rafmacalaba/datause-encoder-data huggingface dataset:
data with qualitative data from focus groups (World Bank, 2008). These focus groups revealed that many female business owners tend to mistrust credit institutions and fear the consequences of taking on debt, suggesting that the low use of finance among female-owned firms in Bolivia is due to demand-side constraints.

# **Table 4b: Access to External Finance** **<u>Differences in External Finance of Female- and Male-Owned Firms</u>**

||Mexico|Bolivia|Peru|Enterprise<br>Surveys|
|---|---|---|---|---|
|% of working capital financed with internal funds||6.2|-4.2|-0.8|
|% of working capital financed by banks||1.0|0.9|-0.7|
|% of investment financed with internal funds||||-1.4|
|% of investment financed by banks||||1.9|
|Participates in a ROSCA||16.3***|||
|Has aloan fromafinancial institution|-0.88|-12.8***|2.9|5.2*|

_Note_ :<sup>#</sup> In the Mexican data, this measure refers to ever having had a loan since starting the business, rather than currently having a loan. Significance levels: *** 1 percent, ** 5 percent, * 10 percent. _Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

The Mexican data and the Enterprise Surveys include information on the reasons why firms do not have a loan from a financial institution. Table 5 shows that only a relatively small fraction of female-owned firms does not have a loan because their loan application was rejected. Moreover, in the Mexican data, female-owned firm are less likely than male-owned firms to have a loan application rejected. In the Enterprise Surveys, there is no difference in loan rejection rates across female- and male-owned firms. Table 5 also illustrates that 91.4 percent of female-owned firms in the Mexican data and 48.8 percent of female-owned firms in the Enterprise Survey data do not have a loan because they did not apply for a loan. For the Mexican data, this number is 1.4 percent higher than for male-owned firms, but the size of the difference is small relative to the average of 91.4 percent. Among firms that did not apply for a loan, there is some evidence that a number of female-owned firms may be credit constrained. Female-owned firms in the Enterprise Surveys are 10.8 percent more likely to not have applied for a loan due to cumbersome application procedures. However, this result is reversed in the Mexican data, where women are 1.4 percent less likely to not apply for a loan due to cumbersome application procedures.

Overall, the data do not provide systematic evidence for the hypothesis that female-owned firms have less access to external finance than male-owned firms. This leads me to examine other possible reasons for why female-owned firms in Latin America tend to be smaller than maleowned firms.

15
# **Non-Technical Summary**

It seems sensible that international migration has no impact on the size of world population. However, this paper argues that international migration may have resulted in a smaller world population than in the non migration scenario. The author claims that most of recent migration has been from high to low birth-rate countries, and since migrants typically adopt and send back ideas that prevail in host countries, they are potential agents of the diffusion of demographic modernity to their country of origin.

The author uses data from three major origin countries: Morocco and Turkey (where emigration is bound for the West), and Egypt (where emigration is bound for the Gulf). These three countries offer contrasted situations: the host countries are either more (the West) or less (the Gulf) advanced in their demographic transition than the home country. He finds empirical evidence that time-series data on birth rates and migrant remittances (reflecting the intensity of the relationship between the emigrants and their home country) are strongly correlated. Correlation is negative for Morocco and Turkey, and positive for Egypt. This suggests that Moroccan and Turkish emigration has been accompanied by a fundamental change of attitudes regarding marriage and birth, while the opposite holds for Egyptian migration.

The broader conclusions from this paper are that migration may have caused a relaxation of demographic pressures for the world as a whole. In addition, if it turns out that emigrants are conveyors of new ideas in this area, the same may apply to a wider range of civil behavior.

2
To represent business cycles, we use per capita real income growth in percentages, with a mean value of 1.96%. The impact of state ownership on the procyclicality of banks lending possibly depends on the general effectiveness of the government bureaucracy, i.e. good governance. As a proxy of this, we use a composite indicator of the government effectiveness from the World Governance Indicators (WGI) database (Kaufmann et al., 2010), which includes perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures. This variable is reported on an annual basis only after 2001, limiting regressions that include this variable to the period 2002-2010.

