rafmacalaba/datause-encoder-data huggingface dataset:
and former high budget deficit levels. Since it is mainly a supply problem (Sóvágó, 2011), the goal is to reduce the risks assumed by banks. A possible way to achieve this goal is the provision of state guarantees. Such programs already exist in Hungary; moreover, more than 20 percent of the loans extended to the SME sector are backed by state guarantee through Garantiqa Zrt (Chart 16). In view of the success of this program, similar options should be explored and aimed at supporting the revival of lending (MNB, 2011).

**Chart 15. Net change in loans outstanding to nonfinancial corporations**

<!-- Start of picture text --> HUF Bn HUF Bn<br>600 600<br>500 500<br>Forecast<br>400 400<br>300 300<br>200 200<br>100 100<br>0 0<br>-100 -100<br>-200 -200<br>-300 -300<br>-400 -400<br>-500 -500<br>Loans from financial enterprises - HUF Loans from banks - FX<br>Loans from banks - HUF Loans from financial enterprises - FX<br>Total domestic lending<br>2007Q1 Q2 Q3 Q4 2008Q1 Q2 Q3 Q4 2009Q1 Q2 Q3 Q4 2010Q1 Q2 Q3 Q4 2011Q1 Q2 Q3 Q4 2012Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB. Note: ―Banks‖ includes banks, branches, and cooperative credit institutions.

**Chart 16. Loans outstanding to the SME sector backed by the guarantee of Garantiqa Zrt**

<!-- Start of picture text --> HUF Bn per cent<br>4 500 27<br>4 000 24<br>3 500 21<br>3 000 18<br>2 500 15<br>2 000 12<br>1 500 9<br>1 000 6<br>500 3<br>0 0<br>The outstanding amount granted with the guarantee of Garantiqa<br>The outstanding amount granted to SMEs without the guarantee of Garantiqa<br>Guaranteed loans in proportion to total outstanding amount (right-hand scale)<br>2008Q1 Q2 Q3 Q4 2009Q1 Q2 Q3 Q4 2010Q1 Q2 Q3 Q4<br><!-- End of picture text --> Source: MNB.

# **_III. 2. Household lending: Regulatory measures_**

The household loan portfolio continued to grow in the last quarter of 2008, albeit at a slower pace, but net flow turned negative in 2009. The negative effects of the financial crisis became increasingly obvious for households as well; and this was gradually perceived in their income position. In addition, rising installments, due to the weakening of the forint, made households 20
8 terms of monitoring investments and exerting corporate governance after providing finance, Country A scores 90, while Country B scores only 20 on a scale from 0 to 100, and so on.

But, obtaining such direct measures of these financial functions is a major challenge. Levine (2005) points out that such empirical proxy variables often do not accurately measure the concepts emerging from theoretical models.

Thus, the goal of this paper is empirically measure four important characteristics of financial systems around the world for a broad cross-section of countries over a long time period. Specifically, this paper examines the following four characteristics of financial systems: (a) depth; (b) access; (c) efficiency, and (d) stability. The focus here is on measuring financial system characteristics.

These financial system characteristics are proxies of the services provided by the financial system. For example, ―financial depth‖ is not a function in itself, but it is a proxy of the overall extent of services provided by the financial system. Similarly, the measures available for ―access‖ do not directly measure how well the financial system identifies good investments, regardless of the collateral of the individual; but it provides an (imperfect, ex post) approximation of the breadth of use of particular financial institutions and instruments.

For completeness, the accompanying database includes data on broader social welfare indicators as well as the financial sector policy indicators and the other factors that define the enabling environment.

For each of the four characteristics, this paper presents measures for financial institutions (in particular banks, but also other, non-bank financial companies) and for financial markets. Table 1 provides a summary representation of this 4x2 matrix of financial system characteristics, building on a review of empirical literature on financial systems, discussed in the remainder in this section. Principal component analysis was used to double-check the choice of variables and their assignment to the 4x2 categories from the previous research.

