rafmacalaba/datause-encoder-data huggingface dataset:
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31
<!-- Start of picture text --> African Growth Incidence Curve (fractile groups), 1993-2008<br>2<br>3<br>B (ES<br>5<br>oa<br>a<br>oO<br>I a<br>(=) a<br>Sw<br>2<br>£<br>s p 5<br>=<br>2<br>3)<br>o O<br>=)<br>=<br>=<br><x<br>-.5<br>10 20 30 40 50 60 70 80 90 100<br>Percentile groups of African distribution of consumption expenditure<br>Annualised growth rate in average consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Growth incidence evaluated at ventile groups (e.g. bottom 5%). The horizontal line shows the growth rate in the mean of .92%.<br><!-- End of picture text --> <!-- Start of picture text --> African Growth Incidence Curve (fractile groups), over time<br>se 10<br>fa}<br>”/<br>=SL /<br>a 5 “ao re /<br>= ai ke?<br>©= eo cuieeet age "s<br>=} .<br>(Si e<br>(oe:<br>Lito<br>10 20 30 40 . 50. . 60 70 . 80 90 100<br>Percentile groups of African distribution of consumption expenditure<br>——— 1993-1998 ———-— 1998-2003 ---------- 2003-2008 -—— ——: 1998-2008<br>Annualised growth rate in average consumption expenditure of the fractile group (not the fractile). Weighted by population.<br>Growth incidence evaluated at ventile groups (e.g. bottom 5%).<br><!-- End of picture text -->
# **Table 7: Panel Regressions – Robustness of main result to alternative indicators: Volume of all transactions for all crypto-assets**

- - <u>Dependent Variable: Log monthly on chain crypto assets volume (all transaction sizes), US dollars</u>

|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|---|---|---|---|---|---|
|**Change in broad money (yoy)**|0.779|0.028|-0.057|-0.026|-0.061|
|Change exchange rate (mom)|-1.865|-0.597|0.940***|0.972***|0.633|
|Change Bitcoin price (mom)||0.580***|0.406***|0.641***|0.342***|
|Change Ether price (mom)||0.574***|-0.021|0.049|0.054|
|US 10-year Yield (inflation adjusted)|||-0.242***||-0.353***|
|**US 10-year breakeven inflation rate**|||1.911***||2.337***|
|**US BBB Corporate bond spread**||||-0.533***|0.196***|
|Change gold price (mom)||||-1.305***|-0.754**|
|Pandemic dummy||1.494***|1.395***|2.417***|0.969***|
|N of Observations|2258|2258|2258|2258|2258|
|N of clusters (countries)|102|102|102|102|102|
|Adjusted R squared|0.017|0.451|0.749|0.635|0.756|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|
|Variable|(6)|(7)|(8)|(9)|(10)|
||||**EMDEs only**|||
|**Change in broad money (yoy)**|0.671|0.034|-0.065|-0.027|-0.069|
|Change exchange rate (mom)|-1.195|-0.467|1.084***|1.102***|0.811**|
|Change Bitcoin price (mom)||0.548***|0.364***|0.606***|0.302***|
|Change Ether price (mom)||0.560***|-0.038|0.036|0.041|
|US 10-year Yield (inflation adjusted)|||-0.298***||-0.411***|
|**US 10-year breakeven inflation rate**|||1.904***||2.303***|
|**US BBB Corporate bond spread**||||-0.535***|0.187***|
|Change gold price (mom)||||-1.502***|-0.963**|
|Pandemic dummy||1.529***|1.385***|2.457***|0.967***|
|N of Observations|1689|1689|1689|1689|1689|
|N of clusters (countries)|78|78|78|78|78|
|Adjusted R squared|0.014|0.443|0.731|0.623|0.737|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported.
|||**Tabl**<br>**Incidence of**|**e 6: Credit Bure**<br>**Petty Corruption**|**aus and Corrup **<br>|**tion**|**Depth of Pet**|**ty Corruption**||
|---|---|---|---|---|---|---|---|---|
|Dependent variable: Financially<br>Constrained (dummy)|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Incidence or Depth of Petty<br>Corruption (Cell<br>average)*Credit Bureau Present|-1.564***|-1.984***|-1.596**|-1.956***|||||
|(dummy)|(0.603)|(0.627)|(0.686)|(0.696)|||||
|Depth or Depth of Petty<br>Corruption (Cell<br>average)*Credit Bureau Present<br>(dummy)|||||-1.475**<br>(0.701)|-1.769**<br>(0.721)|-1.344*<br>(0.789)|-1.746**<br>(0.801)|
|Incidence or Depth of Petty|1.110**|1.457***|5.426*|-0.563|1.097**|1.458***|6.312|0.558|
|Corruption (Cell average)|||||||||
||(0.469)|(0.440)|(3.077)|(2.039)|(0.517)|(0.482)|(3.871)|(2.335)|
|Incidence or Depth of Petty|||0.200**|0.194**|||0.199*|0.193|
|Corruption (Cell<br>average)*Annual Sales (2009|||||||||
|USD, logs)|||||||||
||||(0.095)|(0.096)|||(0.116)|(0.119)|
|Incidence or Depth of Petty<br>Corruption (Cell average)*GDP|||-0.776**||||-0.875**||
|per capita (logs)|||(0.372)||||(0.445)||
|Annual Sales (2009 USD, logs)||-0.140***|-0.199***|-0.197***||-0.140***|-0.192***|-0.189***|
|||(0.036)|(0.044)|(0.044)||(0.036)|(0.045)|(0.046)|
|Age of Firm (logs)||0.032|0.035|0.033||0.029|0.031|0.031|
|||(0.072)|(0.071)|(0.072)||(0.072)|(0.071)|(0.071)|

