rafmacalaba/datause-encoder-data huggingface dataset:
|**US$**<br>**(%)**|**Produced**<br>**capital**|**Natural**<br>**capital**|**Net foreign**<br>**assets**|**Human**<br>**capital**|**Intangible**<br>**residual**|**Total**<br>**wealth**|
|---|---|---|---|---|---|---|
|ROU|14,292<br>(17.7)|8,527<br>(10.5)|-1,403<br>(-1.7)|45,078<br>(55.7)|14,411<br>(17.8)|80,906<br>(100)|
|ESP|82,194<br>(20.1)|7,033<br>(1.7)|-11,999<br>(-2.9)|289,929<br>(71.0)|41,227<br>(10.1)|408,385<br>(100)|
|GBR|84,861<br>(12.8)|5,896<br>(0.9)|-7,290<br>(-1.1)|404,239<br>(61.0)|174,919<br>(26.4)|662,624<br>(100)|
|USA|100,075<br>(13.6)|13,012<br>(1.8)|-6,947<br>(-0.9)|409,524<br>(55.8)|218,532<br>(29.8)|734,195<br>(100)|

Source: Author’s calculations.

As indicated in the table, human capital accounts for more than half of total national wealth for all the selected countries. This finding is consistent with the results found by both Ferreira and Hamilton, (2010) and national studies for Canada (Gu and Wong, 2008) and Norway (Greaker, 2008).

# **_5.1. Discussion_**

Figures 2 and 3 summarize the key results from the wealth decomposition, with the focus being put on the shares of human capital and of the intangible residual in the total wealth of each country.

**Figure 2. Human capital share of total wealth, 2005**

<!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Authors’ calculations 11
were both denoted with a period. If important variables such as sales, value added, number of employees, and cumulated investment that should not be zero were missing for at least two consecutive years, it was assumed that the firm went out of business and was dropped from the sample for the years when the values were missing. This was later checked with independent information on firms that went out of business and our decision rule was observed to be very accurate. We assumed that firms with a period for exports did not export.

As calculated sous-branche level data from the summation of individual firm data, we were careful to aggregate only firms with complete records on all variables used in the analysis. Thus, if data on the number of employees was missing, we did not add that firm's domestic sales to the total domestic sales in the sous-branche. Even with this rule, the sous-branche level data that we constructed proved to be very close to the sous-branche level data published by the Banque des Donnees Financieres. For example, for 12 out of 30 sous-branches we had exactly the same value of domestic sales. For only three sectors, did our selection rule result in less than 80 percent of the value of domestic sales.

# _II. THE PRICE DA TA._

We used 6 price indices, for:

- Export Prices

- Import Prices

- Output Prices

- Intermediate Good Prices

- Cost of Capital

- Cost of Labor

All prices, except for labor costs, were provided by the Direction de la Statistique of the Ministere du Plan et de l'Industrie. The labor costs were calculated with firm data from the BDF. Exports, Imports, Output Price and Labor Costs are available at the sub-sector (3 digits) level. The Intermediate Good Price Index is available only at the branch level (2 digits) and the Capital Price Index is common to the whole economy.

For some years and some sectors the export price index, as reported by the Direction de la Statistique, increases or decreases dramatically by up to 100 % from one - 32 -
are 7% of AEL for the strategy with contingent credit and 42% of AEL for the strategy without, a decrease of 35% of AEL.

These strategies demonstrate the main advantages and disadvantages of contingent credit. Contingent credit allows the reserve fund to bear more risk and therefore save on reinsurance costs. However, by bearing more risk the fund exposes itself to the possibility that a series of years with high losses deplete reserves and force a drawdown of contingent debt, before reserves have built up sufficiently.

# **_3.4.Stochastic projections: Probabilities of liquidity crunch and contingent debt drawdown_**

While it is important to analyze individual scenarios to understand the potential effects of contingent credit purchase, these three scenarios are not in any way representative of the likely outcomes. To understand the difference in outcomes between a strategy with contingent credit purchase and one without, we run the CC-DFA model with 5,000 simulations, with losses drawn from the assumed probability distribution.

As can be seen from Figure 8, the strategies incorporating contingent debt do not seem to lead to increases in the probability of a liquidity crunch, despite the increase in retained risk. Indeed, by saving on reinsurance premiums in early years, and therefore facilitating a larger increase in the average growth in reserves in early years, strategies with contingent credit reduce the 10 year probability of a liquidity crunch occurring from 5.8% to 4.4% in the case of India, from 12.7% to 5.1% in the case of Fiji and from 4.4% to 0.0% in the case of Costa Rica.