We consider several bank-level control variables, all lagged one year. Among these, assets are the log of total assets in constant 2000 dollars to capture the impact of a bank‟s toobig-to-fail status. Equity is the ratio of equity to total assets, to control for bank soundness, averaging 10.6% in our sample. Cooperative bank, real estate and mortgage bank and savings banks are dummy variables that are one if a bank is in the pertinent bank category. The loans over assets variable represents the relative importance of lending in a bank‟s activities, with a mean of 0.540. Liquidity, constructed as the ratio of liquid assets to total assets, can be a measure of bank soundness and its ability to sustain its lending, as well as an indicator of inefficiency (since too much liquidity comes at the cost of bank intermediation). In addition, deposits over total liabilities is a measure of the stability of a bank‟s funding, since non-deposit funding tends to flee quickly during periods of instability. In our sample the bulk of the funding source consists of deposits, with a share of 0.771.

We consider two macroeconomic controls from the World Development Indicators (WDI, 2011) database. These are GDP per capita in thousands of constant 2000 dollars, and inflation measured as the percentage change in the GDP deflator. Finally, the bank crisis variable is a dummy variable signaling a country is experiencing a banking crisis (Laeven and Valencia, 2010).

# **3. Methodology and empirical results**

In section 3.1, we describe the system GMM estimation that we apply to our empirical specifications. In section 3.2, we present our results regarding the relative procylicality of state 7
**Figure 11: The distribution of government subsidy across providers, 2008**

<!-- Start of picture text --> 2200<br>2000<br>1800 Government subsidy<br>1600 Medical service fees<br>1400<br>Drug revenues<br>1200<br>Other revenues<br>1000<br>800<br>600<br>400<br>200<br>0<br>City hospitals County hospitals Township centers Community centers<br><!-- End of picture text --> Source: 2008 National Health Financial Report, MoH 23
39