The Global Financial Development Database is based on this 4x2 framework. It builds on, updates, and extends previous efforts, in particular the data collected for the ―Database on Financial Development and Structure‖ by Beck, Demirgüç-Kunt, and Levine (2000, 2010). The database also incorporates data from the Financial Access Survey (http://fas.imf.org), the Global Findex (www.worldbank.org/globalfindex) and Financial Soundness Indicators <u>(http://fsi.imf.org). Appendix I provides a further description of the data sources.</u>
# **8. REFERENCES**

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Krugman, P. (1991), _Geography and Trade_ , Cambridge, Mass.: MIT Press.

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35
Figure 3 – Germany, by survey <!-- Start of picture text --> Germany<br>Relative inequality of opportunity Return to education<br>1980 1990 2000 2010 2020<br>year of survey<br>st.deviation logs mean log dev. 1980 1990 year of survey2000 2010 2020<br>Parental networking Intergenerational persistence in education<br>1980 1990 2000 2010 2020 1980 1990 2000 2010 2020<br>year of survey year of survey<br>Regressors include gender, age, age2, born in East Germany and foreign citizenship<br>.55 .5 .09<br>st.deviation logs .45.5 .3.4 mean log dev. .08<br>.07<br>.4 .2<br>.06<br>.03<br>.75<br>.02<br>.01 .7<br>0<br>.65<br>-.01<br>-.02 .6<br><!-- End of picture text --> Figure 4 – Germany, age-cohort decomposition <!-- Start of picture text --> Germany<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Regressors include gender, age, age2, born in East Germany and foreign citizenship - Age and cohort effects obtained from Deaton's decomposition<br>.4 .04<br>.3 .03<br>beta1 .2 beta1 .02<br>.1 .01<br>0 0<br>.5 .03<br>.4 .02<br>.01<br>beta1 .3 beta1 0<br>.2 -.01<br>.1 -.02<br>.02 0<br>0 -.05<br>beta1 -.02 beta1 -.1<br>-.04 -.15<br>-.06 -.2<br>.05 .2<br>0 0<br>beta1 -.05 beta1 -.2<br>-.1 -.4<br><!-- End of picture text --> 15
The upswing in lending naturally goes hand in hand with convergence. Ideally, however, banks are capable of financing loans from internal savings. Accordingly, the loan-to-deposit ratio should be around 100 percent. In early 2003 this equilibrium prevailed in the Hungarian banking system. By the end of the year, however, the ratio rose to 110 percent, as the phase out of subsidized forint loans generated a sharp rise in net flow. Against the backdrop of the lending surge, the ratio rose nearly continuously and reached 160 percent by the beginning of the crisis. The dynamics of the loan-to-deposit ratio is in correlation with the share of foreign liabilities in the balance sheet. When the loan-to-deposit ratio rises above 100 percent, external funds are needed to replenish deposit funds. Developments in foreign liabilities confirm our previous statements. While their share was around 17 percent in proportion to the balance sheet total at the beginning of 2003, this value exceeded 30 percent by the onset of the crisis.<sup>9</sup>

**Chart 11. Net change in loans outstanding to households**

<!-- Start of picture text --> Per cent Per cent<br>55 55<br>50 50<br>45 45<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Loan to GDP actual level<br>Equilibrium level based on median of country constants<br>Estimated equilibriums with the different county constants<br><!-- End of picture text --> Source: Kiss et al. (2006); authors‘ calculations.

Besides the increasing weight of foreign liabilities, risks were exacerbated by a growing reliance on the FX swap market. On the deposit side, Hungarian banks obtained liquidity predominantly in forint, which was conveniently used to close their open FX positions in the swap market. This allowed banks to obtain cheaper foreign currency liquidity. However, this meant higher funding liquidity risks. On the one hand, their excessive reliance on the swap market posed serious problems during times of market disturbances. On the other hand, foreign currency liquidity obtained in the swap market was generally of much shorter maturity than the on-balance sheet foreign currency funds, which increased renewal risks. Third, it led to a drastic contraction in the liquid assets portfolio of the banking system that serves to ensure that institutions are capable of meeting their obligations even in times of crisis (Chart 12). Despite all this, Hungary did not adopt any liquidity regulations aimed at mitigating these risks.

