rafmacalaba/datause-encoder-data huggingface dataset:
-13-

To test econometrically the impact of policy improvement during project implementation, we modified the basic multivariate analysis to control for initial conditions of the black market premia (table 6). Selected specifications which control for fixed country effects confirm the statistical robustness of the relationship between policies and ERRs, controlling for initial policy conditions: economic reforms within a country seem to yield payoffs within a few years.

**Table** 6. ERRs and policy reforms: controlling for initial conditions

||Not controlling for initial policy<br>conditions|Black market premia change<br>during project implementation|
|---|---|---|
|Intercept|87.5|82.4|
|Black market premia at<br>project evaluation|-0.046<br>(2.5)**||
|Black market premia at<br>project appraisal||-0.031<br>(1.0)|
|Premia change since<br>project appraisal||-0.047<br>(2.5)**|
|Capital/labor ratio|-6.8<br>(1.8)*|-6.2<br>(1.6)|
|Education years|-1.6<br>(0.9)|-1.7<br>(1.0)|
|Project complexity|-2.8<br>(2.0)**|-2.7<br>(2.0)*|
|Terms of trade change|0.02<br>(0.2)|0.02<br>(0.2)|
|GDP growth|0.06<br>(0.2)|0.07<br>_(0.5)_|
|Country fixed effects|Yes***|Yes***|
|Log likelihood|-2368|-2369|
|No. of observations|624|624|
|Notes: Dependent variable<br>projects (with black<br>The intercept was n<br>Numbers in parenth|is reestimated economic rate of return (<br>market prenia data available at project<br>ot suppressed in these specifications; a c<br>eses are t-statistics.|ERR) for public and private<br>appraisal and evaluation).<br>ountry dummy was omitted.|

Significance levels: *** = 99 percent; ** = 95 percent; * = 90 percent. Source: Authors' calculations.

V. The Effect of the Overall Public Investments Program on Individual Proiect Productivity In the developing world, governments have been responsible for the provision of basic infrastructure services--in transport, energy, and agriculture (World Bank 1994b).<sup>22</sup> Such public investments 22 The econornic justification for such public investments are familiar. These services enjoy a substantial public good component, and their production and provision are often subject to extemalities and/or large economies of scale. Thus, the private sector will be less likely to provide them or may do so in less than optimal quantities.
exists at the firm and industry levels (Demirguc-Kunt and Maksimovic, 1998 and Rajan and Zingales, 1998). More recently, Beck, Demirguc-Kunt and Levine (2004) have shown that financial development also leads to lower levels of poverty and inequality.

By analyzing the impact of remittances on financial development, our paper not only examines an unexplored potential determinant of financial development, but also this study investigates a new channel through which remittances can affect economic development. Most studies on the development impact of remittances have focused on issues such as poverty, education, entrepreneurial activity, and health. Research on the impact of remittances on poverty using household data suggests that these transfers help reduce the level of poverty, but have an even greater influence on its severity, as measured by the poverty gap (e.g., Adams, 2004, on Guatemala; Lopez-Córdova, 2005, and Taylor, Mora, and Adams, 2005, on Mexico). In addition, Maimbo and Ratha (2005) find that in terms of poverty reduction, rural areas in developing countries tend to benefit the most because much of the world’s migrants are drawn from these areas.

The finding that remittances help to reduce poverty is confirmed in cross-country studies. Based on a dataset of 74 low and middle-income developing countries, Adams and Page (2003) find that remittances have a statistically significant impact on reducing poverty. This result is also corroborated in a separate analysis for 101 countries over the period 1970-2003, reported in the IMF’s 2005 World Economic Outlook.

Studies that analyze the impact of remittances on education such as Cox and Ureta (2003), for the case of El Salvador, Yang (2005), for the case of Philippines, and Hanson and Woodruff (2003) and López-Córdova (2005), for Mexico, find that by helping to relax household constraints, remittances are associated with improved schooling outcomes for children.