Figure 8. Probability of uninsured loss exceeding reserves and contingent credit <!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Year 1 2 3 4 5 6 7 8 9 10<br>India: No contingent loan facility India: 25% contingent loan facility<br>Fiji: No contingent loan facility Fiji: 35% contingent loan facility<br>Costa Rica: No contingent loan facility Costa Rica: 1,100% contingent loan facility<br>end of this year<br>Probability of liquidity crunch by the<br><!-- End of picture text --> The cumulative probability that part of the contingent credit has been drawn down increases as time goes on (see Figure 9). The probability of drawdown in early years is particularly low since we assume that the facilities begin with substantial reserves and receive annual net allocations from government that allow significant purchase of reinsurance. In early years losses have to exceed the reinsurance exhaust point to trigger a drawdown of contingent credit. In later years, the probability of drawdown is much higher since drawdown can be triggered by a series of years with reasonably high losses, a much more likely event a very high loss occurring in any one year. The probability that 17
private flows more than doubled in the last two decades. In this period, low-income countries also received higher amounts of capital flows as a share of their GDP, but unlike the MICs, they received mostly official flows (Figures 9 and 10).

<!-- Start of picture text --> Figure 9.Total Net Capital Flows<br>as a Share of GDP<br>* Middlc-income contnefs * Other devwloping countries<br>5 6 5 5<br>4<br>E4 2 9<br>3 22<br>'-1 19F9S 19 sss6.1970s<br>Source: World Bank data.<br><!-- End of picture text --> <!-- Start of picture text --> Figure 10. Private Net Capital Flows<br>as a Share of GDP<br>U Medle-irmme conries U Other devloping tountries<br>6<br>4~~~~~~~~~~~'4<br>E 3 o<br>1 ~~~~~~~~~19 4<br>1990s 1990-96 1996-9h<br>Source: World Bank data<br><!-- End of picture text --> 39. While the MICs received around 95 percent of private capital flows to the developing countries during the 1990s, they accounted for only about 11 percent of global private flows in the same period. This is significantly lower than their share in world GDP or exports (about one-fifth), an indicator of their lack of creditworthiness compared to high-income countries. Within MICs, access to international capital markets is highly skewed. Indeed, in 1998, only ten MICs received more than two-thirds-and twenty countries received more than 80 percent-of all capital flows to developing countries.6 The distribution was even more skewed with respect to portfolio capital flows, which provide a better measure of a country's integration into international financial markets. In 1998, the ten top recipients among the MICs accounted for about 80 percent of all equity flows and 90 percent of all bond flows to developing countries. Typically, it is the richest countries among the MICs that have better access to capital **Figure 11. Share in Total Capital Flows** markets, and these countries increased their 4 *970. 19. C 1990s 43 share of total flows in the 1990s (Figure 1 1). The majority of MICs received little capital 30 flows, and the share of the poorest group 21 within MICs has declined in recent years. 13 13 lo 1 1I <!-- Start of picture text --> 4 *970. 19. C 1990s 43<br>30<br>21<br>13 13 lo 1 1I<br>10 _<br>o _ _ I _ L .<br>Source Global Development Finance<br><!-- End of picture text --> 40. The number of MICs rated by Standard and Poor's doubled between 1995 and 2000 from 23 to 52 countries. But only a handful of these countries are rated investment grade. The majority of the MICs still have little access to international capital markets, or if they do have access, they have to pay a high price for raising finance.

6 Top ten capital recipients in 1998 were Brazil, China, Mexico, Russian Federation, Argentina, Korea, Poland, Chile, Thailand and Malaysia.

14
# **2. Data and Main variables**

The data we use are a cross-section of 107 countries for which data are available on all our variables. The estimation method used is Ordinary Least Squares (OLS) with Huber-White robust standard errors. A formal definition of all the variables used in the regressions is provided in Table 1 and summary statistics of the variables are provided in Table 2.

# 2.1 _Dependent variable_

The dependent variable is real GDP per capita annual growth rate ( _Growth_ ). To eliminate annual fluctuations, we use average values of growth rates taken over 2006 to 2008. The data source for the variable is World Development Indicators, World Bank. The mean value of the dependent variable equals 3.5 and the standard deviation is 2.6. In our sample, Armenia has the highest growth rate at 11.75 while Bahrain has the lowest growth rate at -1.06 percent per annum.

# 2.2 _Main explanatory variables_

We use lagged values of all our explanatory variables in order to avoid reverse causality from the dependent variable (growth) to the various explanatory variables. That is, all the explanatory variables discussed below are for the year 2005 or prior to that.