|Marg.Propensityto Cons.|Kilimanjaro||Ruvuma||Gansu||Inner Mong|olia|
|---|---|---|---|---|---|---|---|---|
||OLS|hh FE|OLS|hh FE|OLS|hh FE|OLS|hh FE|
|Unearned income in cash|0.035<br>(0.813)|0.021<br>(0.448)|-0.036<br>(-0.373)|-0.182<br>(-1.172)|0.195***<br>(3.518)|0.195***<br>(3.674)|0.054*<br>(1.829)|0.057**<br>(2.111)|
|Unearned income in kind|0.170<br>(0.794)|0.274<br>(1.027)|0.730**<br>(2.642)|0.771**<br>(2.593)|0.13<br>(0.826)|-0.077<br>(-0.369)|-0.303*<br>(-1.713)|-0.137<br>(-0.604)|
|**_Transportation and_**|||||||||
|**_communication_**|||||||||
|Earned income in cash|0.008***|0.004**|0.027***|0.013**|0.042***|0.035**|0.045***|0.036***|
||(4.046)|(2.679)|(6.055)|(2.199)|(4.690)|(3.111)|(7.680)|(5.073)|
|Earned income in kind|0.001|0.000|0.003|0.003|0.003|0.004|0.01|0.01|
||(0.614)|(-0.108)|(0.538)|(0.565)|(0.547)|(0.836)|(0.849)|(0.869)|
|Unearned income in cash|0.037**|0.035**|0.148*|0.080|0.056**|0.04|0.049**|0.039**|
||(2.441)|(2.015)|(1.867)|(0.883)|(2.478)|(1.634)|(2.604)|(1.971)|
|Unearned income in kind|0.079*|0.043|0.298**|0.399**|0.107|0.137|0.025|0.051|
||(1.746)|(1.126)|(2.218)|(2.547)|(0.755)|(0.837)|(0.203)|(0.328)|
|**_Clothing_**|||||||||
|<br>Earned income in cash|0.004**|0.001|0.029***|0.023***|0.016***|0.013***|0.017***|0.006**|
||(2.213)|(1.328)|(11.079)|(7.737)|(6.747)|(3.675)|(7.053)|(2.354)|
|Earned income in kind|0.001|0.001|0.003|0.001|0.002|0.001|0.013**|0.008*|
||(1.012)|(0.897)|(1.321)|(0.452)|(1.643)|(0.512)|(3.083)|(1.660)|
|Unearned income in cash|0.046***|0.045***|0.133***|0.110**|0.011**|0.014**|0.022**|0.026**|
||(4.818)|(3.925)|(3.640)|(2.369)|(3.123)|(2.500)|(2.217)|(2.674)|
|Unearned income in kind|0.102**|0.071**|0.244***|0.259**|0.044*|0.011|-0.038|0.009|
||(3.058)|(2.078)|(3.576)|(2.917)|(1.651)|(0.358)|(-1.035)|(0.273)|
|**Social expenditures**|||||||||
|**_Education_**|||||||||
|Earned income in cash|0.006*|0.001|0.012***|0.007*|0.064***|0.016|0.056***|0.043***|
||(1.819)|(0.683)|(3.507)|(1.849)|(3.797)|(1.158)|(6.770)|(4.452)|
|Earned income in kind|<br>0.000|<br>0.001|<br>0.001|<br>0.000|<br>0.101***|<br>0.056***|<br>-0.034**|<br>-0.017*|
||(0.111)|(0.479)|(0.358)|(0.104)|(3.685)|(3.879)|(-2.736)|(-1.826)|
|Unearned income in cash|<br>0.017|<br>0.019|<br>0.079|<br>0.047|<br>0.063|<br>0.066*|<br>0.013|<br>0.028|
||(0.845)|(0.929)|(1.351)|(0.741)|(1.621)|(1.671)|(0.375)|(1.111)|
|Unearned income in kind|<br>0.105*|<br>0.055|<br>-0.020|<br>-0.019|<br>-0.091|<br>-0.283|<br>0.057|<br>-0.037|
||(1.717)|(1.259)|(-0.287)|(-0.246)|(-1.135)|(-1.141)|(0.519)|(-0.411)|
|**_Health expenditures_**|||||||||
|<br>Earned income in cash|0.000|0.000|0.004|0.005|0.020|0.016|0.000|-0.007|
||(-0.132)|(0.150)|(1.559)|(1.261)|(1.368)|(0.813)|(0.017)|(-0.862)|
the investment climate influences not only productivity _per se_ , but also input choices of firms. This means that in the production function regression, the error term (i.e. TFP) is likely to be correlated with the regressors (labor, capital etc.) via the investment climate, leading to bias. If possible, this approach should thus be avoided. Escribano and Guasch (2005) suggest alternative procedures.

- In the absence of panel data, an approach similar to Miguel, Gertler and Levine (2005) might be useful to alleviate some endogeneity problems. They try to explain industrialization (measured as the growth in manufacturing employment) at the district level in Indonesia over a 10 years period with social capital indicators at the beginning of the period (but find no effect!). A similar approach could be taken with indicators from ICA surveys.

Finally, note that a complete assessment of the results in the literature we have surveyed so far would also require that we look more closely at the issue of the quality and relevance of the performance proxies used as dependent variables (productivity or profit, or sales growth etc.). While entering into the details of the literature on this topic would take us beyond the scope of this paper, let us note that in general measures of firm-level productivity are much more likely to run into problems and generate biases, as the very construction process of these variables make them likely to be correlated with policy shocks and managerial decisions (Katayama, Lu and Tybout, 2006). This is not to say that alternative proxies (e.g. profit, sales or employment growth) are completely free of these problems (see Del Mel, McKenzie and Woodruff, forthcoming) but in many cases they appear to be preferable.

# _2. The Research Agenda_

There remain a number of areas in which additional research could bring interesting results. At the theoretical level, we need to develop a better understanding of the link between firms’ choices and the business climate, especially in developing countries. That means modeling decisions about investment, R&D, employment and so on, which hinge on the type of constraints revealed by the existing surveys (things like credit constraints, infrastructure bottlenecks, level of competition in goods and labor markets, volatility of macroeconomic conditions, entry costs, commitment and enforcement problems or information issues).