6
# New Firm Formation and Industry Growth: Does Having a Market- or Bank-Based System Matter?

# Thorsten Beck and Ross Levine

JEL Classification: Gl; G2; 04

## Keywords: Financial Structure; Economic Growth; Extemal Finance

Beck: World Bank; Levine: Carlson School of Management, University of Minnesota. This paper's findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. Work for this paper was completed while both authors were visiting the Banco Central de Chile, which provided an excellent research environment. We would like to thank Franklin Allen, Nicola Cetorelli, Robert Cull, Patrick Honohan, Asli Demirguc-Kunt, Raghuram Rajan, and Rene Stulz for helpful comments.
<!-- Start of picture text --> a ss SE<br><!-- End of picture text --> <!-- Start of picture text --> =}<br>oo —-------- - - - - ee eee ee ee +<br>N<br>is]2°<br>on<br>E<br>Cs<br>oo<br>oo<br>3<br>25<br>Ww Oo©<br>e<br>eo<br>ad<br>?<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> <!-- Start of picture text --> assa<br><!-- End of picture text --> <!-- Start of picture text --> i=}<br>oSo -------- - - - ~~ ee ee ee ee ee ee --<br>xc<br>23 °<br>on<br>E<br>ce<br>oo<br>oo<br>as}<br>25<br>Ww o=<br>°<br>N<br>2S<br>°<br>2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text -->
TABLE 3: SUMMARY OF FINDINGS FROM BANGLADESH IMMUNIZATION AND CAUSE OF DEATH STUDIES ON NEONATAL TETANUS

|IMMUNIZATION STUDIES|NEONATAL MORTALITY RATE (PER 1000)<br>____________-_____________________-<br>0 TT<br>2 TT|% REDUCTION IN<br>NEONATAL MORTALITY|
|---|---|---|
|BLACK, ET AL. (1980):<br>1975-77 BIRTHS|59.7<br>39.8|33.3%|
|RAHMAN, ET AL. (1982):<br>IMMUNIZED IN 1974<br>IMMUNIZED IN 1978-79|78.3<br>63.8<br>78.3<br>42.8|18.5%<br>48.8%|
|KOENIG, ET AL. (1991b):<br>1975-79 BIRTHS|78.2<br>49.5|36.7%|
|CAUSE OF DEATH STUDIES|NEONATAL TETANUS MORTALITY RATE<br>(PER 1000)|% OF ALL<br>NEONATAL DEATHS|
|BHATIA (1989):<br>1982-83 MATLAB<br>COMPARISON AREA|13.7|20.4%|
|KOENIG, ET AL. (1991a):<br>l1986-87 MATLAB<br>COMPARISON AREA|18.6*|27.2%*|
|HLADY, ET AL. (1991):<br>N. BANGLADESH DIVISION|18.2|33.9%|

*Adjusted for the effects of the 1974 vaccine trial.
Table 4: Baseline - January 2011 survey.

|||PRO|BIT|||LO|GIT||
|---|---|---|---|---|---|---|---|---|
|**Information**|**0.564*****|**0.526*****|**0.589*****|**0.555*****|**0.927*****|**0.869*****|**0.986*****|**0.940*****|
||(0.103)|(0.108)|(0.112)|(0.118)|(0.170)|(0.180)|(0.187)|(0.199)|
|**Delivery**|**0.551*****|**0.588*****|**0.606*****|**0.617*****|**0.904*****|**0.968*****|**1.009*****|**1.027*****|
||(0.089)|(0.094)|(0.097)|(0.103)|(0.148)|(0.160)|(0.165)|(0.177)|
|**Partisanship**|**0.505*****|**0.503*****|**0.561*****|**0.568*****|**0.833*****|**0.831*****|**0.939*****|**0.961*****|
||(0.088)|(0.092)|(0.099)|(0.104)|(0.148)|(0.156)|(0.170)|(0.180)|
|Constant|YES|YES|YES|YES|YES|YES|YES|YES|
|Personal||YES||YES||YES||YES|
|Dummies|||YES|YES|||YES|YES|
|Observations|1032|1032|1032|1032|1032|1032|1032|1032|
|PCP|0.70|0.72|0.74|0.77|0.70|0.73|0.75|0.77|
|ROC|0.71|0.75|0.77|0.80|0.71|0.75|0.77|0.80|
|AIC|1143.8|1142.5|1221.7|1222.4|1145.0|1144.8|1223.0|1224.3|
|BIC|1163.6|1310.3|1641.0|1789.4|1164.8|1312.6|1642.2|1791.4|

