rafmacalaba/datause-encoder-data huggingface dataset:
open a savings account, corresponding to 14% of one minimum monthly wage. Additional costs for ancillary services such as a registration book and monthly administrative fees range from an additional 1.3% to 8.1% of a minimum salary, bringing the potential requirements for opening a savings account up to 26.5% of one minimum monthly salary. These costs rapidly decline as a percentage of income for higher income earners, falling to a significantly lower 4.1% of an average monthly income in the fifth income quintile. Excluding the minimum balance requirements, the monthly cost of a savings account still amounts to 12.6% of a minimum income. This represents high costs for the majority of the unbanked, who earn less than one minimum wage, effectively making even the most basic financial service hard to afford.

**Table 2. 2  Costs of savings services by income quintiles**

|||% half<br>min. wage|% min.<br>wage|% Quintile<br>1|% Quintile<br>2|% Quintile<br>3|% Quintile<br>4|% Quintile<br>5|
|---|---|---|---|---|---|---|---|---|
|Average monthly<br>||179,000|358,000|379,241|727,087|846,080|917,584|2,321,841|
|income (in COP)|||||||||
|**Savings account**|||||||||
|Min. balance|50,000|27.9%|14.0%|13.2%|6.9%|5.9%|5.4%|2.2%|
|Registration book|29,082|16.2%|8.1%|7.7%|4.0%|3.4%|3.2%|1.3%|
|Monthly admin. Fee|4,808|2.7%|1.3%|1.3%|0.7%|0.6%|0.5%|0.2%|
|Debit card|5,563||1.6%|1.5%|0.8%|0.7%|0.6%|0.2%|
|processing||3.1%|||||||
|Other monthly fees|5,500|3.1%|1.5%|1.5%|0.8%|0.7%|0.6%|0.2%|
|**TOTAL**|**94,953**|53.0%|**26.5%**|**25.0%**|**13.1%**|**11.2%**|**10.3%**|**4.1%**|
|**Excl. Min. balance**|44,953|25.1%|12.6%|11.9%|6.2%|5.3%|4.9%|1.9%|

_Source: Superintendencia Bancaria; Encuesta de Calidad de Vida 2003_ 13. **Real interest rates for small savings amounts are frequently negative** . The average interest rate paid by banks on savings accounts ranges from 2-7%, depending on the bank and the type of service provided (see Figure 2.3). The majority of banks do not pay interest below a certain minimum balance. For example, BanColombia, the largest bank in the country, starts paying interest on savings accounts on a minimum balance of COP300,000. Factoring in inflation of 7% in 2003, the real interest rate for small deposits is therefore negative. This provides limited incentives for savers to convert informal savings into savings with the formal banking sector. Focus groups confirmed that low income people generally do not trust formal financial institutions as they feel that they do not receive any remuneration on savings accounts, and fees are too high<sup>31</sup> .

> 31 Marulanda, 2004.

36
Table B1: Aggregates of corporate profits from tax records

||Number|Aggregate profts|Share with|Profts net|Profts matched|Profts matched|
|---|---|---|---|---|---|---|
|o|f Corporations|(L billion)|negative profts|of dividends<br>(L billion)|to individuals<br>(L billion)|to individuals w/ valid ID<br>(L billion)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|2003|10,972|8,037|0.30|8,037|5,758|4,433|
|2004|11,774|11,909|0.30|11,909|8,497|5,465|
|2005|12,399|17,359|0.29|17,359|13,338|7,369|
|2006|13,247|23,431|0.29|23,431|19,192|8,548|
|2007|14,628|26,171|0.27|26,171|23,322|8,507|
|2008|15,674|21,221|0.29|21,221|18,479|4,485|
|2009|16,304|18,831|0.34|18,831|13,991|2,941|
|2010|17,370|22,988|0.27|22,976|15,486|5,064|
|2011|18,293|24,991|0.25|20,059|19,979|6,048|
|2012|19,147|24,015|0.23|14,397|18,657|6,534|
|2013|20,258|26,440|0.22|18,825|23,572|8,263|
|2014|21,594|27,655|0.23|23,102|26,729|8,871|
|2015|24,935|43,335|0.22|37,766|34,424|11,884|
|2016|27,146|52,673|0.20|45,407|40,541|16,371|
|2017|29,648|56,270|0.21|43,822|48,091|19,476|
|2018|32,183|45,868|0.24|32,242|41,520|11,299|
|2019|33,704|56,362|0.24|46,312|47,958|17,347|

