brokerage Firms|45.7|74.0|31.2|40.2|
|Others|15.8|28.3|31.4|30.1|
|Foreign|29.4|64.8|29.4|37.8|

28