> _⇤⇤⇤_ indicates at 0.1% level, _⇤⇤_ at 1% level and _⇤_ at 5% level.
coefficients from this estimation. The results indicate that Ukrainian refugees are consistently more disparity is particularly pronounced between Ukrainian refugees and Italian students, but significant <!-- Start of picture text --> Average Scores Maths Scores<br>220 220<br>200 200<br>180 180<br>160 160<br>140 140<br>120 120<br>100 100<br>Grade8& Grade 10 Grade 13 Grade8 Grade 10 Grade 13<br><!-- End of picture text --> <!-- Start of picture text --> Italian Scores English Scores W Ukr post<br>220 220 Feb 2022<br>200 200 @ Ukr pre<br>Feb 2022<br>180 180<br>@ Migrant<br>160 160 post Feb<br>2022<br>140 140 @ Migrant<br>pre Feb<br>120 120 2022<br>Gitalians<br>100 100<br>Grade8 Grade 10 Grade 13 Grade8 Grade 13<br><!-- End of picture text -->
fringe benefits, better access to pensions and other social insurances, more job security and higher prestige. Therefore, overall compensation packages received by Bhutan’s public sector workers tend to be far more generous than those offered in the private sector. This also explains why for many in Bhutan the public sector is the most desirable employer.

This study aims to make three contributions. First, it is among the very first empirical studies on Bhutan’s labor market and the very first one documenting patterns of sector selection, wage structure and wage determinants. The issue of public/private wage differentials is particularly relevant for Bhutan as the country’s public sector is not only seen as the sector of choice by many job seekers but also large, comprising almost 50 percent of employment outside agriculture. Because three hydropower projects currently under construction that together have a cost of 150 percent of Bhutan’s GDP will come on stream in the coming years, incentives to expand the size of the public sector even further are likely to rise as are incentives to increase the generosity of the compensation packages offered to public sector workers. Second, the study adds to the literature on public/private wage differentials in development countries. While quite a bit is known on this issue for developed and transition economies, knowledge is still limited or even non-existent for large parts of the developing world. Finally, by using a refined three-step procedure that combines the decomposition method introduced by Oaxaca (1973) with the multinomial logit model for sector self-selection suggested by Bourguignon, Fournier and Gurgand (2007) the study makes a methodological contribution.

This study is closely related to the microeconomic literature on public/private wage differentials. Ehrenberg and Schwartz (1986), Bender (1998) and Gregory and Borland (1999) review early contributions largely with respect to developed countries. The body of literature focusing on transition economies is surveyed in Lausev (2014). The literature for developing countries is more scattered. Pioneering analyses include those by Lindauer and Sabot (1983) for Tanzania, van der Gaag and Vijverberg (1988) for Côte d'Ivoire, Stelcner, van der Gaag and Vijverberg (1989) for Peru and Terrell (1993) for Haiti. More recently, empirical investigations of the public/private wage differential have been conducted for Vietnam (Bales and Rama, 2001), China (Chen, Démurger and Fournier, 2005, and Démurger, Li and Yang, 2012), Turkey (Tansel, 2005), India (Glinskaya and Lokshin, 2007, and Saha, Roy and Kar, 2014), Pakistan (Aslam and Kingdon, 2009), different Latin American economies (Mizala, Romaguera and Gallegos, 2011), again Peru (Coppola and Calvo-Gonzales, 2011) and a handful of other countries. As noted by Lausev (2014), the relevant literature typically finds that