Our main explanatory variables are a measure of gender inequality in the country, income level, and most importantly, the interaction term between the gender inequality and the income variable. The interaction term measures how the strength of the relationship between gender inequality and growth varies with the level of income.

7
- _nomic Statistics_ , vol. 38, no. 3, 2020, pp. 613–20. https://doi.org/ `10.1080/07350015. 2018.1546591` .

- Keller, Wolfgang. “International technology diffusion”. _Journal of economic literature_ , vol. 42, no. 3, 2004, pp. 752–82.

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- Lerner, Josh, and Amit Seru. “The Use and Misuse of Patent Data: Issues for Finance and Beyond”. _The Review of Financial Studies_ , vol. 35, no. 6, 2021, pp. 2667–704.

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32
|Table A2: Attributes of workers employe|Did not<br>d at exporting|Did not receive<br>manufacturing|Received<br>firms by trai|ning status|
|---|---|---|---|---|
||<br>received any<br>training in<br>sample period|technical<br>upgrading in<br>current or<br>previous years|technical<br>upgrading in<br>current or<br>previous year|<br>s<br>Full sample|
|Log hourly wage|2.130|2.136|2.603|2.154|
||(0.738)|(0.734)|(0.679)|(0.737)|
|Share with schooling less than high school|0.474|0.467|0.237|0.458|
||(0.499)|(0.498)|(0.425)|(0.498)|
|Share with schooling high school|0.415|0.423|0.632|0.430|
||(0.492)|(0.494)|(0.482)|(0.495)|
|Share with schooling more than high school|0.110|0.109|0.130|0.110|
||(0.313)|(0.312)|(0.336)|(0.313)|
|Share of production workers|0.458|0.458|0.427|0.457|
||(0.498)|(0.498)|(0.494)|(0.498)|
|Share of workers in skill level 4|0.046|0.045|0.030|0.044|
||(0.210)|(0.207)|(0.173)|(0.206)|
|Share of workers in skill level 3|0.069|0.070|0.129|0.072|
||(0.254)|(0.256)|(0.335)|(0.260)|
|Share of workers in skill level 2|0.736|0.736|0.737|0.736|
||(0.440)|(0.440)|(0.440)|(0.440)|
|Share of workers in skill level 1|0.147|0.147|0.102|0.145|
||(0.354)|().354|(0.303)|(0.353)|
|Tenure|53.329|53.073|65.031|53.516|
||(65.741)|(65.420)|(72.972)|(65.754)|
|Share of men|0.701|0.706|0.901|0.713|
||(0.457)|(0.455)|(0.298)|(0.451)|
|Share of white|0.643|0.645|0.669|0.646|
||(0.478)|(0.478)|(0.470)|(0.478)|
|Observations<br>Notes:<br>Table reports<br>means<br>and standard<br>manufacturing firms that exported every year <br>2010 Brazilian Reais. Sample restricted to wo<br>period.|11,439,308<br> deviations<br>(in<br>over the period <br>rkers employed|13,489,458<br> parentheses)<br>2009-2012, by tr<br> in firms that ex|519,676<br>for workers<br>aining status. <br>ported in all y|14,009,134<br>employed at<br> Wages are in<br>ears over that|
|**Table 7: Heterogeneous Impact on Formalizat**<br>_Dependent variables:_|**ion by Base**<br>(1)|**line Charac**<br>(2)|**teristics**<br>(3)<br>**For**|(4)<br>**malized: G**|(5)<br>**UFE data**|(6)<br>More than|(7)<br>Female not|
|---|---|---|---|---|---|---|---|
||Female|Operates<br>in Tokpa||Species<br>classifi-|At least<br>secondary|one visit of<br>a tax|trader<br>outside|
|_Variable for heterogeneous analysis:_|owner|market|Trader|cation|education|inspector|tokpa|
|_Impact in group [...] for heterogeneous variable_|_=0_|||||||
|Group1<br>Group2|0.118***<br>(0.033)<br>0.192***|0.106***<br>(0.024)<br>0.148***|0.130*** <br>(0.030)<br>0.170***|0.092***<br>(0.023)<br> 0.120***|0.058**<br>(0.027)<br>0.105***|0.084***<br>(0.024)<br>0.122***|0.153***<br>(0.043)<br>0.231***|
|Group3|(0.022)<br>0.220***|(0.015)<br>0.178***|(0.020)<br>0.195***|(0.014)<br> 0.144***|(0.016)<br>0.112***|(0.015)<br>0.149***|(0.029)<br>0.230***|
||(0.019)|(0.013)|(0.018)|(0.013)|(0.015)|(0.013)|(0.025)|
|_Additional impact in group [...] for heterogeneo_|_us variable_|_=1_||||||
|Group1 x Heterogeneous variable (int1)|-0.042|-0.069|-0.076*|-0.003|0.080*|0.031|-0.050|
||(0.043)|(0.051)|(0.043)|(0.055)|(0.041)|(0.051)|(0.069)|
|Group2 x Heterogeneous variable (int2)|-0.097***<br>(0.028)|-0.082**<br>(0.032)|-0.072***<br>(0.027)|0.056*<br>(0.032)|0.064***<br>(0.024)|0.042<br>(0.030)|-0.134***<br>(0.045)|
|Group3 x Heterogeneous variable (int3)|-0.097*** <br>(0.025)|-0.089*** <br>(0.029)|-0.067***<br>(0.024)|0.080***<br>(0.028)|0.112***<br>(0.021)|0.049*<br>(0.026)|-0.078**<br>(0.039)|
|Observations|3,600|3,600|3,600|3,600|3,600|3,600|1,622|
|R-squared|0.388|0.386|0.386|0.385|0.393|0.384|0.398|
|Adjusted R-squared|0.080|0.076|0.076|0.076|0.087|0.074|0.088|
|Mean heterogeneous variable|0.629|0.217|0.549|0.182|0.409|0.196|0.415|
|Mean dep. var. in Control heterogeneous=0|0.011|0.007|0.007|0.007|0.004|0.007|0.006|
|Mean dep. var. in Control heterogeneous=1|0.008|0.015|0.011|0.018|0.016|0.017|0.009|
|_Pvalues of test:_||||||||
|Group1=group2|0.046|0.120|0.242|0.286|0.126|0.158|0.109|
|Group1=group3|0.002|0.003|0.033|0.028|0.061|0.008|0.072|
|Group2=group3|0.215|0.045|0.215|0.104|0.720|0.077|0.977|
|Group1+int1=group2+int2|0.533|0.574|0.187|0.124|0.416|0.365|0.925|
|<br>Group1+int1=group3+int3|0.096|0.252|0.014|0.010|0.013|0.100|0.355|
|Group2+int2=group3+int3<br>Note: Administrative data from GUFE and survey da<br>interaction terms (treatment dummies X variable for<br>**, * indicate statistical significance at 1, 5 and 10%.|0.097<br>ta March 201<br>heterogene<br>|0.413<br>5. OLS regre<br>ous analysis)|0.086<br>ssion of the<br>, controlling|0.155<br>outcome var<br>for strata du|0.017<br>iable on treat<br>mmies (dum|0.314<br>ment dummies<br>mies for each tr|0.101<br>and<br>iplet). ***,|
Table 6: Grid capacity elasticity of fixed operating costs