_Note:_ This table presents information on corporate profits from tax records. Number of corporations refers to the number of firms filing CIT each year. Aggregate profits is the sum of taxable profits (in billion Lempiras) from all CIT filers. Share with negative profits is the percentage of filers declaring negative income. Profits net of dividends is the total amount of profits minus total amount of distributed dividends. Profits matched to individuals refers to the amount of profits net of dividends that can be attributed to some shareholders using the shareholder database - this number can be larger than column (4) since profits can be negative. Finally, ”profits matched to individuals with valid ID” refers to the total profits net of dividends that are attributed to a shareholder with valid national ID - that excludes foreign corporations and foreign shareholders, for example, which do not have a national ID.

# **B.2 Creation of shareholder list**

Once we compute net-of-dividends corporate profits for each period, we need to assign them to each shareholder in proportion to their equity in firms. We do so by using a detailed list of corporate shareholders and their shares.

No consolidated and updated shareholder list of corporations exists in Honduras, so we worked with the Tax Authority to build one. We use two main datasources: Annex B of the ”professional relations table”, a document firms file when they are incorporated, listing all relevant parties in the firm and including participation of shareholders; and the withholding form for dividends DMR 113, under the assumption that individuals receiving dividends are shareholders even if not listed in the Annex B. We emphasize that the quality of Annex B information is far from perfect: there is no formal validation of the information imputed, including validating the tax id (RTN) of 49
**Table A.4. Overview of the follow-up** **<u>periods covered, by survey</u>**

|Years since baseline|BENINFORM|EGYPTMACROINS|GHANAFLYP|IFLS<br>KENYAGETAHEAD|MALAWIFORM|MXFLS|NGLSMSISA|NGYOUWIN|SLKFEMBUSTRAINING|SLKINFORMALITY|SLLSE|SLMS|TOGOINF|TTHAI|UGWINGS|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|0.25|||x||||||x|||x||||
|0.5||x|x||||x|||||x||||
|0.75|||x|||||||||x|x|||
|1|x||x|x|x|||x|x||x|x||x||
|1.25|||x|||||||||x|x|||
|1.5|||||||x||||x|x|||x|
|1.75|||||x|||x|x|x||x|x|||
|2|x||||||x||||x|x||x||
|2.5||||x|||x|||x|x|x||||
|3|||||x|x||||x|x|x|x|x||
|3.5|||||x|||x|||x|||||
|4|||||||||||x|||x||
|4.5||||||x|||||x|||||
|5||||||||||||x||x||
|5.5|||||||||||x|x||||
|6|||||||||x|||||x||
|7||||x||||||||||x||
|8||||x||x||||||||x||
|9||||||||||||||x||
|10||||||||||||x||x||
|11||||||||||||x||x||
|12||||||||||||||x||
|13||||||||||||||x||
|14||||||||||||||x||
|15||||x||||||||||x||
|16||||||||||||||x||
|17||||||||||||||x||

71
# **4.1 Ivory price data**

The analysis relies on a database of raw ivory prices that contains 4,873 price data points covering the time period 1970-2014 and spanning 72 countries. Details on the data collection methodology can be found in the online appendix. In a nutshell, the data are compiled from published reports and papers, websites, government and private sector proprietary data, and ivory seizure data. Prices are observed at various segments of the value chain (poachers, importers, middlemen, retailers, etc.) and are mostly for African elephants, with a few observations on Asian elephant or mammoth ivory.

To construct a country-year dataset on ivory prices from these 4,873 observations, we proceed in three steps. First, we convert the prices into U.S. dollars and deflate them so that all prices are in 2005 constant U.S. dollars. Then, since the heterogeneity in data sources induces variability in observed prices along observable dimensions, we partial out the effects of value chain, species, source and price category by regressing the logarithm of the price on these four groups of dummy variables and taking the residuals from the regressions. We conduct this exercise separately for Sub-Saharan countries and China. Lastly, for each country and year, we take the median of these residuals to form our final dataset on ivory prices.

