rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 1. How Do the Bilateral Data Compare to the Balance of Payments Data?**

How do the bilateral data used in this paper compare with the flows reported by the financial account of the balance of payments (BoP)? In particular, (i) do bilateral flows systematically underestimate or overestimate the flows reported by the BoP? and (ii) do these bilateral flows move over time in a manner that is consistent with the flows derived from the BoP?

BoP data come from the International Monetary Fund (IMF) and provide country-level information on an annual basis from 1970 until 2012 on different types of capital flows measured in current U.S. dollars. The data are divided into two major categories: the current account and the financial account. The financial account is divided into four subcategories: direct investments, portfolio investments, other investments, and international reserve assets. While the BoP data provide aggregate figures for each country with respect to the rest of the world, the data employed in this paper are at the bilateral (country-pair) level. Therefore, we need to aggregate them to compare the two databases.

For FDI (M&A and greenfield flows), the two databases can be compared directly. Appendix Figure 3 shows the total flows to/from North and South (including LAC) countries for each of the two databases.

At the region-year-level, the figure shows a significant positive correlation between the two datasets and similar values for both of them. However, for South countries (both inflows and outflows) the bilateral data seems to overestimate slightly the flows reported in the BoP accounts, possibly because the bilateral data are gross inflows, while the BoP data are net inflows (e.g., the net of inflows and outflows of foreigners). In addition, the greenfield data used reflect announced investments and they may differ from the actual flows recorded in the BoP data. Still, at the countryyear-level, the correlation between the bilateral data and the BoP data is still rather high: 0.89 in the case of outflows and 0.86 in the case of inflows.

For syndicated loans, direct comparison between the bilateral data and the BoP data is not possible, becuase the “other investment” category in the BoP database covers not only syndicated loans but short- and long-term trade credits, loans, currency and deposits (transferable and other— such as savings and term deposits, savings and loan shares, shares in credit unions, etc.), and other accounts receivables and payables (IMF, 1993). Thus, syndicated loans enter only as part of the other investment category in the BoP.

For portfolio investments, the BoP database covers transactions in equity and debt securities, while the bilateral database used in this paper (the CPIS) contains information about the holdings of 24
Table 3: Number and Percentage of Taxpayers by Gender and Year

||2011|2012|Total|
|---|---|---|---|
|Men|1308|1582|2890|
||71|71|71|
|Women|524|634|1158|
||29|29|29|
|Total|1832|2216|4048|
||100|100|100|

Note: The percentage by gender is displayed below the number of taxpayers or observations.

ence of women in informal businesses and various barriers limiting women’s economic engagement.

Furthermore, beyond the variables mentioned earlier, the data-set encompasses additional information such as industrial classifications, taxpayer categories, and the adoption of sales registry machines. This supplementary information offers control variables to isolate gender disparities in tax declarations and payments.

# **4. Empirical Evidence of Gender Difference in the Tax Burden**

# _4.1. Descriptive Statistics_

# _4.1.1. Profitability, Tax Declarations and Income Tax Burden by Gender_

Table 4 presents the disparities between businesses owned by men and women using tax declaration indicators and payment. The differences in financial statements and tax liability declarations for businesses owned by men and women will be interpreted as differences stemming from the taxpayers’ behaviors. Conversely, a difference in tax payment will be interpreted as a tax enforcement differences. The first and third columns of Table 4 present the mean values for businesses owned by men and women, respectively. The fifth column displays the difference between the mean values for men and women, along with whether the difference is statistically significant. When 12
# **Table 3**

# **Average Pairwise Spearman correlation coefficients by agent, type of financial service and stability measures**

The average value of the correlation coefficients for each category reported in Table 2. Rows are organized according to the stability measure; columns refer to the types of financial services, split by agent. Red marks denote negative average correlations statistically different from 0; green marks positive average correlations statistically different from 0. Statistical significance (5 percent level) is determined using Fisher Z- transformation.