||||_Dependent v_|_ariable:_|||
|---|---|---|---|---|---|---|
||Employees <br>blishment,|per esta-<br>Agriculture|Employees <br>blishment,|per esta-<br> Industry|Employees <br>blishment,|per esta-<br> Services|
||_OLS_|_IV_|_OLS_|_IV_|_OLS_|_IV_|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Grid capacity|_−_0.003<sup>_∗∗∗_</sup>|0.023|0.027|_−_0.103|0.004<sup>_∗∗∗_</sup>|0.081|
||(0.001)|(0.100)|(0.017)|(0.670)|(0.001)|(0.146)|
|GVA per capita|0.019<sup>_∗_</sup>|0.052<sup>_∗∗_</sup>|0.474<sup>_∗∗∗_</sup>|0.426<sup>_∗∗∗_</sup>|0.083<sup>_∗∗∗_</sup>|0.093<sup>_∗∗∗_</sup>|
||(0.011)|(0.023)|(0.157)|(0.123)|(0.013)|(0.033)|
|Observations|5,948|2,556|5,815|2,512|5,943|2,554|
|R<sup>2</sup>|0.728|0.755|0.394|0.644|0.802|0.610|
|Adjusted R<sup>2</sup>|0.637|0.509|0.185|0.276|0.736|0.218|

_Notes:_<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05;<sup>_∗∗∗_</sup> p _<_ 0.01. The Lipscomb instrument is employed for grid capacity. Reported standard errors are robust. The amount 10<sup>_−_11</sup> is added to all variables, which enter the regression in logs. Regressions are weighted by municipal GVA and include municipality and time fixed effects.

39
<!-- Start of picture text --> 100%<br>91%<br>86%<br>90%<br>80%<br>70% 62%<br>60%<br>50% 44%<br>40%<br>29%<br>30% 26%<br>20%<br>20% 15%<br>10%<br>0%<br>High income Low income Upper middle Lower middle<br>income income<br>2014 2017<br>(age 15+)<br>Sent or received digital payments in last year<br><!-- End of picture text --> _Source_ : Findex.