44
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31
inflows (including remittances from Egyptian workers abroad – particularly to redirect such flows through official channels).

b. Financing for Development Options and Prospects – Lessons from Korea and Poland 65. Egypt’s large fiscal deficit and government debt burden has severely constrained its fiscal space, as evidenced by the large debt service outlays (which now limit its capacity to adequately fund health services, education, and infrastructure). While Egypt’s public finances and debt situation worsened markedly since 2011, they have historically been relatively weak, as shown in Figures 4 and 5. The general government fiscal deficit averaged 12.5 percent of GDP over the period spanning 1995 to 2016, and the general government debt averaged well over 80 percent of GDP during the same period.

66. As we begin to compare the growth models for Korea and Poland, it should be noted that our analysis is based on their respective periods of transition: between 1970-1990 for Korea and 1990-2010 for Poland.<sup>25</sup> 67. During the 1970s and into the early 1980s, coinciding with its early development period that depended on public investment in social infrastructure and heavy industry, Korea ran a budget deficit (though it did not exceed 5 percent of GDP). Commencing in 1981, Korea shifted to a tight fiscal policy to address the structural imbalances that amassed during the first phase of development; while not enshrined in law or regulation, the principle of maintaining a balanced budget has since governed the fiscal stance. Between 1995 to 2016, as shown in Figure 3, Korea’s general government produced an average fiscal _surplus_ amounting to 1.5 percent of GDP, reflecting its commitment to maintaining a sound budget position.<sup>26</sup> As this figure incorporates the surplus of the pension fund, it overstates the surplus of the central government alone. However, the strength of the fiscal position is further illustrated by the ratio of government debt (which represents the accumulation of prior deficits) to GDP; in the case of Korea, this ratio averaged 25 percent over the 1995-2016 period.

> 25 The data comparisons for Egypt, Korea and Poland depict the general government finances in order to use a standard methodology taken from the IMF’s World Economic Outlook, 2017.

> 26 The 1997 Asian Financial Crisis led to large deficits, but the fiscal surplus re-emerged quickly when Korea’s economic growth rebounded.

24
where _n_ is the total population, incomes are ordered from _i_ =1 as the poorest and _q_ are poor, _z_ is the poverty line, and _gi_ the poverty gap, _gi_  _z_  _yi_ , (<sup>_y_</sup> _i_<sup>is per capita consumption in the</sup><sup>_i_th</sup> household).<sup>17</sup> For  =0, _P0_ is the head-count index, for  =1, _P1_ is the poverty gap index, and for  =2, _P2_ is the squared poverty gap or poverty severity index. The _P_  class additively decomposes contributions from each subgroup to the total level of poverty, which is reported in the table as the “share” of poverty. Another useful manipulation of the _P_  measures is to calculate the “risk” of poverty, which is the poverty rate for a particular subgroup relative to the overall average, and this is also reported.