Figure 1 plots median prices in each year in Africa and in China over time; the dotted line plots gold prices, which are discussed below. We highlight two stylized facts. First, the prices in producing (solid line) and consuming countries (dashed) closely track each other. The differences between the two, beyond measurement error, are possibly driven by shipping, smuggling costs, and the increased concentration in the ivory trafficking sector. We investigate this relationship in greater detail below.

11
# **Table 6:  Large Changes in Governance, 1998-2006, Cont'd**

||**Gove**<br>|**rnance S**<br>|**core**||||**Agree/**||||
|---|---|---|---|---|---|---|---|---|---|---|
||**2006**<br>**(Level)**|**1998**<br>**(Level)**|**Change**|**Agree**|**No**<br>**change**|**Dis-**<br>**agree**|<br>**(agree+**<br>**Disagree)**|**Sources**<br>**Added**|**Balanced**<br>**Change**|**Bal Chng/**<br>**Actual Chng**|
|**Rule of Law**|||||||||||
|ZIMBABWE|-1.71|-0.47|-1.24|9|0|1|0.90|8|-1.18|0.95|
|VENEZUELA|-1.39|-0.69|-0.70|10|1|1|0.91|6|-0.70|0.99|
|ARGENTINA|-0.58|0.03|-0.61|8|2|2|0.80|5|-0.66|1.09|
|TRINIDAD AND TOBAGO|-0.26|0.36|-0.62|5|1|1|0.83|4|-0.53|0.87|
|COTE D'IVOIRE|-1.51|-0.90|-0.61|5|2|1|0.83|3|-0.73|1.18|
|ERITREA|-0.99|-0.22|-0.78|4|0|0|1.00|5|-0.57|0.74|
|ALGERIA|-0.63|-1.17|0.54|5|1|3|0.63|6|0.37|0.70|
|GEORGIA|-0.61|-1.18|0.57|3|3|1|0.75|10|0.34|0.59|
|KIRIBATI|0.84|-0.69|1.53|1|0|0|1.00|3|0.63|0.41|
|LIBERIA|-1.16|-2.07|0.91|4|0|0|1.00|3|0.78|0.86|
|TAJIKISTAN|-1.06|-1.74|0.68|4|2|0|1.00|11|0.66|0.97|
|SERBIA|-0.59|-1.29|0.71|3|1|1|0.75|9|0.61|0.86|
|RWANDA|-0.59|-1.47|0.88|5|0|0|1.00|6|0.59|0.67|
|_Average_||||_5.08_|_0.94_|_0.84_|_0.88_|_5.92_|||
|**Control of Corruption**|||||||||||
|ZIMBABWE|-1.36|-0.42|-0.94|7|0|1|0.88|6|-0.82|0.88|
|COTE D'IVOIRE|-1.16|-0.38|-0.78|5|1|0|1.00|1|-0.78|1.00|
|ERITREA|-0.17|0.73|-0.90|2|1|0|1.00|4|-1.08|1.20|
|TANZANIA|-0.37|-1.07|0.70|6|1|0|1.00|7|0.61|0.86|
|SERBIA|-0.35|-1.08|0.73|4|0|0|1.00|8|0.77|1.06|
|_Average_||||_4.15_|_0.78_|_0.49_|_0.92_|_6.07_|||
|**Overall Average**||||4.36|0.49|0.52|0.91|4.82||0.94|
|||||||||**Cases>1.2**||5|
|||||||||**Cases<.8**||15|
|||||||||**0.8 < Cases**|**< 1.2**|70|

34
Syria’s population and labor force were adjusted downward in order to reflect (a) the loss of life since 2011; (b) the number of Syrian refugees who fled the country during the period from 2011 to 2014;<sup>13</sup> and (c) the number of Iraqi refugees who left Syria during the same period.<sup>14</sup> At the same time, the population and labor force of the refugee-receiving countries were adjusted upward in order to reflect the inflow of refugees from Syria (Table 3). Adjustments were also made in order to reflect the return of Iraqi refugees to their home country during the period 200713. In the absence of information on the skill mix and participation rates of Syrian refugees, we assume that skilled and unskilled workers are equally affected by the war and that labor force participation rates among refugees are respectively the minimum of the participation rates in Syria and each refugee receiving country. The shocks to the Levant countries’ population and labor endowments are shown in Table 3. Although adjustments were made in all countries/regions in the model, we show only those applying to the Levant as the magnitudes for countries outside the Levant are negligible.

