rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 5. Gross Portfolio Flows** Cumulative 1991-93.

<!-- Start of picture text --> Brazil Korea<br>Chile (2'.6%) Argentina (42.6%)<br>(2.1%) Argent/n<br>g < S ~~~(14.0%) \.O/ (l O 6 °c)~~~~~~~Indonesia<br>(10.6%)<br>Others India<br>(11.7%) Thailand (6.9%)<br>Mexico (I11.9%)<br>Others<br>(50.5%)<br>(28.0%)<br>Latin America Asia<br><!-- End of picture text --> Source: The World Bank, World Debt Tables 1993-94
U.S. and Japan and their bilateral current account. In this case, only U.S. holdings of Japanese assets and Japanese holdings of U.S. assets matter.

There are many reasons for choosing the U.S. and Japan in our empirical exercise. The first one is data availability. Bilateral current account data between the U.S. and Japan, Canada, or the U.K. is available since 1960 on a quarterly basis. The data for other countries starts in the late 1970s, and therefore, has an insufficient time span for our purposes. Furthermore, in our empirical exercise, we use asset returns on stock markets, government bonds, and private firm profits (return on equity). This last variable is not available for the U.K. and Canada, although we could have excluded it from our analysis.

Second, Japan was economically relevant for international capital flows from 1960 to 2006. Both the U.S. and Japan are representatives of the so-called “global imbalances.” The current account deficit in the U.S. was soaring and reached 7% of its own GDP in 2005 – almost 2% of world GDP. Japan has long been the country with the largest current account surplus. Furthermore, the U.S. and Japanese current account balances were mirror images of each other until the late 1990s, suggesting that they could have had a large counterpart in each other’s balances.

Third, the total U.S. current account and the U.S.-Japan bilateral current account have similar dynamics. This is highlighted in Figure II, which plots these series as a percentage of U.S. GDP. Movements in the total U.S. current account clearly resemble movements in the U.S. bilateral current account with Japan. Thus, determinants of the bilateral current account can be indeed relevant to the understanding of factors affecting the total U.S. current account.

Lastly, it is well known that countries’ portfolios are subject to home bias – that is, portfolio composition tends to be biased toward domestic assets. For example, institutional investors in the 19
**Table 6. Correlates of Sudden Stops**

**(Probit model, marginal effects, 1991-2002)**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VIX, Log|0.01*|0.0086*|0.0079*|0.0087**|0.0083**|0.0079*|0.0067|0.0074|
||[1.93]|[1.92]|[1.92]|[2.18]|[2.10]|[1.65]|[1.61]|[1.61]|
|US Policy Rates (%)|0.01***<br>[4.27]|0.0097***<br>[4.79]|0.0092***<br>[4.32]|0.0083***<br>[4.25]|0.0084***<br>[4.15]|0.0092***<br>[3.46]|0.0085***<br>[4.22]|0.0090***<br>[3.61]|
|Capital Flows/GDP|0.01***|0.0128***|0.0117***|0.0130***|0.0139***|0.0128***|0.0121***|0.0121***|
||[6.46]|[6.02]|[6.09]|[6.27]|[5.12]|[5.99]|[6.13]|[6.17]|
|Domestic Credit/GDP||-0.0023|-0.0012|-0.0012|-0.0021|-0.0022|-0.0017|-0.0017|
|||[1.07]|[0.72]|[0.48]|[1.08]|[1.05]|[0.76]|[0.80]|
|RER (% Change)|||-0.0045*<br>[1.93]||||||
|Reserves/GDP||||-0.0068*<br>[1.93]|||||
|External|||||||||
|Liabilities/GDP|||||-0.0044*<br>[1.70]||||
|# of Sudden Stops<br>elsewhere in the world||||||0.0021||-0.0032|
|||||||[0.47]||[0.50]|
|# of Sudden Stops<br>elsewhere in the Region|||||||0.0065*<br>[1.96]|0.0079*<br>[1.66]|
|Observations|882|862|840|862|861|862|862|862|
|Pseudo R-squared|0.120|0.121|0.130|0.137|0.129|0.122|0.135|0.137|

Dependent variable is a binary variable which is equal to 1 if a sudden stop occurs and 0 otherwise. The first quarter of sudden stops are included in the regressions, all subsequent quarters dropped. Domestic variables are averages of previous eight quarters. All variables have been standardized around zero mean and standard deviation equal to 1. Capital flows, domestic credit and reserves, and international investment are in percent of GDP. Real exchange rate is in percent change; an increase denotes a depreciation. VIX is in log; sudden stop episodes elsewhere in the world or region are the number of sudden stops elsewhere in the same quarter. Regressions are estimated with robust standard errors, and observations clustered by countries. Z statistics reported in parentheses. ***,** and * indicate significance at 1, 5, and 10% levels, respectively.