If the Engel curve deflator is used to adjust for regional and sectoral differences in the cost-of-living, it makes the national headcount poverty rate appear ten percentage points higher than if either WCPD deflator is used (37% versus 27%). This upward bias comes entirely from the rural sector, where the Engel curve deflator causes poverty to be overstated with proportionate biases of 39% in the headcount, 68% in the poverty gap, and 92% in the poverty severity index. The basic pattern of the poverty profile is not altered by using the Engel curve deflator—poverty is overwhelming rural in Vietnam—but the policy response to finding that one-half (49%) of rural dwellers live in households below the poverty line and that the risk of being poor in urban areas is just one-tenth the national risk (for the poverty severity index) is likely to be quite different to finding just over one-third of the rural population poor, which is what is revealed when either a variable-weight or fixed-weight WCPD deflator is used.

> 17. The poverty line of VND 653,000 used by World Bank (2012) is in national average prices of January 2010. In contrast the deflators used here are based on urban Red River prices, for surveys centred on October 2010, for which the equivalent poverty line is VND 881,000.

30
as many as publicly-supported post-secondary training institutions, and they gross $2.5 billion annual revenues (Wilms, 1975).

Documentation of their activities in developing countries is almost non-existent but one study reveals that their contribution can be much greater than most planners give credit. Lukomski (1978) traced the training origins of a sample of 540 lathe-operators, perhaps the archetypal industrial occupation, in Sao Paulo in Brazil. Before the study was undertaken it was supposed that about 60% would have served conventional three-year apprenticeships with SENAI. In fact the figure was 30%. Most of the remainder had trained by taking non-apprenticeship courses, and "what was most surprising was the high percentage of courses taken in proprietary industrial schools--about 55%. Although it was known before the study that the proprietary schools existed, it was not realized that they were responsible for 1.7 times as many courses as SENAI.. .The existence of a 'system' of proprietary industrial training--relatively large and with a long history--was one of the most important findings of the study."

# 8.8 CorresRondence courses

Correspondence courses are the dominant form of proprietary training in the U.S., enrolling two million students and generating over $1 billion annual revenues (Wilms, 1975). They account for 25% of the formal training received by accountants, 27% of that received by engineers, and 30% of that received by radio and television repairmen (U.S. Department of Labor, 1964, Table 4). And yet very little is known about their costs or benefits--how many students complete their courses, what recognition, if any, is given to their credentials by employers, etc. Moreover, if the Proceedings of the National Invitational Forum on Corresponder.ce Education held in Columbus, Ohio in 1984 (Cambell-Thrane, 1984) are indicati:e, there is no prospect of systematic evaluation being undertaken in the immediate future.

In other countries there may even be no systematic information on the identities of the enterprises, never mind the scale of their operations, especially where their activities cross international frontiers. The occasional ad-hoc training survey may shed a little illumination. In Zambia, for example, the 1983 Manpower Survey (Zambia National Commission for Development Planning, 1985) revealed that "out of total Zambian employees, 23.5 percent reported to be receiving some kind of education or training at the time of the survey. Of these, 43.2 percent were training through correspondence, 24.2 percent formal on-the-job training, 15.1 percent evening classes and 10.2 percent in-service training". There is no information on the content of the courses and it is evident that white-collar skills predominated, but a table shows that 30% of them were being taken by production and related workers.

All too often, however, the survey instrument in such an inquiry preempts the gathering of information on correspondence courses by consigning them to the catchall category of "other types of training".

