rafmacalaba/datause-encoder-data huggingface dataset:
more rigorous analysis to future research, this result is consistent with the results of Sabarwal and Terell (2008) that finds that the return to scale of female owned firms is significantly larger than men’s and that the main reason for the sub-optimal size of women owned firms is likely to be barriers to formal financing.

# **6.  Conclusions**

In this paper we systematically study the use of different financing sources for new and young firms. We use a unique dataset from over 170 surveys, which contain about 70,000 firms, most of which are small and medium sized (SMEs) in 104 developing and developed countries, including many low-income countries. We use this dataset to examine corporate financing decisions: First, the relationship between firm age and sources of external financing, and second, the differential impact of business environment on access to financing by young versus older firms.

As expected, we confirm that in all countries younger firms have less reliance on bank financing and more reliance on informal financing. The relationship with leasing and trade credit is less associated with firm age.

Most interestingly, we explore the interaction of firm age and the business environment and the relative impact of the business environment on a young firm’s mix of financing sources. We find that younger firms are more likely to use bank finance in countries with better rule of law. While younger firms are less likely to use bank financing in all countries, they have worse access in countries with poor rule of law. Similarly, we find that credit information has a differentially positive effect on the use of bank finance by young firms; this might highlight the importance of personal credit 20
**16**

Bover system GMM estimator (1995) with the finite sample correction of the two-step standard errors proposed by Windmeijer (2005).<sup>13</sup> We use lagged differences of the predetermined variables as instruments for the predetermined variables ( _Y_<sup>_a_</sup> _it-k_<sup>and</sup><sup>_Yn_</sup> _it-k_<sup>) in our levels equation and lagged</sup> levels of the predetermined variables as instruments for the first difference equation.<sup>14</sup> The analysis utilizes 3-year averages of log per capita GDP over the period 1960-2004. For the estimations we exclude OECD-countries and countries from Eastern Europe and Central Asia. The former have typically already passed through the structural transformation and the economic systems of the latter have undergone dramatic structural change over the past 15 years rendering their historical experience atypical of the remaining sample. The estimation sample consists of 106 countries and 1489 observations, but missing observations on the GDP figures and the use of lagged dependent variables in the regressions lead to a reduction in the actual regression sample sizes. Sub-Saharan Africa is comparably well represented with almost half of all countries and observations in the sample.

Table 3 reports the findings for the regressions of non-agricultural GDP. Non-rejection of the reported 2nd-order autocorrelation tests indicates that the autocorrelation of the models is in general well-specified.<sup>15</sup> The Sargan-Hansen-tests are not rejected, providing confidence in our set of instruments. As the linkage effects are also likely to differ depending on the stage in the structural transformation (see also Bravo-Ortega and Lederman, 2005), results are presented for the full sample (column 1) as well as for middle and low-income countries separately (columns 2-3 and 4-5 respectively).<sup>16</sup> A Sub-Saharan Africa indicator variable is subsequently introduced to examine whether linkages in SSA differ from those observed in other low and middle-income countries respectively.

> 13 The use of estimated parameters for the construction of the weight matrix introduces extra variation and a difference between the finite sample and the usual asymptotic variance of the two-step GMM estimator.  Windmeijer (2005) shows that this difference can be estimated for a finite sample corrected estimate of the variance. 14 A more detailed exposition of the specification and estimation strategy is provided in Appendix A1. 15  The regressions are estimated in differences, and 1st-order autocorrelation is therefore expected, while 2nd-order autocorrelation is a sign of serial correlation in the levels.  Further lags in non-agricultural GDP were added in some specifications to adjust for serial correlation in the levels.