23
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44
# **TABLE 1: JURISDICTIONS PARTICIPATING IN THE SURVEY**

|Angola|**Hong Kong SAR, China**|Pakistan|
|---|---|---|
|Argentina|Hungary|Palestinian Territory|
|Armenia|**Iceland**|Panama|
|**Australia**|India|Paraguay|
|**Austria**|Indonesia|Peru|
|Bahrain|Iraq|Philippines|
|Bangladesh|**Ireland**|Poland|
|Belarus|**Isle of Man**|**Portugal**|
|**Belgium**|**Israel**|Puerto Rico|
|Belize|**Italy**|Qatar|
|Benin|Jamaica|Romania|
|Bhutan|**Jersey**|Russian Federation|
|Bosnia and Herzegovina|Jordan|Samoa|
|Botswana|Kazakhstan|Senegal|
|Brazil|Kenya|Serbia|
|Bulgaria|Korea, Republic of (South Korea)|Seychelles|
|Burkina Faso|Kosovo|Sierra Leone|
|Burundi|Kuwait|**Singapore***|
|**Canada**|Kyrgyzstan|**Slovakia**|
|**Cayman Islands**|Latvia|Slovenia|
|Chile|Lebanon|South Africa|
|China-People's Rep.|Lesotho|**Spain**|
|Colombia|**Liechtenstein**|Sri Lanka|
|Cook Islands|Lithuania|Suriname|
|Costa Rica|**Luxembourg**|Swaziland|
|Côte d'Ivoire|**Macao, SAR**|**Switzerland**|
|Croatia|Madagascar|Syrian Arab Republic|
|**Cyprus**|Malawi|**Taiwan, China**|
|**Denmark**|Malaysia|Tajikistan|
|Dominican Republic|Maldives|Tanzania, United Republic of|
|Ecuador|Mali|Thailand|
|Egypt|**Malta**|Togo|
|El Salvador|Mauritius|Tonga|
|**Estonia**|Mexico|Trinidad and Tobago|
|Ethiopia|Moldova, Republic of|Tunisia|
|Fiji|Montenegro|Turkey|
|**Finland**|Morocco|Uganda|
|**France**|Mozambique|Ukraine|
|Gambia|Myanmar|United Arab Emirates|
|**Germany**|<br>Namibia|**United Kingdom**|
|<br>Ghana|Nepal|<br>**United States**|
|**Gibraltar**|<br>**Netherlands**|Uruguay|
|**Greece**|**New Zealand**|<br>Vanuatu|
|Guatemala|Nicaragua|Venezuela, Bolivarian Republic of|
|**Guernsey**|Niger|**Virgin Islands, British**|
|Guinea-Bissau|Nigeria|Yemen|
|Guyana|**Norway**|Zimbabwe|
|Honduras|Oman||

Notes: Advanced economies in bold. Emerging markets and developing economies in regular font. The classification follows IMF’s September 2011 _World Economic Outlook_ (WEO). For jurisdictions not classified by IMF’s 2011 WEO, 2010 output per capita figures were used to place the jurisdiction in one of the two income categories utilized in this paper. *A response was provided but only on a confidential basis; not part of the published dataset.

18
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29
42 existence of a broad trend toward an expanded role for smaller enterprises and also toward the greater relative importance of small establishments within large enterprises. This seems to have been underway for about two decades, and represents a reversal of the previously prevailing historical trend toward concentration. Given the limitations of available data and the complexity of the causal relations that may be involved, it is no small task to prove that this trend is real (that the measurements mean what they appear to say) and significant (that the reasons involve some changes directly affecting the competitive environments of small units). As argued by Loveman and Sengenberger (1991), the strength of the case for an affirmative answer on these points does not rest so much on the conviction carried by any single piece of well-honed evidence, but rather on the scope of the evidence across sectors and national economies: "Despite important methodological caveats, the nine country studies taken together present a convincing case for a shift in employment to smaller units of production" (p. 25).