The type of modeling used in the literature on firms’ choices of formality might be useful here.<sup>98</sup> Together with tools from industrial organization and contract theory, this approach should provide a good basis to formalize insights on market behavior in developing countries. Additionally, results could then be used to understand the very different shapes of firms’ distributions we see across countries, for example in terms of size, productivity or exporting behavior, and guide the empirical applications.

> 98  For instance: Rauch 1991; Straub 2005; De Paula and Sheinkman 2008.

29
_Table 5: Determinants of energy poverty (Marginal Effects) (measured via the energy poverty index)_

||(1)|
|---|---|
|VARIABLES|EnergyPoverty|
|Members|-0.135***|
||(0.000569)|
|Adult equ.|0.139***|
||(0.000554)|
|Female-headed|0.0402***|
||(0.000409)|
|HH with +1 unemployed|0.203***|
||(0.000641)|
|Single-elderly HH|0.197***|
||(0.000907)|
|HH with +1 child|-0.161***|
||(0.000795)|
|HH with +1 disabled|0.0705***|
||(0.000723)|
|HH with +5 members|0.0480***|
||(0.00105)|
|HH with +1 pensioner|0.00778***|
||(0.000631)|
|Social-aid recipient HH|-0.0316***|
||(0.000831)|
|Urban|-0.0425***|
||(0.000422)|
|Female|0.0197***|
||(0.000434)|
|Unemployed|0.0653***|
||(0.000981)|
|Age|-0.000325***|
||(1.90e-05)|
|Elderly|0.0230***|
||(0.000995)|
|Child|-0.0220***|
||(0.00116)|
|Disabled|0.0411***|
||(0.000823)|
|Pensioner|0.0190***|
||(0.00107)|
|Social aid|-0.0131***|
||(0.000612)|
|Observations|6,962,414|

_Notes: The table presents marginal effects from a Probit model that estimates the correlation between individual and household characteristics on energy poverty. Energy poverty relies on the definition of the energy poverty index (see Section 3.3). Source: Own estimates based on EU-SILC (2020)._ 31
shows the share of exports excluded from the preferences, which was around 4% of exports in 2001. There were again substantial differences between countries; while, for example, 92% of Fiji’s exports were excluded from preferences, in the case of Botswana no exports were excluded from the preferences. The final column presents the share of exports eligible for preferences, which for the country group as a whole was around 31%. Although the preferential scheme seems to be significant for several countries, some countries trade mainly in products where the MFN tariffs were already reduced to zero limiting the impact of Cotonou preferences on these countries’ trade.

**<u>Table 2 Coverage of preferences</u>**

||Exports to the<br>EU (1000Eur)|Exports of 0<br>duty products|Exports excluded<br>from Cotonou<br>preferences|Exports eligible<br>for Cotonou<br>preferences|
|---|---|---|---|---|
|Antigua,Barb|<br>351387|99%|0%|1%|
|Bahamas|<br>608919|35%|0%|65%|
|Barbados|<br>85112|48%|31%|21%|
|Belize|81037|8%|36%|56%|
|Botswana|<br>1159120|91%|0%|9%|
|Cameroon|<br>1735020|79%|0%|21%|
|Congo|<br>384645|96%|0%|4%|
|Dominica|<br>25622|10%|0%|90%|
|Dominican R.|<br>313795|44%|0%|56%|
|Fed.Micron.|<br>57|96%|0%|2%|
|Fiji|103679|3%|92%|5%|
|Gabon|<br>1170255|94%|0%|6%|
|Ghana|1058739|56%|0%|44%|
|Grenada|<br>33881|94%|0%|6%|
|Guyana|<br>204075|26%|67%|6%|
|IvoryCoast|<br>2054493|62%|1%|37%|
|Jamaica|<br>569589|2%|14%|84%|
|Kenya|<br>920723|42%|5%|53%|
|Marshall Is.|<br>114954|100%|0%|0%|
|Mauritius|<br>1287726|10%|23%|67%|
|Namibia|<br>746864|49%|0%|51%|
|Nigeria|6371696|95%|0%|5%|
|Palau|64|100%|0%|0%|
|Papua N.G.|<br>284574|55%|0%|45%|
|Senegal|<br>450001|27%|0%|73%|
|Seychelles|<br>189410|14%|0%|86%|
|South Africa|<br>15377512|61%|4%|35%|
|St Lucia|<br>48128|45%|0%|55%|
|St Vincent|<br>153671|75%|0%|25%|
|St.Ch.&Nevis|<br>14057|2%|81%|18%|
|Surinam|<br>153576|3%|7%|89%|