|**Average Pairwise Spearman**<br>**Correlations**|**Account ow**|**nership**|**Paymen**|**ts**|**Saving**|**s**|**Credi**|**t**|**Insura**|**nce Indivi**|**duals**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||Individuals|Firms|Individuals|Firms|Individuals|Firms|Individuals|Firms|Agri|Life|Non-life|
|High resilience|**-0.223**|-0.096|**-0.189**|-0.155|-0.152|0.132|-0.152|-0.098|0.101|-0.095|-0.081|
|_Capital_|**_-0.220_**|_-0.037_|_-0.161_|**_-0.170_**|**_-0.187_**|_0.060_|_-0.131_|_0.059_|_0.098_|_-0.240_|_-0.251_|
|_Liquidity_|**_-0.413_**|**_-0.310_**|**_-0.413_**|**_-0.243_**|**_-0.293_**|_0.159_|**_-0.353_**|**_-0.369_**|_0.275_|_0.022_|_0.171_|
|_NPLs_|_-0.038_|_0.058_|_0.006_|_-0.052_|_0.024_|_0.177_|_0.029_|_0.016_|_-0.071_|_-0.065_|_-0.162_|
|Low volatility|-0.002|0.007|-0.060|0.049|0.055|0.049|-0.024|0.084|-0.026|-0.272|0.046|
|_Low variability lending rate_|**_-0.272_**|_-0.178_|**_-0.350_**|_-0.139_|_-0.162_|_0.131_|**_-0.248_**|_-0.033_|_0.110_|_-0.053_|_0.282_|
|_Low variability credit growth_|**_-0.379_**|_-0.166_|**_-0.411_**|**_-0.190_**|**_-0.349_**|_0.111_|**_-0.288_**|_-0.181_|_-0.002_|**_-0.684_**|_-0.017_|
|_Low variability bank deposit rate_|**_0.313_**|**_0.249_**|**_0.257_**|**_0.262_**|**_0.357_**|_-0.020_|**_0.204_**|**_0.312_**|_-0.147_|_0.069_|_-0.035_|
|_Low variability deposit growth_|**_0.331_**|_0.122_|**_0.265_**|**_0.261_**|**_0.375_**|_-0.026_|**_0.237_**|**_0.239_**|_-0.065_|_-0.418_|_-0.044_|
|Milder/No crisis|**-0.459**|-0.073|**-0.462**|**-0.436**|**-0.383**|**0.259**|**-0.310**|0.044|0.132|0.117|0.153|

24
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# **APPENDIX 3: REGRESSION RESULTS WITH AN EXTENDED NUMBER OF OBSERVATIONS.**

|Equation|1|2|3|4|
|---|---|---|---|---|
|Estimationprocedure|Poisson|Poisson|Tobit|Tobit|
|Dependet variable|Number of<br>Projects|Number of<br>Projects|Total Volume<br>of Investment|Annual<br>Volume of<br>Investment|
|_hst_Averag GDP_|8.68E-06***<br>(29.53)|9.63E-06***<br>(29.04)|||
|_inv_Averag GDP_|8.43E-07***<br>(20.07)|1.06E-06***<br>(20.96)|||
|_hst_inv_Averag GDP_||-1.18E-12***<br>(-6.93)|||
|_hst_Average Annual Energy Use_|||3076.01***<br>(6.54)|409.74***<br>(6.57)|
|_inv_Average Annual Energy Use_|||5389.33***<br>(8.70)|715.44***<br>(8.70)|
|_hst_Ease of Doing Business_|-8.90E-03***<br>(-9.64)|-7.41E-03***<br>(-5.35)|-4.70E06***<br>(-2.60)|-6.32E05***<br>(-2.63)|
|_inv_Ease of Doing Business_|-0.016***|-1.4E-02***|3.37E06|4.52E05|
||(-5.59)|(-2.16)|(0.69)|(0.69)|
|_hst_inv_Ease of Doing Business_||-4.19E-5<br>(-0.61)|||
|_hst_Governance_|0.042***<br>(3.73)|0.042<br>(0.89)|3.15E07*<br>(1.81)|4.09E06*<br>(1.77)|
|_inv_Governance_|0.300***<br>(15.46)|0.286***<br>(14.23)|2.18E08***<br>(4.65)|2.89E07***<br>(2.72)|
|_host_inv_Governance_||1.64E-04<br>(0.04)|||
|_hst_Renewable Energy_|8.63E-04***<br>(10.63)|8.81E-04***<br>(10.47)|5.32E05***<br>(2.70)|7.11E04***<br>(2.72)|
|_inv_Renewable Energy_|-8.38E-04***<br>(-5.30)|-9.82E-06<br>(-0.05)|-7.44E05***<br>(-3.10)|-9.88E04***<br>(-3.10)|
|_host_inv_Renewable Energy_||-1.23E-06***<br>(-4.63)|||
|_hst_Impact Vulnerability_|0.061***<br>(23.68)|0.099***<br>(12.99)|1.50E07***<br>(3.87)|1.97E06***<br>(3.83)|
|_inv_Impact Vulnerability_|0.018***<br>(8.29)|0.083***<br>(6.38)|2.49E07***<br>(5.09)|3.30E06***<br>(5.20)|
|_host_inv_Impact Vulnerability_||-1.15E-03***<br>(-5.24)|||
|_Trade1_|85.71***<br>(7.32)|146.45***<br>(9.28)|||
|_Constant_|-8.29***|-10.68***|-5.94E09***|-7.87E08***|
||(-24.56)|(-19.28)|(-8.79)|(-8.79)|
|Observations|3411|3411|2856|2856|
|Uncensored obs.|||182|182|
|LogLikelihood|-2898.2|-2849.64|-4313.30|-3945.42|
|LR|10573.85***|10670.97***|419.20***|419.69***|
|Pseudo R<sup>2</sup>|0.646|0.651|0.046|0.050|