Thus, in thinking about the future roles of SMEs in developing countries, it may be important to question the relevance of current and historical patterns in advanced countries as a conceptual model. The appropriate conceptual model may be visible only at the "leading edge" of the wave of structural change in the advanced countries, or may even remain invisible at this point, but accessible to imaginative
## _Demographic and Health Surveys_

Demographic and Health Surveys (DHS) were carried out in Peru in 1996 and 2000. These nationally representative surveys of over 28,000 households each are part of a program funded by USAID and implemented by Macro International Inc, which has included over 70 nationally representative household surveys in more than 50 countries. The DHS surveys are conducted in single rounds with two main survey instruments: a household schedule and an individual questionnaire for women of reproductive age (15-49). The household schedule collects information on household members, assets and access to public services. Since income or expenditure data is not collected, we use the asset data to construct household asset indices as measures of household wealth (Sahn and Stifel 2001). Child anthropometric measurements are recorded in the individual module.

## _Public Expenditure Tracking Survey_

The PETS for the VL program was conducted by _Instituto Apoyo_ at the end of 2001 and early in 2002, to quantify leakages and delays in the process of public expenditure disbursements, and to assess the effects of deficiencies in the system on the quality of the services provided. As such, interviews were conducted at three levels - the municipality, the mothers' committee, and the household. One hundred municipalities were sampled, from which four mothers' committees each were selected randomly, and finally four beneficiary households were selected randomly from each mothers' committee in the sample. Because in some municipalities there are fewer than four committees, only 393 committees were interviewed, and 1,587 beneficiary households were interviewed. The household survey includes information on household demographics, assets, and details about participation in the VL program including values of transfers, types of products transferred and information on additional purchases.

# **4.** Research Strategy

## _A. Program Targeting_

We first address the question of the incidence of distribution of the benefits to the poor. We ask not only what share of the poor - defined in terms of income or wealth as well as in terms of nutritional status of children - participate in the program, but also what share of benefits accrue to different groups. This provides a fair description of both errors of exclusion and inclusion. We also take this a bit further using a technique to decompose the targeting efficiency into interdistrict and intradistrict components (Galasso and Ravallion forthcoming).

_4_
While spreads are the difference between ex-ante contracted loan and deposit interest rates, margins are the actually received interest (and non-interest) revenue on loans minus the interest costs on deposits (minus non-interest charges on deposits). The main difference between spreads and margins are lost interest revenue on non-performing loans, so that spreads are normally higher than margins. We compute the _spread_ between the weighted average lending rate and the weighted average deposit rate for each bank and each quarter, where the weights are the relative amounts of deposits or loans contracted at specific interest rates in the respective quarter and by the respective bank. While the interest and loan/deposit amount data are available on a monthly frequency, we average them at quarterly frequency to make them comparable to financial statement data.

In the academic literature, many definitions for margins exist, and in the following we will make usage of two commonly used margin expressions. First, we define the variable _margin_ as the difference between total interest income and expenses over total earning assets.<sup>12</sup> Second, _wide margin_ is defined as the difference between interest and commission received over loans and interest paid minus fees over deposits. The latter definition adds interest and fee charges and can therefore be seen as a more complete measure of the interest rate margin.<sup>13</sup> We have bank balance sheet data from the Bank of Uganda for interest margins and spreads for the period between the second quarter of 1999 and the second quarter of 2005. We note that this sample period omits the banking crisis in 1998 when four banks were closed.

Further, we do not have spread data for UCB before its privatization; since we are aiming for a

> 12 Total earnings assets comprise Bank of Uganda bills, dues from commercial banks, other banking institutions and non- banking institutions in and outside Uganda, securities, financing schemes, loans, overdrafts, discounts, administered advances and investments. Total earnings assets do not include cash assets or fixed assets amongst others.