99
Moving from disposable income to consumable income further reduces the Gini coefficient to 0.464 (Figure 2), which is indicative of a remarkable finding: the net effect of adding indirect subsidies and subtracting indirect taxes to/from disposable income is surprisingly equalizing. As Figure 4.B shows, this is not only due to the positive marginal contribution of indirect subsidies to the inequality reduction, but also because indirect taxes have a slightly positive effect despite their regressivity, as indicated by a Kakwani index of -0.03. The latter result is referred in the literature as the Lambert’s Conundrum (Lambert, 2001), which states that “ _If taxes are regressive in relation to the original income but progressive with respect to the less unequally distributed post-transfers (and subsidies) income, regressive taxes exert an equalizing effect over and above the effect of progressive transfers_ ” (Enami, Lustig, and Aranda, 2017).<sup>24</sup> As noted, indirect taxes in Chile are regressive with respect to market income plus pensions (the original income), but they are progressive with respect to disposable income (the less unequally distributed post-transfers income), as indicated by a Kakwani index of 0.09. Indirect taxes, therefore, exert an equalizing effect over and above the effect of progressive direct taxes and direct transfers. This finding evidences that the redistributive impact of fiscal interventions must be assessed by considering the whole system and not as isolated tools, which in turn could lead to misleading policy conclusions.<sup>25</sup> Overall, when taking consumable income as the end concept, only social contributions and the subsidy to gas exhibit, respectively, a negative and neutral effect on inequality, whereas both direct taxes and transfers account for the largest positive marginal contributions (Figure 4.B). The overall equalizing effect of taxes and direct transfers is unambiguous as the Lorenz curve for consumable income lies completely above the Lorenz curve for market income plus pensions (Figure 5).

**Figure 5: Concentration and Lorenz curves**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Cumulative share of population<br>Lorenz curve for market income plus pensions Direct taxes<br>Indirect taxes Direct transfers<br>Subsidies Lorenz curve for consumable income<br>Cumulative share of income, taxes and transfers<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016).

> 24 Enami, Lustig and Aranda (2017) offer a detailed theoretical explanation regardig this counterintuitive result. 25 Recently, Eduardo Engel found the same Lambert’s conundrum in the Chilean system using the same data exploited in Engel, Galetovic and Raddatz (1999).

14
how some of the existing shortcomings in terms of availability, quality and policy relevance of migration data can be overcome via improvements in data collection methods.

# II. DATA SOURCES AND CHALLENGES<sup>3</sup>

# **Sources of Data**

The majority of migration data come from destination countries, as it is often easier to capture people where they currently are rather than where they left. Destination countries use a wide range of tools to count and analyze characteristics of migrants within their boundaries. Among these data sources are (i) censuses that are aimed at capturing all people within borders at a given point, (ii) various surveys, such as labor market or specialized and multi-topic surveys, that sample a smaller portion of the population but ask more detailed questions, (iii) population registers, common in certain countries, and (iv) various administrative data sources such as border statistics, employment and residency permits, as well as naturalization records. Such data sources are used in quantifying migration patterns, especially between country pairs, as well as in identifying demographic, economic, social and cultural characteristics of migrants within a country. In addition, the impact of migration on destination countries’ labor markets or various other social and economic outcomes can be assessed using these data in conjunction with other relevant data sources.

> 1. Some of the sections in this paper draw from Carletto and de Brauw (2008) and de Brauw and Carletto (2012).

3
response to the relative decline of'competitiveness in the NIEs. In 1988, the volume of direct investment from NIEs to ASEAN exceeded that from Japan to ASEAN (Watanabe, 1991). A consequence of these net capital inflows appears to be stimulation of the equity markets. The sharp rise of the stock markets in Korea and Taiwan was around 1986, while the big boom of the markets in Indonesia, Malaysia, Philippines and Thailand began in about 1988, coinciding in each case with the upturn in foreign investment.

Another channel of capital inflow is through portfolio investment in the form of country funds. For example, in 1984, the first country fund, the Korea Fund, Inc., was listed on the New York Stock Exchange, with an initial value of $60 million. Since then, many local, regional, and global funds have been launched in international markets. Favorable treatment with regard to taxation and foreign exchange control was implemented in some of the countries and consequently these funds became very popular among international investors. Since it is more likely that these investors would invest in the equity markets of those economies which had better financial infrastructures, and conversely, the development of the equity markets would tend to increase the demand for other financial services, the development of the financial markets in general and equity markets in particular would tend to go hand in hand in these countries.

# Explanations for the Second Correlation Pattern

The lack of correlations between MCY and indicators of financial depth in most of the non-Asian economies over time may be due to two reasons. One is the effect of inflation. Table 10 shows the period averages of annual rates of inflation in the 20 economies under study. It is clear that the rates of inflation were much higher in Latin American economies. High inflation tends to introduce distortions and instability into our variables, since these variables are calculated by taking the averages of year-end values of stock variables and then dividing by a flow variable. In addition, high inflation could have opposite effects on MCY and QMY. While domestic equity investments can be more attractive in an environment of high inflation, quasi-money assets (which are generally fixed interest instruments) are likely 11