11
# **Table 6**

# **Contemporaneous Herding**

This table presents the average Lakonishok et al. (1992) herding statistic over all assets and by asset class. The herding statistic is calculated using the asset-specific probability of buying an asset at any point in time. Column (1) presents the results considering all assets, column (2) considers assets traded by more than one PFA, and column (3) considers assets traded by more than half of the PFAs in operation at any point in time. Numbers represent percentages (results are multiplied by 100). T-tests are two-tailed. One asterisk indicates statistical significance at the ten-percent level and two asterisks indicate statistical significance at the five-percent level. Standard errors are presented in parentheses. In addition, column (4) presents the average asset-specific probability of buying an asset, calculated over all assets and by asset class. The dashes in column (3) indicate that Foreign Equity is not traded by more than half of PFAs in operation.

|||HerdingStatisti|c||
|---|---|---|---|---|
|||||Average Probability of|
||All Assets|Assets Traded by More<br>Than One PFA|Assets Traded by More<br>Than Half of PFAs|Buying an Asset|
||(1)|(2)|(3)|(4)|
|**All Asset Classes**|2.26**<br>(0.03)|0.88**<br>(0.04)|1.77**<br>(0.09)|54.6%|
|**Domestic Assets**|||||
|Former Pension System Bonds|-2.53**<br>(0.04)|-11.02**<br>(0.08)|2.07**<br>(0.29)|66.3%|
|Corporate Bonds|2.38**|5.04**|5.74**|534%|
||(0.25)|(0.61)|(0.52)|.|
|Financial Institutions|0.81**|1.86**|1.66**|735%|
||(0.08)|(0.16)|(0.58)|.|
|Government Paper|-0.10|-2.45**|2.73**|610%|
||(0.07)|(0.15)|(0.42)|.|
|Investment and Mutual Funds|2.41**|3.03**|1.35**|575%|
||(0.61)|(1.25)|(0.56)|.|
|Equity|0.96**|1.28**|0.66**|534%|
||(0.18)|(0.24)|(0.25)|.|
|Mortgage Bonds|8.84**|4.45**|0.92**|249%|
||(0.06)|(0.06)|(0.11)|.|
|**Foreign Assets**|||||
|Fixed Income|-0.01<br>(0.23)|3.09**<br>(1.00)|15.60**<br>(5.14)|61.8%|
|Investment and Mutual Funds|1.43**|2.23**|1.51**|624%|
||(0.12)|(0.21)|(0.28)|.|
|Equity|-0.23|-0.32|-|672%|
||(0.34)|(1.79)|-|.|
**-TeachrNs4o-tidhwr** Pay Dlffhrwrc 15-