- 3 -
_26 How Fast Has_ **_Cine_** _Inrdwtyr Grown?_ TABLE 4: NET OUTrT AT CURRENT PRICES (100 MILON YUAN) INDEPENDENT ACCOUNTiNG UNITS, EXCLUDING VILAGE ENTERRLSEs

||Total|State|Colective|Xiang|
|---|---|---|---|---|
|1978|1358||||
|1979|1486||||
|1980|1648|1319|322|114|
|1981|1690|1317?|342||
|1982|1774|1373|369||
|1983|1930|1501||415|
|1984|2246|1721|_506_|183|
|198S|2767|2058|679|247|
|1986|2979|2178|763|296|
|1987|3488|2530|894|354|

Source: Rawali wntten files GVIO-tables.

Net output ratio (baed on CVIO data for independent accounting unit - not shown)

||Total|State|Colective|
|---|---|---|---|
|1978<br>1979||||
|1980|0.35|0.351|0.348|
|1981|0.344|0.341|0.337|
|1982|0.336|0.332|0.334|
|1983|0.332|0.332|0.332|
|1984|0.333|0.340|0.314|
|1985|0.329|0.336|0.313|
|1986|0.316|0.322|0.301|
|1987||||
Table 4 Selection-corrected wage regressions

||Pu|blic sec|tor|Pri|vate se|ctor|
|---|---|---|---|---|---|---|
||Estimate||S.E.|Estimate||S.E.|
|_Labor market characteristics_|||||||
|Regular paid employee|-0.150||(0.254)|-0.442|***|(0.042)|
|Casual paid employee|-0.478|*|(0.269)|-0.357|***|(0.085)|
|Contract / piece paid worker|-0.240||(0.262)|-0.196|*|(0.116)|
|_Socio-demographic characteristics_|||||||
|<br>Urban|-0.031||(0.040)|0.271|***|(0.049)|
|Female|0.036||<br>(0.054)|-0.108||<br>(0.101)|
|Bhutanese|-0.398|***|(0.144)|-0.137||(0.130)|
|Potential experience|0.043|***|<br>(0.008)|0.016||<br>(0.018)|
|Potential experience squared|-0.000|***|(0.000)|-0.000||(0.000)|
|_Education_|||||||
|Primary education|0.130|**|(0.058)|-0.023||(0.083)|
|Lower secondary education|0.267|***|(0.058)|0.159|**|(0.072)|
|<br>Middle secondary education|0.556|***|<br>(0.063)|0.422|***|<br>(0.091)|
|<br>Higher secondary education|0.874|***|<br>(0.083)|0.624|***|<br>(0.121)|
|Lower tertiary education|1.295|***|(0.083)|0.920|***|(0.128)|
|<br>Higher tertiary education|1.443|***|<br>(0.100)|1.373|***|<br>(0.176)|
|_Industry_|||||||
|Fishing|-||-|2.371|***|(0.146)|
|<br>Mining and quarrying|1.141|**|(0.545)|1.006|***|<br>(0.122)|
|Manufacturing|0.849|*|(0.514)|0.771|***|(0.068)|
|<br>Electricity, gas and water supply|1.008|*|<br>(0.523)|0.651|***|<br>(0.215)|
|Construction|1.271|**|(0.553)|0.732|***|(0.120)|
|Wholesale and retail trade|0.939|*|<br>(0.520)|0.608|***|<br>(0.072)|
|Hotels and restaurants|-||<br>-|0.552|***|<br>(0.088)|
|Transport and communications|0.946|*|(0.524)|0.648|***|(0.069)|
|<br>Financial intermediation|0.895|*|<br>(0.516)|0.542|***|<br>(0.170)|
|Real estate and business activities|0.901|*|(0.514)|0.947|***|(0.147)|
|Public administration and defense|0.831||(0.514)|1.019|***|(0.361)|
|Education|0.874|*|<br>(0.517)|0.787|***|<br>(0.101)|
|Health and social work|0.996|*|<br>(0.515)|1.200|***|<br>(0.175)|
|Other service activities|1.026|**|<br>(0.521)|0.841|***|<br>(0.154)|
|Private households|-||<br>-|0.470|***|<br>(0.175)|
|_Correction terms and constant_|||||||
|Correction term (inactive)|-0.077||(0.156)|0.101||(0.190)|
|<br>Correction term (public sector)|-0.065||<br>(0.052)|-0.195||<br>(0.246)|
|<br>Correction term (private sector)|0.289|*|<br>(0.153)|-0.208|**|<br>(0.106)|
|<br>Constant|2.890|***|(0.571)|2.849|***|(0.354)|
|R-square||0.536|||0.335||