In parentheses are t-values; *** (=0.01); ** (=0.05); * (=0.10).

38
these specific variables from these specific surveys had been used in the literature (i.e., whether as an outcome or as explanatory variables, or as indicative of social norms). We were able to detect that for a given topic, data were used as indicative of a presence of social norms when the indicators referred to were closer to beliefs or opinions (for example, acceptance of wife-beating, intensity of preference for sons), and as an outcome when reporting occurrence or frequency of a behavior (for example, incidence of IPV, fertility). We found many indicators being used both to refer to dimensions of women’s empowerment or agency and to social norms. We then proceeded to sort indicators from the closest proxy of a possible social norm (i.e., beliefs and normative explanations for behaviors), to the ones that reported individual-level outcomes that appear to be the most independent from a possible gender norm. We then identified which indicators were available across countries and survey rounds. The final classification of indicators labels them as either ‘gender norm-influenced outcomes’ or ‘gender-normative attitudes’. Most of the first set of variables come from the DHS data, while the latter is mainly from the WVS data.

Since our primary gender outcome of interest is employment status, that variable is included, followed by education (years of education), which impacts gender outcomes across multiple domains. Next, we look at key hypothesized determinants of labor force participation around marriage and children (age of marriage and age at first birth). The next set of variables relate to intrahousehold power and negotiationagency (role in household decision-making), IPV (incidence and acceptance), age and education gap with spouse, difference between desired fertility and realized fertility, time between marriage and age at first birth, and son preference. The literature has identified these variables as strongly influenced by social norms. Clearly, some variables have a more direct link with social norms, such as the age of marriage and IPV, while others, such as educational achievements, are determined by multiple factors, and their links with social norms may be less direct. Lastly, we look at a list of subjective attitudes around gender that are expected to have a strong correlation with underlying gender norms: gender attitudes towards education, economic empowerment, IPV, and fertility. While attitudinal data remains an imperfect and likely incomplete proxy for underlying social norms, cross-country data on influential social norms are not available. Table 1 summarizes these indicators across the different domains.

# **4. Methods**

_Pseudo-panel from repeated cross-sections_ Using repeated cross-sections from the Demographic and Health Surveys and the World Value Surveys, we create pseudo panels, where individuals from specific birth cohorts are followed as they age. Using

> what is acceptable and appropriate as an action for women and men in a given group or society (Cislaghi and Heise, 2020).