> 13 See Brock and Rojas- Suarez (2000) as well as Chirwa and Mlachila (2004) for a discussion of different margin definitions

10
**Figure 11: Boosting shared prosperity has largest effect where the poverty rate is close to 40%.**

<!-- Start of picture text --> m=0% m=1% m=2%<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>$1.25 poverty headcount rate in 2011<br>Note: Trendlines from lowess smoothing. Zero growth scenario.<br>1<br>0<br>-1<br>-2<br>Change in $1.25 poverty headcount rate (2011-2012)<br><!-- End of picture text -->

# **4.3 Impacts on distribution and inequality**

So far, the paper has focused on the poverty impacts of various growth and shared prosperity scenarios. Naturally, imposing a higher or lower growth rate on the bottom 40% of the distribution also has substantial impacts on inequality within countries, which we briefly summarize.

Under the scenarios with the highest shared prosperity premium, in which the bottom 40% grow 2pp faster than the mean, inequality falls rapidly, as can be seen in Table 1. The mean Gini in our sample of 124 countries falls 10 points, from 40.8 to 31.2 (around the level of inequality experienced in Albania and Pakistan in the most recent available data). A 10 point fall in the within-country Gini over a 20 year period represents a fast decline in inequality when compared to historical data, however it is not unprecedented. For example, Brazil’s Gini fell from a peak of 63.3 percent in 1989 to 53.9 in 2009. For countries that are less unequal today, inequality falls to a level which is extremely low. In fact, in the simulated results for 2030, more than 25% of countries have a Gini which is lower than the lowest Gini we observe in our dataset in 2011 (24.8% for Ukraine). Meanwhile, with the decline in the within-country Gini is around 4pp, which is more realistic when compared to historic rates of change. mm= 1% An alternative measure of inequality which is particularly relevant to shared prosperity is the share of total income received by the bottom 40%. It is directly relevant to the World Bank’s Corporate Scorecard which compares the growth in the bottom 40% to that of the mean. Of course, a positive shared prosperity premium implies that this income share increases. In 2011, the mean income share of the 19
of papers have shown that state-owned banks could be useful in smoothing the lending cycle (Micco and Panizza, 2006, , Brei and Schclarek, 2013, Cull and Martinez-Peria, 2013, Coleman and Feler, 2015, Bertay et al., 2015, Duprey 2015, Allen et al., 2017, Ture, 2021, and Panizza, 2022). Additional research that used more recent data and moved beyond pure cross-sectional regressions did not find a significant correlation between government ownership of banks and economic growth or financial depth (Levy Yeyati et al., 2007, and Panizza, 2022). Argentieri (2022) studies the effect of bank privatization in Brazil, and finds that privatized banks became more profitable, that they also contracted their credit supply, and that this credit contraction had a negative effect on economic activity (see also Sanches et al., 2018).

One problem with the existing literature is that country or industry-level data do not allow controlling for unobservable factors that are jointly correlated with state ownership and the outcomes of interest. Given that the cross-country and over time distribution of government-owned banks is not random, the correlation between the observed level of state-ownership and each of financial and economic growth could either be driven by political failures (in this case, a larger share of government-owned banks causes worse economic outcomes) or by the presence of market failures (in this case, economic outcomes cause the share of stateowned banks) or by the joint presence of these different types of failures (see Rodrik, 2012, for a formal model). Models that include country-fixed effects can partly address this problem, but only if the source of endogeneity only varied across countries and is time-invariant within countries.

The literature that uses bank-level data can partly address endogeneity concerns by looking at what different types of banks do while controlling for country-year fixed effects. However, this strand of literature can tell us what banks do (for instance if they lend more or less during recessions), but does not tell us anything about the impact of different types of bank lending on economic activity. In order to address this question, it is necessary to link bank-level information with firm-level information. While we are aware of studies that match firm-level data with bank level data (including some studies that are able to do so at the loan-by loan level, see Jiménez et al., 2014), these studies require confidential information and usually focus on just one country. To the best of our knowledge, we are the first to use firm-bank matched data for a large number of countries.

We use confidential information from the World Bank Enterprise Survey (WBES) and the bank-level dataset described in Panizza (2022) to match bank-firm information for a sample of nearly 13,000 firms in 36 countries observed over 2012-2020.<sup>3</sup> We then use this matched bank-firm data to test whether firms that

> 3 We also have data for 5 advanced economies but do not include these countries in the analysis.

4