> 9 The acceleration recorded in the last three months of 2008 primarily reflected parent banks‘ intentions to strengthen balance sheets.

13
- Justino, P., & Litchfield, J. (2003). Poverty dynamics in rural Vietnam: winners and losers during reform. _Poverty Research Unit at Sussex Working Paper_ , 10.

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- Lanjouw, P., Marra, M., & Nguyen, C. (2017). Vietnam’s evolving poverty index map: Patterns and implications for policy. _Social Indicators Research_ , 133(1), 93-118.

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- Marrero, G. A., & Servén, L. (2022). Growth, inequality and poverty: a robust relationship? _Empirical Economics_ , 63(2), 725-791.

- <mark>Marrero, Á. S., Marrero, G. A., & Servén, L. (2024). Poverty convergence clubs.</mark> _<mark>Review of Income and Wealth</mark>_ <mark>. Doi: https://doi.org/10.1111/roiw.12688.</mark>

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Table 3: Disruptiveness and elasticity of funding conditional on receiving funding

||Any Tec|hnology|Excludin<br>Payments|g Mobile<br>and Wi-Fi|
|---|---|---|---|---|
||Ever Funded|Last Funded|Ever Funded|Last Funded|
||(1)|(2)|(3)|(4)|
||Panel|a. All Firms|||
|Disruptive|0.036|0.024|0.016|0.043|
||(0.127)|(0.119)|(0.117)|(0.134)|
|Disruptive_×_Africa|-0.023|-0.077|-0.077|-0.190|
||(0.217)|(0.194)|(0.275)|(0.290)|
|Disruptive_×_LAC|-0.306**|-0.344**|-0.352**|-0.387**|
||(0.131)|(0.128)|(0.129)|(0.150)|
|_R_<sup>2</sup>|0.524|0.476|0.524|0.521|
|Observations|6519|5791|6519|5791|
|Countries|33|32|33|32|
|P|anel b. Start-u|ps (10 years old|or less)||
|Disruptive|<br>0.035|<br>0.063|<br>0.025|0.054|
||(0.130)|(0.133)|(0.120)|(0.136)|
|Disruptive_×_Africa|-0.035|-0.086|-0.059|-0.175|
||(0.200)|(0.199)|(0.254)|(0.261)|
|Disruptive_×_LAC|-0.324**|-0.374**|-0.374***|-0.398***|
||(0.143)|(0.143)|(0.126)|(0.144)|
|_R_<sup>2</sup>|0.531|0.485|0.531|0.527|
|Observations|5520|4907|5520|4907|
|Countries|33|32|33|32|
|Pa|nel c. Older fr|ms (11 years ol|d or more)||
|Disruptive|0.036|-0.138|-0.009|0.010|
||(0.198)|(0.181)|(0.206)|(0.223)|
|Disruptive_×_Africa|0.184|-0.058|-0.270|-0.317|
||(0.663)|(0.681)|(0.602)|(0.666)|
|Disruptive_×_LAC|-0.134|-0.177|-0.150|-0.259|
||(0.301)|(0.215)|(0.302)|(0.298)|
|_R_<sup>2</sup>|0.458|0.428|0.458|0.461|
|Observations|999|884|999|884|
|Countries|24|22|24|22|

Clustered errors in parentheses. *** 10%, ** 5% and * 1%. All regressions include as controls age (in IHS transformation), dummies for 9 categories of employment size, number of similar firms (in IHS transformation), dummies for the number of overlapping sectors where the firm operates (one or two additional sectors). The dependent variable is the IHS transformation of the amount of funding in US Dollars received in the last funding round. Standard errors clustered at the country level.