7
# **Contents**

|**1.**<br>**INTRODUCTION ............................................................................................................ 2**|
|---|
|**2.**<br>**THE SUGAR SECTOR ................................................................................................... 3**|
|**2.1** **GOVERNMENT INTERVENTION AND MARKET STRUCTURE OF THE SUGAR INDUSTRY 4**|
|**2.2** **THE SUGAR CRISIS OF2011: POTENTIAL EXPLANATIONS OF PRICE SPIKES ................ 8**|
|**2.3** **WELFARE IMPACT OF HIGH SUGAR PRICES: EFFECTS ON HOUSEHOLD INCOME AND**|
|**POVERTY INCIDENCE ............................................................................................................. 14**|
|**2.4** **CONCLUDING REMARKS ON SUGAR ............................................................................. 17**|
|**3.**<br>**THE MAIZE SECTOR ................................................................................................. 18**|
|**3.1** **GOVERNMENT INTERVENTIONS IN THE MAIZE SECTOR HAVE LED TO HIGHER PRICES**<br>**19**|
|**3.2** **HOUSEHOLDS IN THE MAIZE SECTOR: CONSUMERS AND PRODUCERS ...................... 23**|
|**3.3** **WELFARE IMPACT OF HIGH MAIZE PRICES: EFFECTS ON HOUSEHOLD INCOME AND**|
|**POVERTY INCIDENCE ............................................................................................................. 25**|
|**3.4** **ACTIONS BY THECOMPETITIONAGENCY IN THE MAIZE SECTOR ............................ 28**|
|**3.5** **CONCLUDING REMARKS ON MAIZE .............................................................................. 30**|
|**4.**<br>**CONCLUDING REMARKS ........................................................................................ 31**|
|**REFERENCE LIST .............................................................................................................. 33**|
|**APPENDIX ............................................................................................................................ 36**|
|**A1 SUGAR PRICE CALCULATIONS ......................................................................................... 36**|
|ESTIMATING IMPORT PARITY PRICES....................................................................................... 36|
|ESTIMATING BENCHMARKS TO WHICHKENYAN SUGAR PRICES CAN BE COMPARED.............. 37|
|**A2 ESTIMATION OF ELASTICITIES ........................................................................................ 39**|
|**A3 THEINTERNATIONALSUGARMARKET .......................................................................... 40**|

1
17 gender of the head of household remains the same, with female‐headed households showing higher expenditure shares.

Table 3: Share of total household cash expenditures spent on electricity

||A|ll househol|ds||Poor||Househ|old head|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Urban|Rural|Total|Female|Male|
|Botswana|6.3|7.9|6.6|9.6|13.3|10.4|7.0|6.3|
|Burkina Faso|5.4|5.0|5.3|6.8|5.1|6.2|5.4|5.3|
|Cote d'Ivoire|2.6|2.9|2.7|3.3|3.8|3.5|2.7|2.6|
|Ethiopia|3.1|2.1|2.7|4.9|3.2|4.2|3.4|2.4|
|Ghana|2.6|2.3|2.5|3.3|2.9|3.1|2.6|2.5|
|Malawi|0.3|0.3|0.3|0.4|0.4|0.4|0.4|0.3|
|Mali|2.9|1.7|2.4|2.4|1.6|1.8|2.7|2.4|
|Niger|3.4|2.8|3.3|3.1|2.2|2.5|3.7|3.2|
|Nigeria|3.7|3.9|3.8|4.7|4.6|4.7|4.4|3.7|
|Rwanda|1.4|1.8|1.6|2.4|4.3|3.5|1.7|1.5|
|São Tomé and Príncipe|2.0|2.0|2.0|2.3|2.2|2.3|1.9|2.1|
|Senegal|3.8|3.8|3.8|3.7|3.9|3.7|3.8|3.8|
|Sierra Leone|4.5|10.7|4.6|4.9|15.5|5.3|4.5|4.6|
|Swaziland|9.8|9.9|9.8|14.3|12.1|12.8|10.4|9.4|
|Tanzania|3.4|4.3|3.5|2.0|7.2|3.7|4.0|3.4|
|Togo|3.2|3.5|3.2|3.7|4.0|3.8|3.1|3.2|
|Uganda|2.6|2.2|2.5|—|4.8|4.8|2.8|2.4|
|Zambia|5.7|7.0|5.8|8.8|7.9|8.7|6.1|5.7|
|Median|3.3|3.2|3.3|3.7|4.2|3.8|3.6|3.2|

_Source_ : World Bank staff calculations using household survey data.

