rafmacalaba/datause-encoder-data huggingface dataset:
We follow the international finance literature (e.g., Chinn and Prasad, 2003; Gruber and Kamin, 2007; Chinn, Eichengreen, and Ito, 2014) in using a 5-year period to smooth out the effects of business cycles and transitory (and global) factors, as well as to allow for the fact that the effect of the changes in structural drivers on FDI and RCA tend to materialize over time (adjustment lag).<sup>20</sup> We merge the indicators data set with FDI and trade data sets. The raw FDI data contained 702 sectors (sector/activity pairs in the _fDi Market_ s’ nomenclature) for 206 countries with large number of zeros (no FDI activity over the entire five-year period) for individual sectors. We first excluded countries for which the country indicators are not available over the period of analysis (2010-19). We then excluded those sectors which have fewer than 5 non-zero observations for the remaining countries, arriving to the final number of 245 sectors. Using the information on FDI flows, we construct a variable which takes value 1 if sector _i_ of country _j_ was the recipient of any FDI flow over the five-year period 2015-19, and 0 otherwise. ii,jj YY Analogously, we create a variable based on the information on country/sector pair FDI "success" over the 2010-14 period. Figure 1 shows the histogram of the empirical distribution of the number ii,jj ZZ countries which received FDI flows in any of the 245 sectors over the 2015-19 period. We see that the distribution is largely skewed toward sectors in which FDI flows went to only a subset of countries (the left-hand side of Figure 1). In the median sector (by the number of recipient countries) only 20 countries are the recipients of FDI inflows.

_Figure 1. Country recipient distribution of sector level FDI_ <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>[5,10] [11,20] [21,30] [31,40] [41,50] [51,60] [61,70] [71,80] [81,90] [91,116]<br>Number of countries<br><!-- End of picture text --> Note: the histogram reports the number of countries for each of the 245 sector/activity pairs which received FDI inflows over the 2015-19 period.

The same steps are used with the trade data. The initial data set included information on RCA for 1,245 4-digit sectors. We excluded sectors which have fewer than 5 non-zero observations for the sample countries, leading to a final number of 1,220 4-digit sectors. We transform the data set and construct a dummy variable which takes value 1 if the country _j_ has RCA in sector _i_ above 1 and 0 otherwise. The RCA is calculated based on the sum of export and import flows over the 2015-19 period. Analogously, ii,jj we create a variable YY based on the information on country/sector pair RCA "success" over the 201014 period. Like Figure 1, Figure 2 shows the histogram of the empirical distribution of number of ii,jj ZZ 20 There is the possibility of reverse flows (i.e., an outflow investment is made and then disinvested) during the period, but if any at all, these are likely to be negligible relative to the overall sum of investment or trade flows.

12
21

**Figure 11:  State-owned banks are still holding on especially in India and MENA**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>India MENA EAP ECA LAC Developed<br>1985 1995 2003<br>private commercial banks<br>Percentage of bank assets at<br><!-- End of picture text --> Source: World Development Report 2005

# _Energy_

41. In energy, political issues surrounding private and in particular foreign investment are paramount in sectors such as oil and gas and thus many such companies remain either fully or majority state-owned. Some inroads are being made: in Mexico, for example, the need to inject funds into heavily indebted Pemex, the state-owned oil company accounting for 36 percent of government revenues, has recently led policy-makers to consider a “new model” for the company, including the removal of an explicit prohibition on private investment and possible share offering of shares on the domestic stock market.

42. In short, the above analysis suggests that despite a long track record of privatization over more than a decade, government ownership still appears to be prevalent in many regions and sectors.

43. The costs of not reforming state enterprises are high: the productivity of state enterprises is typically two to three times lower than private firms, and in some cases significantly lower; they continue to remain a fiscal burden; lack of investment capital has led to poor service delivery in critical sectors that impact the economy as a whole. A high percentage of subSaharan African and South Asian firms identify poor infrastructure services in telecommunications and electricity as major or very severe obstacles to private sector development and growth (World Bank Investment Climate Database). Thus the longer governments wait to address these issues, the greater the economic and financial burden and the greater the barriers to achieving economic growth.