_Notes_ . (1) The symbols *, ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. (2) Weights have been used for the estimation. (3) The reported standard errors are bootstrap standard errors from 200 replications. (4) Location variables are not depicted. (5) The wage regressions are based on the selection regressions of Table 3. (6) The correction terms are the Bourguignon, Fournier and Gurgand (2007) equivalents for the Mill’s ratio.

In spite of some important differences between correlates of log hourly wages in the public and the private sector, Table 4 shows that altogether the patterns associated with individual wages look rather similar for the two sectors. Moreover, the overall picture that emerge from the table is that

- 20 -
_Table 4: Mean Technical Efficiency by Farm size_

|**Year**|**LandQrtl. 1**|**LandQrtl. 2**|**LandQrtl. 3**|**LandQrtl. 4**|**Full sample**|
|---|---|---|---|---|---|
|**2000**|0.77|0.77|0.76|0.74|0.76|
|**2004**|0.78|0.79|0.76|0.74|0.77|
|**2008**|0.76|0.75|0.76|0.70|0.74|

_Source_ : Authors’ estimates using the 62-village panel survey.

Coming next to the growth in productivity, a TFP levels index is derived using the three measures derived from the SPF estimates – scale effects (SE), a technology effect (T) and technical efficiency (TE). The scale effect is derived from the scale elasticity reported in Table 3. The technology effect is estimated using the coefficients on time, indexing the base year at 1 for all farm sizes. The efficiency effect is estimated as average mean technical efficiency. To capture trends over time as well as by farm size, these measures are averaged over farm size quartiles and then indexed to the first farm size quartile in the base year (2000). The estimated TFP index is shown in Figure 4.

The trend of TFP over farm size has shifted from being distinctly negative in 2000 and 2004, to a relatively flatter trend, though the negative trend persists. The implied growth rates of TFP by farm size, and the components of TFP are given in Table 5.

The first finding that emerges from Table 5 is that the primary driver of TFP has been technical change. Annual average TFP growth between 2000 and 2008 is estimated to be high at 2.86%, which is somewhat greater but <!-- Start of picture text --> Figure 4: TFP Index by Land quartile and year<br>(Base: LQ1, 2000=100)<br><!-- End of picture text --> <!-- Start of picture text --> 120.0 109.5 107.0 109.2<br>100.0 102.0<br>100.0 90.0<br>83.9<br>74.1<br>80.0<br>60.0<br>40.0<br>20.0<br>0.0<br>Land Quartile 1 Land Quartile 2 Land Quartile 3 Land Quartile 4<br>2000 2004 2008<br>Source : Authors’ estimates using the 62-village panel survey.<br><!-- End of picture text --> sectional survey of rice producers from one southern district of Bangladesh. They find a higher level of efficiency (0.91) for data from 2000. Given the dominance of rice in Bangladesh, a higher level of efficiency in rice production compared to the whole farm estimate of this study is not surprising.