_Note_ : Only households with positive expenditures on electricity are analyzed. Total household expenditures exclude imputed values of freely acquired items, largely food.

— = no households with positive cash expenditures on electricity.

When the shares in Table 2 and Table 3 are compared, they increase more in rural areas than in urban areas when imputed values are excluded from household expenditures. The differences also predictably decrease with increasing quintile. Both demonstrate greater reliance on home‐grown food by the poor and rural households. Nationally, the largest increase was found in Nigeria (53 percent increase), followed by Ethiopia (21 percent), and Burkina Faso (18 percent). There was little difference between female‐ and male‐headed households, indicating comparable dependence on home‐grown food.

# Charges for Grid Electricity Use and Connection

How expensive is it to buy electricity? Are tariffs designed to make electricity affordable to the poor? Do connection charges take the ability to pay into account, or is every residential customer charged the same?

This study collected information on residential tariffs for grid electricity in effect in July 2014 in 39 countries, including the 22 countries with household survey data. Table 4 summarizes tariff concessions available to those consuming little electricity in the 39 countries. The column labeled kWh shows the size of the lifeline block (the first block in a tariff schedule with a low price per kWh, intended to help the poor consume electricity) or, in the absence of an explicit lifeline block, the first block if there are two or more blocks. The column labeled “ratio” calculates the ratio of the unit energy charge (price per kWh) in the second block to that in the first block, or if there are two or more schedules of a single block each with increasing levels of
# **ABBREVIATIONS AND ACRONYMS**

|ACIM|Association of Media Investigation|
|---|---|
|ATM|Automated Teller Machine|
|CADES|Special District Attention Centers (_Centros de Atención del_<br>_Distrito Especial_)|
|CAV|Savings and Loans Associations (_Cajas de Ahorro y_<br>_Vivienda_)|
|CCO|Check Cashing Outlet|
|CD|Certificate of Deposit|
|CDAT|Saving Term Deposit (_Certificado de Depósito de Ahorro a_<br>_Término_)|
|CDT|Term Deposit (_Certificado de Depósito a Término_)|
|CEDE|Center for Economic Development Studies (_Centro de_|
||_Estudios sobre Desarrollo Económico_)|
|COP|Colombian Pesos|
|CRA|Community Reinvestment Act|
|DANE|National Administrative Statistics Department (_Departamento_<br>_Administrativo Nacional de Estadística_)|
|ECH|Household Survey (_Encuesta Continua de Hogares_)|
|EGM|General Media Survey (_Encuesta General de Medios_)|
|FDIC|Federal Deposit Insurance Corporation|
|FFIEC|Federal Financial Institutions Examination Council|
|FRB|Federal Reserve Board|
|GDP|Gross Domestic Product|
|IOEs|Instituciones Oficiales Especiales|
|IVA|Value Added Tax (_Impuesto sobre el Valor Añadido_)|
|LAC|Latin America and the Caribbean|
|NCUA|National Credit Union Administration|
|NPL|Non-Performing Loans|
|OCC|Office of the Controller of the Currency|
|OECD|Organization For Economic Cooperation And Development|
|OTS|Office of Thrift Supervision|
|US|United States|
|UVR|Real Value Unit (_Unidad de Valor Real_)|

3
<!-- Start of picture text --> Production and capacity utilization rates Manufact eopany pezation ourabl<br>110 . anufacturing: Durable and Non Durable<br>United States, 1972-2011 85 105<br>Latte oO= 100 * At Fi<br>= 95 1. ot<br>90 “ ee" . 753rn 90 a ao ry "| XY<br>: f° He here<br>y Oo 85<br>80 we 70<br>ans vu<br>Fa 80<br>a<br>70 69 75<br>2006-06 2007-06 2008-06 2009-06 2010-06 2006-03 2007-03 2008-03 2009-03 2010-03<br>— Production, Total index, base 100 in 2007 — Manufacturing<br>Production, Durable manufacturing, base 100in 2007 Durable manufacturing<br>beastaesanaee i ilizati i seeeeeees Nondurable manufacturin;<br>Capacity Utilization Rates, Total Index (right) Source: Board of Gavemrorsofthe red i<br>Employment Levels Since Peak in January 2008 Employment Levels Since Peak in January 2008<br>Total Employment in Private Sector: Goods vs Services Manufacturing and Construction<br>;<br>2 LO —PePe _<br>a SX a<br>" NO —<br>2008m1 2009m1 time;; 2010m4 2011m1 2008m1 2009m1 time;; 2010m1 2011m1<br>— Total Private ————— Goods Producing —— Manufacturing ——— Construction<br>——— Services ——— Durable goods ———— Non Durable goods<br>Source: author's calculations based on Bureau of Labor Statistics Source: suthor's calculations based on Buresu of Labor Statistics<br><!-- End of picture text -->