**Table 3 Population, labor force & transport cost shocks due to conflict in the Levant (%)**

||||||Transpo<br>|rt costs<br>|||
|---|---|---|---|---|---|---|---|---|
||Population|Labor force|Turkey|Egypt|Jordan|Lebanon|Syria|Iraq|
|Turkey|0.9|0.8|0.0|3.5|-18.3|-35.2|-18.4|-11.1|
|Egypt|0.2|0.1|5.9|0.0|12.2|-10.2|2.5|3.6|
|Jordan|2.5|2.3|-19.5|11.1|0.0|-32.9|-15.7|-8.5|
|Lebanon|19.5|15.4|-16.2|10.9|-11.3|0.0|-13.4|-7.3|
|Syria|-20.7|-19.0|-23.8|-4.9|-20.2|-33.4|0.0|-12.5|
|Iraq|7.3|7.6|-9.2|4.2|-6.5|-15.0|-8.1|0.0|

We use the World Bank’s Doing Business data on the costs of importing and exporting a standard container and ESCAP World Bank International Trade Costs data on international

> 13 We make this adjustment in order to assess accurately the medium-term effects of war and reflect the fact that the majority of Syrian refugees plan to return to Syria only upon the fall of the Assad regime. 14 We assume that all Iraqi refugees in Syria have gone back to Iraq.

17
in the year of the capital raising or in the following year. Third, Chinese and Indian firms represent a relatively large fraction of the sample (about 16%) and the patterns documented in this paper are similar in these two countries (Didier and Schmukler, 2013). However, the results are qualitatively similar to the ones reported here when excluding China and India, which indicates that these countries are not driving the results. Fourth, the results are robust when considering only the second half of the sample, namely 2006-2010. Fifth, the results are also qualitatively similar to the ones reported here when using the Worldscope balance sheet database that covers a longer time span (1995-2011) but with fewer firms. In particular, we estimated the regressions over three different sample periods (19952011, 1995-2002, and 2003-2011) to verify the robustness of our findings to these different partitions.<sup>17</sup> Sixth, although the analysis considers both IPO (initial public offerings) and non-IPO capital raising activity, the results are robust to the exclusion of IPO capital raising activity. Seventh, the reported estimates from quantile regressions use bootstrapped standard errors with 400 replications clustered at the firm level. The results are robust to the alternative use of more replications, non-clustered standard errors, and other levels of clustering (country, sector, and country-sector level).

Overall, firms that use capital market financing are larger to begin with, grow faster, especially in the year of issuance, and become larger than non-issuing firms. There is not only divergence in size between issuing and non-issuing, but also among non-issuing firms, as the growth rates of non-issuing firms increase with firm size. However, among issuing firms, the growth rates decrease monotonically with firm size, indicating convergence in size among them. Furthermore, the growth differential between issuing and non-issuing firms is significantly larger for smaller firms and declines with firm size.

> 17 This addresses some of the potential problems that could arise with the Orbis database, although those problems are mostly linked to the non-listed firms for which data are not as consistently reported.

21
Haggblade, S, Hazell, P. and Reardon, T. (2007) _Transforming the Rural Nonfarm Economy._ Baltimore: Johns Hopkins University Press and International Food Policy Research Institute.

Hymer, S. and Resnick, S. (1969) "A Model of an Agrarian Economy with Nonagricultural Activities," _American Economic Review_ 59: 493-506.

- Johnston, B. and Mellor, J. (1961) “The Role of Agriculture in Economic Development” _The American Economic Review,_ 51:566-593.