# **IV.   EMERGING ISSUES**

44. A vast body of empirical work on the impact of privatization that has been summarized in detail elsewhere shows that in competitive sectors privatization has improved firm performance (Megginson and Netter 2003 and Kikeri and Nellis 2004 provide recent reviews). Available studies conclude that privatization in these sectors improves performance and
Also, an increase in innovation investment has a larger effect on final output than a firm receiving foreign investment or becoming an exporter. Therefore, the overall and external innovation effects on final output become much larger when considering the minimum requirement of innovation investment (threshold); however, internal innovation still has an insignificant effect on final output, and there is no synergy in investing in both sources of innovation. The results of table 8 further support the results of table 5.

When predicted values of innovation are used, the coefficients of labor become insignificant, and the coefficients of capital remain insignificant. Meanwhile, the coefficients of intermediary inputs are still positive and statistically significant. The coefficients of exports now become negative while FDI status still remains positive and significant.

Table 9 provides the estimation results of using TFP as a dependent variable, instead of output (revenue), which is a direct test of the innovation effect on productivity.

_<u>Table 9. Using TFP as a Dependent Variable, GMM</u>_

|Dep. Var<br>Ln TFP_ijt_|(1)<br>Innov<br>|(2)<br>Internalvs. external|(3)<br>Synergies|
|---|---|---|---|
|ln_all|0.063<sup>***</sup><br>(0.001)|||
|ln_internal||0.024<br>(0.110)|0.034<br>(0.307)|
|ln_external||0.045<sup>*</sup><br>(0.015)|0.039<sup>*</sup><br>(0.047)|
|ln_int×ln_ext|||-0.001<br>(0.853)|
|Eexport_stat|0.635<sup>***</sup><br>(0.000)|0.611<sup>***</sup><br>(0.000)|0.528<sup>***</sup><br>(0.000)|
|FDI_stat|0.807<sup>***</sup><br>(0.000)|0.782<sup>***</sup><br>(0.000)|0.704<sup>***</sup><br>(0.000)|
|# of obs.|15,735|15,735|15,735|
|# of group|4,255|4,255|4,255|
|AR2 (p-value)|0.607|0.534|0.509|
|Sargan (p-value)|0.000|0.000|0.000|
|Hansen(p-value)|0.001|0.002|0.000|

_Note:_ Year and industry dummies are included but not reported. Standard errors are clustered and robust (White’s correction for heteroskedasticity); _p_ -values in parentheses, Significance level: * = 5 percent, ** = 1 percent, *** = 0.1 percent 51
This is a restrictive definition of democracy, and a broader definition of non-democracy. In this latter group quite a number of countries exist that exhibit democratic tendencies, but are not considered a pure democracy where voter costs of removing the incumbent are lowest.<sup>15</sup> Figures 1-4 indicate negative relationships between fuel subsidies and government expenditures in democracies, and positive relationships (albeit much weaker) for nondemocracies. We see from the graphs that the relationships are however not particularly strong. The positive relationship is maintained (in weaker form) for gasoline in nondemocracies when Kuwait (a clear outlier in both figures 2 and 4) is removed in Figure 4; while the relationship for diesel prices, in Figure 2, then becomes a horizontal line. It is thus difficult to find any such systematic relationships for non-democracies; this is overall a very heterogeneous group.

> 15 This distinction between democratic and non-democratic countries is hardly perfect. Note in particular that, in Figures 1 and 3, Venezuela is formally a democracy as the objective institutional structure is such that both measures of electoral competition get its highest score. It is possible that the institutional rules are changed such that the chances of the incumbent increase. For these factors other variables are present in DPI.