<!-- Start of picture text --> Employment Levels Since Peak in January 2008<br>Manufacturing and Construction<br>2 LO —PePe _<br>SX a<br>NO —<br>2008m1 2009m1 time;; 2010m1 2011m1<br>—— Manufacturing ——— Construction<br>——— Durable goods ———— Non Durable goods<br>Source: suthor's calculations based on Buresu of Labor Statistics<br><!-- End of picture text --> <!-- Start of picture text --> Employment Levels Since Peak in January 2008<br>Total Employment in Private Sector: Goods vs Services<br>;<br>a<br>"<br>2008m1 2009m1 time;; 2010m4 2011m1<br>— Total Private ————— Goods Producing<br>——— Services<br>Source: author's calculations based on Bureau of Labor Statistics<br><!-- End of picture text -->
# **Table 5: Largest Currency Pairs in Crypto Vehicle Trades**

|Origin Currency|Destination Currency|Trade Volume as share of total<br>identified vehicle trade volume in<br>Origin Currency (in %)|
|---|---|---|
|Russian Ruble|Russian Ruble|89.8|
|Venezuelan Bolivar|Venezuelan Bolivar|51.7|
|Yuan Renminbi|Yuan Renminbi|84.0|
|US Dollar|US Dollar|60.1|
|Euro|Euro|76.5|
|Colombian Peso|Colombian Peso|62.1|
|Pound Sterling|Pound Sterling|76.9|
|US Dollar|Nigerian Naira|24.3|
|Nigerian Naira|Nigerian Naira|26.3|
|Colombian Peso|Venezuelan Bolivar|24.8|

Source: LocalBitcoins.com API, Authors’ Calculations Out of the 10 top crypto vehicle trades listed in Table 5, two are cross border: US dollars to Nigerian naira, and Colombian pesos to Venezuelan bolivars, both likely capturing remittance flows around fairly strict Venezuelan controls (although these can also represent official Venezuelan entities avoiding US sanctions.) Figure 5 below and Table A1 in the Appendix expands the list to the top 10 cross currency crypto vehicle trades, with Peru, Chile and Argentina alongside Colombia, Venezuela and Nigeria.

23
where subscript _i_ denotes the firm, _j_ the industry (2-digit ISIC rev. 3.1) to which the firm belongs, _t_ denotes ES survey round (latest vs. first round), and _k_ denotes the country where the firm is located. The dependent variable, _Y_ , is the percentage of female workers at the firm; _Exports_ , our main explanatory variable, is a measure of exporting activity of the firm (defined in detail below); CIFE is a set of dummy variables indicating the country-industry group to which the firm belongs (Country-Industry fixed effects); YFE is year fixed effects where year is the calendar year covered by the ES; Firm Controls and Country Controls include the various controls used and discussed in detail below; u is the usual error term.

As discussed above, we go beyond and explore how the relationship between female workers and exports varies depending on several country and industry characteristics. These heterogeneities are estimated using the following equation: Equation (2) differs from equation (1) in two ways. First, it includes the interaction term between our exports variable and country and/or industry characteristics captured by Z. These interaction terms estimate how the relationship between female workers and exports varies with factors such as the dependence of sectors on female workers, level of competition in the domestic markets, social attitudes towards women’s right to scarce jobs and other rights, law & order situation, and gender disparity in labor laws. The interaction terms are included one by one (separate regressions) 10