> 16 To be consistent with the classification applied in the poverty regressions in section 5, we us the same cut-off value of US$ 1160 GDP per capita to classify countries.
Page 4 Figure IV.1: Development Plan of Ahmedabad, 1965 Figure IV. 2: Neighborhood Improvements under SNP Figure IV.3: Process for Conversion of Rural Land already Zoned for Urban Development Figure IV.4: Progress of Town Planning Schemes Started under the new DP Since 2003 Figure IV.5: Process for Brownfield Urban Redevelopment Figure IV.6: Built Up Density Profile in AMC 1991-2001 Figure IV.7: Ahmedabad Built Up Densities in 2001 Figure IV.8a: Existing Formal Housing that would be Illegal for New Construction under the GCDR Figure IV.8b: Existing Formal Housing that would be Illegal for New Construction under the GCDR Figure IV.8c: Existing Formal Housing that would be Illegal for New Construction under the GCDR Figure IV. 9a: Possible Plot Layout according to GCDR Figure IV.9b: Plot Layout with modifications to GCDR Figure V.1: Before and After Slum Networking in Ahmedabad Figure V.2: Typical Lower Middle Class Housing in Ahmedabad Figure V.3: Distribution by Dwelling Size in Sample Slum Pockets

# <u>List of Tables:</u>

Table II.1: Population and Urbanization Growth in Ahmedabad 1971-2011 Table II.2: Growth Rates of Employment and Population 1991-2001 Table IV.1: Town Planning Schemes in Ahmedabad Table IV.2: Key Infrastructure built by AUDA between 1999 and 2010 Table IV.3: Land Appropriated in all TP Schemes in Ahmedabad, 2010 Table IV.4: Key Indicators for Vernacular and GDCR housing Table IV.5: Comparison of Key Specifications for GDCR and Modified GCDR Specification for More Affordable Housing Table IV.6: Cost Comparison with marginal flexibility in the GCDR Table V.1: Summary of Total Costs of Upgradation in Ahmedabad Table V.2: Per Household Upgrading Costs Table V.3: AMC Budget 2008-09 through 2011-2012 Table V.4: Estimates of Populations Unsuitable for In-Situ Upgradation
14 2014; Palacios-Lopez and Lopez, forthcoming) may affect female-managed plots more than malemanaged plots. Similarly, if the substitution of capital for labor is gender-sensitive, the use of laborsaving technologies, such as agricultural implements, might affect the level of women’s labor input into agriculture. In addition to the number of adult household members by gender/age groups, the total number of hired and exchange labor days as well as the number of agricultural implements owned and accessed are included.

Second, culture-specific gender roles determine the capacity of men and women to allocate labor time across economically productive activities and to respond to economic incentives (Ilahi, 2000; Blackden and Wodon, 2006). Domestic responsibilities, such as childcare and caring for the sick, water and firewood collection, and cooking, are usually in the female domain. They are easier to combine with on-farm work close to the homestead than with off-farm employment (Blackden and Morris-Hughes, 1993; Palacios-Lopez and Lopez, forthcoming). To capture the potential effects of these dynamics on the female agricultural labor share, the number of children in the household under 5 and between 6 and 14 years old are included as well as the percent of female and male adults suffering from chronic diseases. Given the possibility of differential control over the proceeds from cash and food crops by gender, the overall female labor share in crop agriculture may further depend on the land allocation to different crops (as well as the overall amount of land cultivated).<sup>19</sup> As these differential gender controls are deeply culturally determined, with potentially substantial differences across ethnic groups, the ethnicity of household head is further controlled for.

Socio-economic factors behind individual labor allocation decisions across sectors include gender differences in education, which may affect the scope of off-farm opportunities available to

> 19 The total land cultivated is based on GPS-based plot area measures, with missing GPS based plot areas imputed following Palacios-Lopez and Yacoubou Djima (2014).
**<u>Figure 4.4:      Squared Urban Poverty Gap (P2), 1981-2001</u>**

<!-- Start of picture text --> 16.0<br>Brazil North<br>14.0<br>Pará<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1981 1985 1990 1992 1993 1995 1997 1998 1999 2001<br><!-- End of picture text --> _Source:_ Calculations based on selected years of PNADs in 1981-2001.