16
The effectiveness of this governance arrangement is periodically reviewed in prescheduled evaluations by independent sector and regulatory experts (preferably including one or more individuals from outside the country) in a public document.

Source: Brown et al (2006)

# **2.2 Regulatory Independence**

An additional important aspect when analyzing regulation and overall sectoral performance, mostly in developing countries, is to distinguish between the formal and informal aspects of the regulatory process; that is the distinction between what is written in the law and regulations, and the way in which things are actually done. Bureaucratic delegation of regulatory competencies from political decision-makers (the “principal") to regulatory agencies (the “agent"), though backed by law and highly formalized, invariably relies upon an incomplete contract, since it is impossible to spell out in explicit detail all the precise obligations of the agent throughout the life of the contract, and the cost of monitoring the whole process would be prohibitive (Gilardi and Maggetti 2010).

Developing countries show a substantial gap between the conditions stated in the legislation and what actually happens in the real world (Rodríguez Pardina 2008). A number of studies have attempted to measure this divergence empirically.

Andres et al (2007) evaluated the performance of regulatory agencies by constructing a Regulatory Governance Index for electricity regulators in Latin America. The principles of autonomy, transparency and accountability were divided into formal (what is stated in the norms) and informal (what really happens) variables. This makes it possible to measure the divergence between them, by looking at the strength of the correlation between the formal and informal indicators for each of these three dimensions. Rodriguez Pardina (2008) undertook such an exercise and the results (Table 3) show that both the direct correlation between the indexes and the rank correlations are relatively low for the three principles included in the study.

**Table 3: Formal and Informal Dimensions Gap**

||Autonomy|Transparency|Accountability|
|---|---|---|---|
|Index Correlation|0.459|0.364|0.365|
|Rank Correlation|0.409|0.330|0.398|

Source: Rodriguez Pardina (2008) A similar conclusion is reached by Correa et al (2006) in their study of Brazilian regulatory authorities, although the correlation is relatively higher (0.7). They compare an overall index, which comprises a complete set of 106 questions regarding the agency ́s formal characteristics and procedures, with the “de facto” index, in which only 28 questions, designed to capture the procedures used by each regulatory agency, are included. This 9
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 10: Projections of teacher dependency rates* under two alternative mortality assumptions show an increase, particularly starting in 2050**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2003 2008 2013 2018 2023 2028 2033 2038 2043 2048 2053 2058 2063 2068 2073<br>Year<br>v ariant 1 v ariant 2<br>Dependency rate<br><!-- End of picture text -->

**Notes:** * Ratio of the total number of beneficiaries in all categories to the total number of active employees.

**Source** : WB calculations based on PROST

The current dependency rate for teachers is 8% and much lower than the 23% rate for civil servants. This stems from the fact that the teachers’ sub-scheme is a much younger scheme. In the future, three main periods can be clearly distinguished:

- 2004-2014 – very low growth rates in the number of employed under the Pay Reform Policy proposal;

- 2015-2050 – period of faster coverage expansion to reach 1% of population in each of the two sub-schemes;

- 2050-2075 – period of slowing down coverage growth to match general population growth rates.

During the first period the dependency rates in both sub-schemes increase as the growth of the number of active members is too slow (especially for civil servants) to offset the impact of ageing.

During the second period of accelerated growth the dependency rate decreases for civil servants and stabilizes for teachers. The difference between the two sub-schemes can be explained by the fact that the number of teachers is higher than the number of active civil servants and therefore, teachers require a lower rate of growth to reach the target 1% of

- 38 -
Government equity injections + δ government loans + Δ SOE loans + Δ DTL

1.77 16.96 0 0 303.02

_Source_ : Authors, based on data from the World Bank Infrastructure SOE Database.