Digital payments empower digital businesses meanwhile brings significant benefits to traditional businesses. Businesses that provide products or services online rely on digital payment mechanisms. Traditional businesses find digital payments to be three times more cost-effective than purchase order processes, with additional benefits of convenience and security.<sup>67</sup> By reducing travel time and expenses, digital payments help accelerate business registration and payments for business licenses and permits.<sup>68</sup> In 2018 Argentina launched an online portal for businesses to enroll with the tax authorities and adopt an electronic wallet for tax payment. According to the _Doing Business_ data set, the time to register a business fell from 24.5 days to 11 days thanks to the reform. This outcome shows the great potential of digital payment tools in reducing informality.<sup>69</sup> The traceability of digital payments also enables governments to better prevent fraud, money hoarding, and tax evasion.<sup>70</sup> The key players providing digital payment services differ in developed and developing countries. Developed countries have been using modern cashless payment methods by means of credit and debit cards since the 1950s. Banks are at the center of this payment system. Acquiring banks host the merchant’s account to receive payment. Issuing banks supply consumers with credit or debit cards to make purchases. When a customer orders a product or service online, the payment instructions and information are encrypted and sent to the acquiring banks through the facilitation of payment service providers. Acquiring banks then transmit the information through card networks such as Visa and Mastercard to the issuing bank. Once verified, the issuing bank transfers money through card networks to the acquiring bank’s merchant account.

Under this credit/debit card payment model, developed countries are in general more efficient in settling online payment transactions because of their well-established formal financial systems (figure 7). Among the 21 countries covered under the Digital Business Indicators project, a domestic online payment is settled within a day in most countries. However, in the Kyrgyz Republic it can take up to a week and five days in Malaysia. For cross-border payments, the settlement period ranges from three days in Indonesia and Vietnam to 10 days in Colombia and the Kyrgyz Republic.<sup>71</sup> A few factors affect the time it takes for merchants to receive payments. Having a subaccount with the payment service provider makes the process faster. Other factors include the delay in the authorization process among the issuing bank, acquiring bank, and payment service providers.

18
None of the other variables included in the model have a clear impact on the choice of service. In Western and Central Africa mother's education is associated with an increase in the probability of private medical facilities. In Eastern and Southern Africa mother's years of schooling is statistically significantly positively associated with seeking advice or treatment from all sources except private commercial. Table 14 reports the average predicted probability of seeking various types of care setting mother's education to 0 and then to 6 and averaging across all observations. Public lower-level facility use is about 4 percentage points higher in both regions for children of mothers with 6 years, as opposed to zero years, of schooling. Similarly, the use of private medical facilities is only about 3 or 4 percentage points higher among the more educated mothers. While the relationship with education is statistically significant, it is not large.

**Table 14. Average predicted probabilities from Multinomial Logit Estimation of Advice/Treatment sought as a result of a child under 3 having a fever in the past two weeks**

||_No/ Self_<br>_treatment_|_Public,_<br>_higher_<br>_level_|_Public,_<br>_lower_<br>_level_|_Private,_<br>_medical_|_Private,_<br>_commercial_|_Total_|
|---|---|---|---|---|---|---|
|_WesternandCentralAfrica_<br>Motherhas0yearsof<br>schooling|45.4|13.8|13.4|6.6|20.8|100|
|Motherhas6yearsof<br>schooling|42.5|11.9|17.2|11.9|16.5|100|
|_EasternandSouthernAfrica_|||||||
|Motherhas0yearsof<br>schooling|39.0|8.6|26.4|13.3|12.6|100|
|Motherhas6yearsof<br>schooling|33.4|9.7|30.3|16.0|10.6|100|

_Source:_ Author's calculations from pooled and weighted DHS data. Predictions are the average probability averaging over all individuals with their observed characteristics but substituting all observations to have the years of schooling of the mother in question.

One surprising result is that having a flush toilet or a pit latrine is positively associated with an increase in the likelihood of seeking treatment or advice from many of the modern sector choices listed. One might expect that better sanitation is a proxy for the general health environment in the household and would therefore affect the probability of being reported as sick (which is not true according to Table 8), but it is surprising that it should affect the choice of care conditional on illness. It is possible that toilet facilities are picking up a component of wealth.

A second specification estimated for each region includes the average wealth among other households in the cluster (and its square) as well as the incidence of fever among children from other households in the cluster. This specification will allow a test of whether it is a household's own wealth that matters or whether it is the general wealth of the surrounding households that matters. Moreover, it will allow a test of McCombie's (1996) observation that experience with malaria affects treatment choices.

_27_