The income of the extreme poor is stagnant **.** One possible explanation for the recent increase in the depth of poverty could be the drop in average incomes of the extreme poor. Data for 1995-2001 reveal that after the launch of the inflation-beating Real Plan in 1994 average per-capita household incomes fell in urban areas in Brazil and in the northern states, including Pará (see Table 4.6). In 2000, the average per-capita income of the extreme poor urban household heads of R$34 was less than a fourth of the mean income of the non-poor in Pará. Furthermore, the average per-capita household income of the extreme poor in urban areas fell marginally more in Pará than in other states in the Amazon region and in Brazil as a whole.

**Table 4.5:  Mean Income of the Extreme Poor in -** **<u>Urban Areas, Selected Years 1995 2001 (R$)</u>**

||**1995**|**1997**|**1998**|**1999**|**2001**|
|---|---|---|---|---|---|
|Brazil|33.4|32.4|33.1|33.7|31.9|
|North|35.5|34.2|35.0|34.6|33.7|
|Rondônia|37.0|38.0|39.3|31.4|35.1|
|Acre|33.1|27.2|34.7|33.5|30.9|
|Amazonas|34.4|34.6|33.3|34.3|32.2|
|Roraima|32.1|42.2|36.6|31.5|31.0|
|Pará|36.8|34.4|36.1|35.7|34.3|
|Amapá|37.1|30.9|29.8|32.0|28.6|
|Tocantins|33.7|33.8|34.8|35.0|35.9|

_Source_ : Authors calculations based on PNADs 1995-2001 _._ 24
therefore interpret both pieces of evidence as generally supportive (albeit weakly) of the private interest hypothesis.

Panel B shows that the chances of being a banker are not higher when the same political sector is in power as when the politician was in office. Under the tollbooth view, politicians can more effectively extract rents from the bank while they are in power. This implies that we should more often observe that the politician bankers are of the same political sector as the ones currently in power. Neither the regulatory capture nor the public interest views require such correlation. Under the regulatory capture view, politicians may be paid ex-post for favorable regulatory treatment, even if their party has left power. Of course, the public interest hypothesis does not require any correlation between the presence of a former politician in a bank’s board and the party in government.

# **3.2. What Banks Are Politically Connected?**

In this section we define the bank as the unit of observation and correlate bank characteristics with whether the bank is politically connected or not, that is, whether it has a board member whose name was matched to a name in our sample of politicians. In particular, we look at measures of size, profitability, leverage, riskiness and liquidity that were constructed directly from Bankscope data using bank’s statements at the end of 2004. As explained above, most of our board composition information comes from the years immediately before 2004 (67 percent of all information comes from the years 2003 and 2004). By focusing on data from the end of 2004 we ensure that the financial information compared comes from a year in which a bank either is or has already been connected and, at the same time, avoid pooling nominal data from different years. This year also has the advantage of not comprising any banking crisis. However, this restricts a little our sample, since some connected banks either do not exist or do not report financial data in 2004. While across all years there are 4,601 banks with information on board composition across of which 194 banks were connected, we have financial data in 2004 for 3,863 banks (3,271 privately-owned and 753 foreign) . Of these, 168 banks in 75 countries have at least one politician in their board.<sup>8</sup> We estimate the parameters of the following parsimonious specification in the sample of banks for which we have information on board composition and financial data at the end of 2004 _Yi_ , _c_     _CONNECTEDi_ , _c_  _X i_ , _c_  _c_   _i_ , _c_ (1)

> 8 The complete list with the number of banks with data and the number of connected banks by country is available upon request.

9
Following other papers that have researched the impact of changes in GDP composition on tax revenues, I conduct a formal empirical analysis of how VAT revenues as a share of GDP are affected by changes in net exports as a share of GDP.