_Note_ : SOE scores were calculated using the Altman Z” parameters as follows: Z” = 3.25 + 6.56 * (current assets – current liabilities)/total assets + 3.26 * retained earnings/total assets + 6.72 * EBIT/assets + 1.05 * book value of equity/total liabilities What is unique about the SOE data is that the estimated Z” scores have very fat tails: Many SOEs have extremely high Z” scores, and many have with extremely low scores. Table 4 compares the distribution of the Z” score for private firms from Altman (2018, table 6) with the distribution of the Z” score for SOEs in our sample. It shows that private firms have more of a bell-curve shape and our data follow a _U_ -shape, with very thick tails on both ends.

**Table 4 Distribution of infrastructure SOEs along the Altman Z” score range**

|_Rating_|_Average Z"_<br>_across_<br>_samples_|_Altman's_<br>_estimates_<br>_(percent of 4,147_<br>_privatefirms)_|_Infrastructure_<br>_Number of_<br>_SOEs_|_SOE sample_<br>_Percent of_<br>_121 SOEs_|
|---|---|---|---|---|
|AAA/AA+|8.15|1.2|30|24.8|
|AA/AA-|7.78|2.2|5|4.1|
|A+|7.61|2.3|3|2.5|
|A|7.04|4.8|3|2.5|
|A-|6.54|4.9|4|3.3|
|BBB+|6.17|5.8|4|3.3|
|BBB|6.00|9.1|3|2.5|
|BBB-|5.90|7.1|2|1.7|
|BB+|5.72|5.5|4|3.3|
|BB|5.55|7.6|4|3.3|
|BB-|5.16|11.4|4|3.3|
|B+|4.79|10.6|3|2.5|
|B|4.16|11.2|9|7.4|
|B-|3.72|6.2|3|2.5|
|CCC+|3.01|1.7|9|7.4|
|CCC|2.42|0.7|7|5.8|
|CCC-|1.70|0.5|3|2.5|
|CC/D|0.30|7.2|21|17.3|

_Source_ : Authors’ calculations based on Altman (2018, table 6).

_Note_ : SOE scores were calculated by the authors using the Altman Z” parameters as follows: Z”= 3.25 + 6.56 * (current assets – current liabilities)/total assets + 3.26 * retained earnings/total assets + 6.72 * EBIT/assets + 1.05 * book value of equity/total liabilities, using a sample of 1,101 firm-year observations from the World Bank Infrastructure SOEs Database.

In our data, the median levels for the current assets minus liabilities and retained earnings ratios (both over assets) are near zero (0.031 and 0.016, respectively), and almost 40 percent of the 22
we match the data set on security issuances from SDC Platinum with 2003-2011 firm-level balance sheet information from the Orbis (Bureau van Dijk) database. The latter covers publicly listed companies, providing a rather homogeneous sample of firms. By omitting unlisted firms in the analysis using the matched data, the sample excludes firms that are (1) relatively small and sometimes informal, (2) likely to have different accounting standards, and (3) less likely to issue in capital markets. Moreover, because of a lack of coverage of capital raising activity in domestic bond markets, Canada and the Republic of Korea are not included in the sample. Firms from countries with less than 10 issuing firms between 2003 and 2011 are excluded from the final sample and so are the firms from offshore financial centers. The final matched data set covers 45,527 firms from 51 countries.<sup>6</sup> We classify firms as issuers or non-issuers based on whether they issued equity or bonds at any point during our sample period. Because firm-level balance sheet information is only available for the period from 2003 to 2011, we classify a firm as an issuer if it had at least one equity or bond issuance during that period. We further classify whether firms are equity or bond issuers depending on whether firms issued any equity or bonds, respectively. If a firm raised capital through equity and bond issuances between 2003 and 2011, we classify the firm as both an equity issuer and a bond issuer. We also classify firms as domestic or foreign equity issuers and as domestic or foreign bond issuers, depending on whether they raised capital in domestic or foreign markets. Foreign issuers are the firms that had at least one capital raising issuance in foreign markets between 2003 and 2011. Domestic issuers comprise firms that issued only in domestic markets.<sup>7</sup> Hence, firms that raised capital in both domestic and foreign markets over the sample period are classified as foreign issuers. The sample of