# **Table VI**

## **Regression Rosults**

-__________..-------------------------------------------

||(1)|(2)|
|---|---|---|
|Dependent**Variable**|Total<br>**Salaries**|Total<br>**Salaries**|
||Non-Teachers|Teachers|
|Years of Schooling|0.1664*|0.1728*|
||(13.17)|(6.82)|
|Diploma|0.1495|----<br>1)|
||(0.87)|----|
|Expirience|0.1234*|0.0664*|
||(7.73)|(2.20)|
|Experience<br>2|-0.002*|-0.0(11|
||(-3.81)|(-1.19)|
|Log of Mthly Hrs Worked|0.2064*<br>(1.93)|-0.0222<br>(-0.21)|
|Work for Government (Yes-i)|-0.0683|0.2744|
||(-0.74)|(1.31)|
|SeY (Female-1)|-0.217_*|0.0257|
||(-2.07)|(0.17)|
|Citizenship (Ivorian-l)|0. 1664<br>(040o)|0.0550<br>(0.18)|
|Location (Abidjan-1)|-0.0988|-0.2278|
||(-1.15)|(-1.22)|
|Constant|7.8755*|9.2830*|
||(12.61)|(13.63|
|R<sup>2</sup>|0.54|0.54|
|F|37.66|8.78|
|Number of Cases|279|54|

------------------------------------------------------

T-values between parentheses.

* Indicates stati.tically significant coefficients at the 0.10 level. 1) All teachers have diplomas.
<!-- Start of picture text --> = — Switches within Funds — - «Benefits + Payouts Transfers across funds<br><!-- End of picture text --> <!-- Start of picture text --> 23% Chile 2s Colombia<br>20% , Sy S - 20%<br>é ue<br>v<br>15%. i 15%<br>ee<br>fe N<br>10% f 10% \<br>é \<br>¢ \<br>5K ‘ 5K \ =<br>me ey -—— a<br>See te ee THe oe ae Sissy eee Se<br>O% mn<br>2005, 2006 oT 2008 2009 2010 2014 2012 2013 214 2015 2005, 2006 2007 20 2009 210 2011 2012 2013 mis 2015,<br><!-- End of picture text --> <!-- Start of picture text --> Costa Rica Mexico<br>25%. iss<br>20% Po<br>iss 15s<br>10% 10% e*<br>a Ney Fa ‘ 3<br>See ete<br>sy Sh<br>«, Seeee<br>2005 2006 oy 2008 2009 2010 2018 2012 2053 eee2014 2015 ag 2005,ne2006 eee2007 2008 2009 20 2011 2012 2013 Pony 2015,<br><!-- End of picture text --> <!-- Start of picture text --> Peru<br>35%<br>-<br>vt<br>30% é S<br>f \<br>e Fd \<br>25% ‘<br>\<br>20%. \<br>\<br>18%. ‘<br>\<br>10% seen el 3<br>ou a<br>- ee ae ee =<br>O%<br>2005 2006 207 2008 2009 210 2012 2012 2033 mi 2015<br><!-- End of picture text -->
**The Role Of Factoring In the New EU Member States**

The significant growth in factoring in most of the EU 8 suggests that factors in these countries have been able to generate databases on domestic accounts, rely on external sources of such information (such as business credit bureaus and credit insurers), or target exporters and finance their cross-border receivables. Moreover, if the EU 15 can be used as a benchmark, there is considerable room for further growth.

**Figure 5. Factoring Volume in the EU 8 as Percent of GDP**

<!-- Start of picture text --> 16.00%<br>14.00%<br>12.00%<br>10.00%<br>8.00%<br>6.00%<br>4.00%<br>2.00%<br>0.00%<br>Czech Estonia Hungary Latvia Lithuania Poland Slovakia Slovenia EU 8<br>Republic<br>1996 1997 1998 1999 2000 2001 2002<br>Source: Factors Chain International, World Bank.<br>% of GDP<br><!-- End of picture text -->

**Figure 6. Factoring Volume in Selected EU 15 Countries as a Percentage of GDP**

<!-- Start of picture text --> 14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>France Germany Ireland Italy Portugal Spain The Netherlands UK<br>1996 1997 1998 1999 2000 2001 2002<br>Source: Factors Chain International, World Bank.<br>% of GDP<br><!-- End of picture text -->

# **FACTORING ENVIRONMENTS IN THE EU 8: SURVEY RESULTS**

To better assess the factoring environment in the EU 8, the World Bank conducted a survey of factors doing business in these countries. There were 13 respondents (all privately owned and operated

**25**

**THE WORLD BANK**
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