**Figure 5:  The relationship between net-exports and VAT revenues as a share of GDP is negative 2000-2012**

<!-- Start of picture text --> Relationship Between Net Exports and VAT Revenues<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15<br>VAT Revenues to GDP ratio<br>40<br>20<br>0<br>-20<br>Net Exports to GDP Ratio<br>-40<br>-60<br><!-- End of picture text --> _Source:_ World Bank staff estimates based on government accounts.

In order to do explore the relationship further, I use the basic regression below: Where VAT/GDP is the ratio of VAT revenues to GDP, TOT is an index of the terms of trade, GDPGROWTH is the growth rate of real GDP, NETEXPORTS/GDP is the ratio of net exports to GDP and GDPPERCAPITA is constant per capita GDP. The lagged VAT share is included to account for persistence in revenues. GDP growth is expected to affect tax revenues: in good times tax collection improves. The output gap is added as a regressor to account for additional revenue effects related to boom and bust periods. Regression results are shown in Table 4. The results indicate that, as expected, net exports and revenue collection from the VAT as a share of GDP are negatively related and the relationship is always strongly significant. The first regression shows the OLS pooled simple regression, the second column shows the same regression augmented by country fixed effects. The magnitude and significance of the coefficient on the net export share increase in the second specification. Subsequent specifications add the variables that theory dictates would predict VAT revenues and include either time 15
� _s_<sup>_c_</sup> _h_<sup>_ε_</sup> _K_<sup>_c_</sup> _h_<sup>+ (1</sup><sup>_−sc_)</sup><sup>_ε_</sup> _K_<sup>_a_</sup> _h_ � _d_ ln _K_<sup> ̄</sup> _h_ in the first order approximation (5), which can be sizeable. Second, the impact of the price change due to the AD policy should be expected to be heterogeneous across farmers and to depend on the exposure to the shock, measured in equation (5) by the income shares _s_<sup>_c_</sup> _h_<sup>.Notefinallythattheoverallimpactoftheshockmay</sup> be a non-linear function of those shares if _d_ ln _p_<sup>_c_</sup> _h_<sup>,</sup><sup>_εi_</sup> _Kh_<sup>and</sup><sup>_d_ln</sup><sup>_K_ ̄</sup><sup>_h_arethemselvesfunctions</sup> of _s_<sup>_c_Thismaybethecaseifproducerswhodifferintheirrelianceoncatfishincomealso</sup> _h_<sup>_._</sup> differ in their ability to bargain on the sale price to the catfish processing industry, in the magnitude of their adjustment costs, or in the extent of intrahousehold spillovers.

# **4.1 Empirical Strategy**

In all our estimated regressions, we use only data from households involved in fish farming and residing in one of the provinces of the Mekong regions where catfish production is concentrated (the M4 and M6 samples defined above). In the absence of a randomized experiment, it is hard to find a suitable control group for catfish Mekong producers. Non-aquaculture households in the target samples are not suitable controls because of the likely self-selection into different economic activities. Also, non-aquaculture households may have been hit indirectly by the AD measure through general equilibrium effects. Aquaculture households in the rest of Vietnam are not a good control group either. On the one hand, the vast differences in trends and in recent history between North and South Vietnam prevent using the Red River delta region as a control. On the other hand, the non-Mekong South specializes in brackish and marine aquaculture, especially shrimp farming, and these activities are likely exposed to different trends relative to catfish farming. Our estimation strategy thus relies on comparing household outcomes before and after the introduction of the U.S. AD duties across households with different levels of exposure to the shock. Concretely, let _Yh_ be one of the outcomes that we explore below (income, income shares, sources of income, and input choices). We study the following model for the change in outcomes, ∆ln _Yh_ , from 2002 to 2004: 19
<!-- Start of picture text --> Figure 1: Health cases in 4 cases in 4 in 4 4 state ib unals,byby date of filing of filing filing<br>2000<br>ee,i <a!<br>oe2<br>Sgee_,<br>ong<br>woTg PSC‘CO;<br>wf<br>1965 1965 ise? t= =] =] 2000 2001 20c2 2003 2048<br><!-- End of picture text --> Figure 1: Health cases in 4 cases in 4 in 4 4 state ib unals,byby date of filing of filing filing
**Table 7. The Investment Equation by CEO government connection**