> 6 Overall, our matched data set covers at least 85% of the listed firms in each country. Appendix Table 1 reports the list of countries and the number of non-issuing and issuing firms from each country covered in the final matched data set. 7 For robustness, in unreported results, we considered overlapping groups of domestic and foreign issuers. That is, foreign issuers are the firms that had at least one capital raising issuance in foreign markets between 2003 and 2011, whereas domestic issuers comprise firms that had at least one capital raising issuance in domestic markets over the same period. The results are qualitatively similar to the ones reported in the paper.

8
Tariff Index Theory J.E. Anderson 3 restrictiveness index in terms of changes in the mean and 'generalized variancecovariance' functions of the tariff schedule. Efficiency is indeed decreasing in both of these 'moments'. The coefficient of variation index used by the World Bank varies monotonically with the trade restricdveness index when: (i) the only form of distortion is tariffs, (ii) the two indices are used to compare a given tariff schedule with the uniform mean-equivalent tariff schedule, (iii) the import-weights in the index are "marginal" import weights, and (iv) the variance concept used in the coefficient oi variation is a 'generalized variance'.

Section III shows that when the trade preferences for imported final goods are CES, the ordinary variance concept may be used in place of the generalized variance, and marginal trade weights may be replaced with the ordinary trade weights. Thus mean preserving eliminations of trade weighted variance in tariffs are efficiency-increasing. The rate of change of the trade restrictiveness index in the Cobb-Douglas case is generally a function of the trade weighted first and second moments of the tariff schedule, although it is not the coefficient of variation function. This form of the trade restrctiveness index highlights an intuitiv sense in which the trade weighted mean and variance of tariffs were both 'partially' legitimate. Finally, a Cobb-Douglas example is offered in which variance reductions are efficiency improving subject to the constraint that the trade-weighted mean tariff be the same. That is, comparisons can be made in which variance need not be eliminated. However, even in the Cobb-Douglas model, when the mean and variance both change there are always welfare-improving tariff reforms which coincide with a rise in the import weighted coefficient of variation.

Sectior IV concludes with some suggestions for future practice. Future work (underway at the World Bank as part of the project on trade policy evaluation) will reveal whether the refinement of the trade restrictiveness index yields different results in practice. However, in related work Anderson and Bannister (1992) show that the trade
WPS3985

# Openness and Technological Innovations in Developing Countries

# ∗ Evidence from Firm-Level Surveys

Rita Almeida The World Bank and Ana Margarida Fernandes The World Bank

### Abstract

This paper analyzes the role of international technological diffusion for firm-level technological innovations in several developing countries. Our findings show that, after controlling for firm, industry, and country characteristics, exporting and importing activities are important channels for the diffusion of technology. We also find evidence that majority foreign-owned firms are significantly less likely to engage in technological innovations than minority foreign-owned firms or domestic-owned firms. We interpret this finding as evidence that the technology transferred from multinational parents to majority-owned subsidiaries is more mature than that transferred to minority-owned subsidiaries. This finding supports the idea that equity joint ventures maximize technology transfers to local firms.

Keywords: Innovation, Technology Adoption, Exports, Imports, Foreign Ownership, Firm Level Data.

JEL Classification codes: F1, F2, O3.

## World Bank Policy Research Working Paper 3985, August 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at_ _<u>http://econ.worldbank.org.</u>_

> ∗ We thank Robin Burgess, Alberto Salvo, Luis Serven and Jim Tybout and participants at the 2006 International Industrial Organization Conference for their suggestions. Please address correspondence to Ana M. Fernandes, The World Bank, 1818 H Street, NW, Washington DC 20433. Phone: 202-473-3983. Fax: 202-522-3518. E-mail: afernandes@worldbank.org.