22
where all variables are expressed in current **US** dollars. **NFQA** denotes the net holdings of equity-related assets and **NFLA** the net holdings of other assets, each given **by** the corresponding term in square brackets in the second line of **(3.1).** Using the letters **A** and L to denote respectively assets and liabilities, **NFQA** can be seen to equal the sum **of** the net holdings **of** direct foreign investment assets **FDIA -** FDIL plus the net holdings of portfolio equity assets, **EQYA - EQYL.** In turn, the second term in square brackets captures the net position in nonequity-related assets, that for brevity we shall call "loan assets". The position consists **of** international reserves RA, plus the net loan position **LA -** LL.

Absent valuation changes, unrequited capital transfers debt forgiveness and other debt reduction operations, and ignoring misinvoicing of current account transactions, the rate of change of **NFA** would just equal the current account surplus **CA,** expressed in **US** dollars: **_ANFA(j,_** _t)_ **_= CA(j,_** _t)_ **(3.2)** Given some initial condition for **NFA,** recursive use of **(3.2)** would then permit construction of the country's net foreign asset position. Likewise, accumulation of disaggregated financial-account flows from the BoP would permit construction of each of the stocks in **(3.1)** above. But the conditions just mentioned under which historical flow accumulation would yield a good approximation to the value of the corresponding stocks are quite stringent. More specifically, correcting for the effects of unrequited capital transfers and debt reduction operations is relatively simple, as the relevant data is available from the **IMF** and the World Bank, but valuation effects are much more problematic. They arise from two main sources cross-exchange rate changes, whose effect depends on the currency composition of foreign assets and liabilities (generally unavailable from standard data sources), and changes in the secondarymarket price of assets **(e.g.,** equity prices in the case of portfolio investment, or market prices of developing-country debt). The latter valuation effects are even more difficult to estimate, as organized secondary markets often do not exist (particularly in developing countries).

Obviously, valuation problems could **be** easily overcome if information were available reporting asset stocks at current exchange rates and market prices. However, available stock information is limited to two main sources: (i) the foreign reserve data collected **by** the IMF's **IFS,** which value foreign exchange reserves at current exchange rates and have very broad coverage across countries and over time; and (ii) the external debt data compiled **by** the World

**13**
SPEcIAL ToPic 11: DEMOGRAPHIC TRENDS IN FORMER SOVIET-UNION blxiii

**Figure** SS. Trends in TFR: high fertility countries

<!-- Start of picture text --> ______________ _____________M<br>.<br>.<br>.<br>0 f- * W a- ^1l|<br><!-- End of picture text -->

Figure S6. Difference in e(0) from USSR average <!-- Start of picture text --> to _. A d,, w a I, LA, ^<br><!-- End of picture text --> Figure S7. Trends in e(O) <!-- Start of picture text --> n<br>i , 1<br>* . . . . . . Y<br>* n * * a* * 7 * n *<br>C a ~Ie ttt * *~~ C *~*0 C trth Itthe<br><!-- End of picture text -->

# _ortali*y_

Differences in life expectancy are not as pronounced as those for fertility and population growth. Figure S6 shows estimated 1992 life expectancy (male and female) from the Soviet Union average for each country. Belarus is highest, with a life expectancy that is two years higher, and Turkmenistan stands out with a life expectancy five years below the Soviet Union average. Recent trends in life expectancy for males are shown in Figures S7 and S8. As with fertility, clear geographic patterns are noticeable, with lower life expectancies for the Central Asian countries. Life expectancy increased in most countries by one to two years during the 1980s although there was a sizable one-time drop in Armenia's level attributable to the 1988 earthquake. One of the more remarkable demographic features of some of the former Soviet republics is the large differences between male and ^ *female life expectancy (Figures S9 and S10), reaching a high of 10.5 years in Russia -- the highest observed difference in the world. These disparities between male and female life expectancy are probably due to greater alcohol and cigarette consumption by men (Dutton 1981).

Although infant mortality increased somewhat during the 1970's (possibly due to improved reporting), it has been decreasing over the last decade for all of the countries. However, considerable diversity still exists in the rates which are higher in the Central Asian countries and lower in most of other countries. The infant mortality rates range from 54 in Turkmenistan to 15 in the Ukraine and Belarus.
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