||**Entrep**|**reneurial CEOs**|**Government-ap**|**pointed CEOs**|
|---|---|---|---|---|
||**OLS**|**FE**|**OLS**|**FE**|
|ln(GDP per capita)|-0.026||0.022||
||(-1.362)||(0.710)||
|ln(city pop)|0.015||0.013||
||(0.945)||(0.485)||
|ln(firm age)|-0.003***||-0.000||
||(-4.806)||(-0.654)||
|Non-state|0.003||0.067***||
||(0.104)||(2.818)||
|Foreign|-0.067*||0.009||
||(-1.771)||(0.161)||
|1(access to loan)|0.175***||0.100***||
||(8.812)||(3.723)||
|trade credit|0.093**||-0.059||
||(2.039)||(-1.051)||
|UCA|0.036***||0.003||
||(4.915)||(0.294)||
|sales/lagged K|0.011***|0.043***|0.008|0.068***|
||(5.923)|(30.438)|(1.164)|(12.941)|
|CF/LAGGED K|0.111***|0.091***|0.045|0.066**|
||(5.887)|(8.418)|(1.008)|(2.088)|
|Lagged sales growth|0.022***||0.000||
||(3.279)||(0.008)||
|Tobin’s Q|0.256***|0.155***|0.030|0.192**|
||(3.381)|(2.814)|(0.388)|(2.134)|
|Number of observations|8,693|19,934|1,261|2,692|
|Adjusted R2|0.084|0.144|0.049|0.133|
|p-value for the test of equality of<br>CF/lagged K for OLS||Entrepreneur = gov|ernment CEO: 0.205||
|p-value for the test of equality of<br>CF/lagged K for FE||Entrepreneur = gov|ernment CEO: 0.655||
|p-value for the test of equality of<br>Loan for OLS||Entrepreneur = gov|ernment CEO: 0.017||
|p-value for the test of equality of<br>trade credit for OLS.||Entrepreneur = gov|ernment CEO: 0.032||

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.

48
# Research Components of Wortd Bank Education<sup>Projects,</sup> since 198Z.

|Area|_m<br>Countryand Loan/Creditl<br>dNumber|_1I<br>Approved||<br>_<br>I<br>Research<br>Components<br>j|Current<br>Status<br>of Studies,<br>including<br> CompletedDocuments/Data|
|---|---|---|---|---|
||||I<br>(iii) tracer study of secondary school<br>graduates'<br>performance<br>in<br>the labor<br>market;<br>(iv)<br>evatuation<br>impact<br>study<br>to cover<br>training<br>of teachers<br>and managers,<br>production<br>of<br>equipment prototypes, and equipment<br>distribution<br>(para.<br>38 p.12).|and<br>A. Starrit<br>"Assessment<br>of,<br>and assistance<br>to,<br>Math<br>educators." (doc. # 433.600)|
|ASIA|Indonesia LN 2599-IND|1986|Science and Technology Training.<sup>Project</sup><br>designed to strengthen countrv's<sup>research and</sup><br>development<br>capacity.<br>No research<br>components.<br>Feedback<br>seminars<br>for<br>returning<br>fellows<br>to<br>investigate<br>relevance<br>of program<br>to country<br>needs.|No information supplied.|
|ASIA|Indonesia LN 2705 ID|1986<br>|| Manpower Development and Training.<br>(i) Labor market studies in key fields<br>(employment of women, youth training etc)<sup>as</sup><br>part<br>of<br>labor<br>market<br>information<br>system<br>(para.<br>2.05 p.12).<br>(ii)<br>Improve<br>manpower policies<br>for<br>Repelita<br>V<br>(AnnexV).<br>(iii) Tracer studies and evaluation of<br>instructor training programs (para.<sup>2.18 p.15)</sup><br>(iv) Tracer studiesof trainees<br>and data<br>cost<br>effectiveness<br>(para.<br>2.28 p.17).|(i) Report on the findings of the July<sup>1984</sup><br>industrial survey. (doc. * E00069);<br>(iii) 0. Clark (1985). "Tracer study<sup>of BKLI</sup><br>graduates"<br>(doc.<br>*E00071);<br>also<br>Indonesia<br>MOE (1986).<br>"Manpower<br>Planning<br>and<br>Labor Market<br>Information<br>System"e (doc.<br>S EOOO5Z)|
|ASIA|Indonesia LN2940-lND|1988|Accountancy<br>Devetopment<br>Project.<br>Evaluation<br>component only through Coordinating<sup>Agency for</sup><br>Accountancy<br>Development<br>(para.<br>2.41 p. <sup>2 7</sup><br>Annex<br>6).|(i)<br>TOR drawnup for accounting manpower<sup>study.</sup><br>(doc.#F27309)|
|ASIA|IndonesiaLN2944-lND||1988|Higher Education Development Project. Eight<br>major education sector planning and<sup>management</sup>|No information sWpplied.|