22
17

|Phnom Penh|0.9|10|13.5|32|
|---|---|---|---|---|
|Port Vita|0|1|_5_|14|
|Rarotonga|s.d.|s.d.|3.5|10|
|Seoul|1.5|16|2.3|6|
|Shangai|11.1|44|6.1|17|
|Singapore|1.1|14|2|_5_|
|Suva|6|37|8.9|26|
|Taipei|1.7|20|1.1||
|Tashkent|6.3|39|17.9|40|
|Thimphu|4|30|25.5|44|
|Tianjin|0.1|2|49.9|49|
|Ulaanbaatar|2|22|579.2|50|
|Ulsan|0.5|7|0.8|1|
|Vientiane|3.3|28|16.1|37|
|Yangon|s.d.|s.d.|12|30|

To test the assumption that the 3 rankings are not correlated, we run a Spearman test of ranking correlation. The correlation's measured are summarised in table 5.

Table 5

|Stochastic=<br>Frontier CLS|1.000|0.861**|-0.205|0.090|
|---|---|---|---|---|
|Stochastic<br>Frontier ML||1.000|-0.172|0.371 *|
|Accounts<br>Receivable|||1.000|0.332*|
|Employees per|||||
|1000 connections||||1.000|

* means that correlation is significantly different from 0 at a 20% level of significance.

** means that correlation is significantly different from 0 at a 10% level of significance.

The correlation between the rankings from the account receivable and number of workers per 1000 connections) is positive and significantly different from 0 at the 10% level of significance. However, the correlation from the ranking resulting from accounts receivable is negative with the two rankings derived from the frontiers suggesting that the two approaches are not consistent (ie, frontiers vs. partial indicators). This means that the regulatory assessments would vary with the approach adopted!"<sup>0</sup> As for the number of employees per 1000 connections, the correlation with the 10 It could be argued that all the firms with accounts receivable for less than 3 months cannot be considered inefficient. However, even if all firms with receivable under 3 months were assigned a ranking of 1, the correlation between the rankings would be similar.
from the World Bank’s Country Policy and Institutional Assessments (“CPIA”). _First_ , “CPIA Legal Framework” measures the legal framework for secure property and contract rights, including predictability and impartiality of laws and regulations. Since this is mostly a _de jure_ measure of the legal system and it mainly concerns resolution of conflicts between private citizens, it is a natural measure of contracting institutions. _Second_ , “CPIA Legal System” measures the quality of the legal and judicial system as indicated by independence, accessibility, legitimacy, efficiency, transparency, and integrity of the courts and other relevant dispute resolution mechanisms. This is mostly a _de facto_ measure of the legal system, and, again, captures primarily contracting institutions.

In addition to the two general measures of the legal system, we also rely on a specific measure of the legal system from the Doing Business (DB) project: “Contracting Procedures” (Djankov et al., 2003). It measures the number of procedures required to resolve a sale-of-goods dispute in local courts. It includes steps to file the case, to go through the trial and judgment, and to enforce the judgment. The data are collected through studying the codes of civil procedure and other court regulations as well as surveying local litigation lawyers. This measure should be complementary to the two measures on the legal system from the CPIA. The CPIA measures are much broader, but because they are highly multi-dimensional and more subjective they may contain more measurement error. The Doing Business indicator is defined more precisely and objectively, so it may contain less pure measurement error but at the expense of measuring only a few narrow aspects of contracting institutions. Obtaining consistent findings from these very different measures would strengthen confidence in our conclusions regarding the importance of contracting institutions for access to external finance. Formal financial systems function better and at lower costs in the presence of a good information and/or institutional environment, where investors have more information on potential borrowers and can better monitor their use of investment funds (Leuz and Verrecchia, 2000; Leuz and Wysocki, 2008; Liu et al., 2015). We measure the information environment by an indicator from the World Economic Forum’s Executive Opinion Survey, “WEF Auditing Quality,” which captures financial auditing and reporting standards regarding company financial performance in a country. A higher value implies stronger standards. Because auditing standards and quality are maintained mainly to facilitate transactions between private citizens, it is an important ingredient of contracting institutions.

The final measure of contracting institutions specifically deals with the rights of suppliers and demanders of investment funds: from Doing Business, “Creditor Rights” measures the degree to 10