Jones, C.I. (2005) “The Shape of Production Functions and the Direction of Technical Change.” _The Quarterly Journal of Economics_ , 120(2):517-549.

Jovanovic, B. (1982) “Selection and the Evolution of Industry” _Econometrica_ 50(3): 649-670.

Kinda, T. and J. Loening. 2008. “ Small Enterprise Growth and the Rural Investment Climate: Evidence from Tanzania.” _World Bank Policy Research Working Paper_ 4675.

Kedir, A. and Girma, S. (2007) "Quadratic Engel Curves with Measurement Error: Evidence from a Budget Survey" _Oxford Bulletin of Economics and Statistics_ 69(1): 123-138.

Krugman, P. (1991) "Increasing Returns and Economic Geography" _Journal of Political Economy_ 99(3) pp. 483-499.

Krugman, P. (1998) "What.s New about the New Economic Geography" _Oxford Review of Economic Policy_ 14(2), pp7-17.

Lanjouw, J. And Lanjouw, P. (2001) “The Rural Non-Farm Sector: Issues and Evidence from Developing Countries” _Agricultural Economics,_ 26(1):1-23.

Loening, J., Rijkers, B., and Söderbom, M. (2008) "Non-farm Microenterprises and the Investment Climate: Evidence from Rural Ethiopia" World Bank PolicyResearch Working Paper 4577.

Mead, D. C. and Liedholm, C. (1998) “The dynamics of micro and small enterprises in developing countries” _World Development_ 26(1): 61-74.

-28-
DJSTRIB. OF ANNUALIZED MARKET RET'URN ON INVESTMENT 1 YEAR PERCENTAGE 40 <!-- Start of picture text --> 30<br>20<br>o11Nii<br>-4 -2 0 2 4 6 8 10 12 14 16 18 __E0g 20 22 24 26 ___ 28 30<br>RATE OF RETURNM( ) 01<br>IF AATE OF RETURN LE --4 THEN MIDPOINT=-4<br>IF llAIE OF RETURN bi 30 THEN MIDPOINT=30<br><!-- End of picture text -->
indicator of chronic malnutrition—and child survival rates are correlated with income across and within countries. Poor nutrition impairs children’s capacity to learn. Poor children may also be kept out of school in order to support low family incomes through home production or informal work or because families cannot afford school fees. Students from poor households have higher learning gaps even when attending school (World Bank 2018). Empirical evidence shows that inequality of wealth, not just inequality of income, reduces the effectiveness of educational interventions (Deininger and Olinto 2000).

As described in Section 2, lower-income countries experience higher poverty rates, partly reflecting the correlation between average country income and the income of the bottom of the distribution. Poor countries have weak capacity to supply public goods such as health and education. Indeed, public spending on health and education is lower for countries with high poverty rates (Figure 10, panel a and b). Higher poverty is associated with lower access to doctors and higher illiteracy rates (Figure 10, panel c and d).

Inequality in education attainment can undermine growth as economies develop (Galor and Moav 2004). In the initial stage of development when physical capital is the prime source of growth, inequality raises growth because it channels resources to individuals with a higher propensity to save. This is reversed later in the development process: as human capital replaces physical capital as the main engine of growth, more equality leads to growth as it alleviates adverse effects of credit constraints on human capital accumulation.

29
## **Reasons for loans reported by borrowers in developing economies**

loan in the past year.

Adults with an outstanding loan for purpose specified (%) <!-- Start of picture text --> Funeral or wedding 3<br>Purchase of home or apartment 3<br>Home construction 5<br>School fees 5<br>Emergency or health purposes 11<br>Note: Respondents could report borrowing for more than one purpose.<br>Source: Demirguc-Kunt and Klapper 2012.<br><!-- End of picture text --> Looking at the origination of new formal loans by education level across income quintiles reveals interesting patterns. In developing economies the share of adults with at least some tertiary education who report having originated a new formal loan in the past year remains fairly steady across income quintiles; the gap between the poorest and richest quintiles is only about 3 percentage points on average. The story is similar for those with a primary or secondary education. The challenges of using formal credit in developing economies appear to affect the wealthy as well as the poor.