42
Since the term α��∑�� A��� does not stem from a theoretical kernel in a data generating process, it would be hard to interpret.

The result of these tests of functional form – testing equation (8) against (10), and (8) against (14), was conclusive: the fit of equation (8) was better as gauged by the within R‐squared (equation (8) had 0.15, while (10) had 0.13 and (14) had 0.12). J‐test and Cox‐Pesaran tests for non‐nested models showed likewise (see Table 2).

**Table 2.  Non‐nested tests of functional form**

|**Non‐nested equations compared**|**Dep. var.**|**J‐test**||**Cox‐Pesa**|**ran test**|
|---|---|---|---|---|---|
|||Statistic|P‐val.|Statistic|P‐val.|
|Cumulated policy actions (8) versus<br>cumulated loggedpolicyactions(10):||||||
|H0:(8),H1:(10)|CPIA A&B|‐0.8|0.43|1.60|0.055|
|H0:(10),H1:(8)|CPIA A&B|2.92|0.004|‐8.35|0.00|
|H0:(8),H1:(10)|Ec. Fr. Index|0.27|0.78|‐0.85|0.20|
|H0:(10),H1:(8)|Ec. Fr. Index|1.65|0.101|‐5.94|0.0|
|Cumulated policy actions (8) versus<br>their level and square(14):||||||
|H0:(8),H1:(14)|CPIA A&B|‐0.69|0.49|1.43|0.076|
|H0:(14),H1:(8)|CPIA A&B|3.64|0.00|‐13.8|0.00|
|H0:(8),H1:(14)|Ec. Fr. Index|0.14|0.885|‐0.48|0.32|
|H0:(14),H1:(8)|Ec. Fr. Index|2.51|0.013|‐11.8|0.00|

The statistics in the first two lines of the table (enclosed in the shaded box with heavy lines) may be interpreted thus: the null that (8) with cumulated policy actions is preferred over (10) with cumulated logged policy actions is not rejected because the P‐value is 0.43, while the null that (10) is preferred over (8) is rejected because the P‐level is 0.004, well below the standard threshold of significance of 0.01 or 0.05. All the tests—within R‐squared, J, and Cox‐Pesaran— point in the same direction, namely that the best‐fitting model is (8).

# (ii) Stationarity and cointegration tests

Since the levels equation includes variables that grow over time, such as GDP per capita and the sum of the number of all past policy actions, it is likely that at least some of the variables are integrated of order 1 or more. So panel unit root tests were conducted.

12