But in high-income economies adults in the richest quintile with at least some tertiary education are more than twice as likely on average to report a new formal loan as those in the poorest quintile with the same education level; the gap averages about 15 percentage points. As in developing economies, however, the likelihood of having a new formal loan in the past year differs little across income quintiles among adults with lower education levels.

All this means a large gap in the origination of new loans among wealthy adults with different education levels in high-income economies. What explains this gap? The difference may indicate lower creditworthiness or less demand for loans among less educated adults. Or it may suggest that less ability to understand complex loan terms and navigate the loan process could be a barrier to the use of formal credit.

# **What are the main purposes for loans?**

Why are people most likely to borrow? Data gathered in developing economies show that emergency or health purposes are the most common reason for having an outstanding loan (figure 3.2).<sup>3</sup> This is especially so in Cambodia, Guinea, Madagascar, Sudan, and the Republic of Yemen, where more than 30 percent of adults report having an outstanding loan for such purposes. Emergency and health loans are also more commonly reported among those in the poorest income quintile: on average in developing economies, 14 percent of adults in the poorest quintile had a loan for emergency or health purposes, compared with 8 percent of those in the richest.

Outstanding loans for school fees are most common in Sub-Saharan Africa, reported by 8 percent of adults in that region. Outstanding loans for funerals or weddings are reported by 3 percent of adults in the developing world as a whole. But they are significantly more common in fragile and conflict-affected states such as Afghanistan (29 percent), Iraq (13 percent), Somalia (11 percent), and West Bank and Gaza (11 percent).

**39**

MEASURING FINANCIAL INCLUSION
Figure 5.1- Toxic Pollution Intensity by Medium for Selected Sectors

| | t

|

<!-- Start of picture text --> 30000<br>25000 | Hi<br>ae= 20000| - . -<br>2] Bby Air<br>3 = Oby Water<br>Fe = W by Land.<br>27<br>2<br>ce 1s000 | a-<br>A =<br>9<br>E<br>3S<br>3o 10000 } oe an<br>o i<br>- he i<br>04<br>- = o yd oS = fo] TM — Nw [=] a<br>® § 5&5 8 6 $$ § 8 8 8 $8 8<br>ISIC Codes |<br>. !<br><!-- End of picture text -->
# **7 Conclusion**

In this paper, we have considered the effects of minimum wages on employment and hours worked in Mauritius. Using data from labor force surveys conducted between 2004 and 2014, we assigned minimum wages to workers covered by 30 categories of remuneration orders and estimated how changes in these minimum wages affected employment and hours of work over the period 2004 – 2014.

Our 2SLS estimates suggest a slightly positive effect of minimum wages on employment in the covered sector where remuneration order specified minimum wages are applicable. The employment elasticity of 0.113 is within the range of elasticities found in previous studies of employment effects of minimum wages in LMI countries and confirms the findings of those studies that increases in minimum wages have benign effects on employment.

We also find that a 10 percent increase in minimum wages was associated with a 2.3 percent increase in average work hours for males in the covered sector but a 1.8 percent decline in average work hours for females in the covered sector. This suggests that industries in which females are more likely to be represented, such as domestic work and services, may be responding to increased minimum wages by decreasing the number of hours worked by female employees rather than firing workers, despite the overall positive employment effect.

In the uncovered sector, a significant hours of work effect, driven by an increase in average male work hours, is found for the sector overall with a 10 percent increase in the minimum wage being associated with an increase in average number of hours worked of 4.2 percent. It seems then that increases in the minimum wage have the effect of increasing average work hours for males in both the covered and uncovered sectors.

We caution, however, that while the overall estimated employment effects seem to be benign, as noted by Bhorat, Kanbur and Stanwix (2015), this may be the result of context-specific factors which interact with the minimum wage to ensure that the effects are on average very small. Ultimately, the impact of any enforced change in wage levels on any particular sector depends on a range of factors. These include: the level of the minimum wage relative to average wages, the size of the wage increase, the sector under consideration, the timing of wage changes, the change post-law in worker productivity levels, and finally, enforcement and compliance.

21