rafmacalaba/data-use-mentions-extended huggingface dataset:
I must avoid introducing endogenous variation into the instruments. This might occur if municipalities are on different time trends according to their values of the pre-period variables used to compute the instruments. Municipalities might also be on different time trends according to initial public pre-primary enrollment levels. I address these concerns by including the interaction of a linear time trend, _t_ with a vector of the 1997 values of per capita revenue, per capita transfer revenue, the fraction of public primary school students in state or federal schools, the fraction of 0-6 year-olds enrolled in public pre-primary, and public pre-primary enrollment squared. This allows for heterogeneous secular trends in municipalities with different pre-reform characteristics.

In the results section, I demonstrate that these instruments satisfy the inclusion restriction: they are correlated with observed per capita revenue and with its interactions with per capita income and inequality. The exclusion restriction should hold since the instruments depend only on pre-reform municipality characteristics (captured by fixed effects) and federal government rules, which are exogenous to any particular municipality’s investment during the sample period.

# **5.3 Measuring income, inequality, and pre-primary investment**

I measure income two ways, to ensure robustness: median and average per capita municipal income. I compute median per capita household income using 2000 Census data. Average income data are available annually. I measure inequality with the municipal Gini coefficient, computed from 2000 Census data. Results instead using mean over median income, and the level of income polarization,<sup>32</sup> are similar and available upon request. Table 4 presents a correlation matrix of these variables. The correlation between the Gini coefficient and per capita income (whether median or average), is always below 0.04 in absolute value. Median and average income have a correlation of 0.72.

I have panel data on several aspects of public pre-primary education quality:<sup>33</sup> pre-primary students per teacher, the fraction of pre-primary teachers with at least some post-secondary education, and
supplemented the information with a star rating, based on the mystery shopper ratings<sup>5</sup> ; (3) 183 firms were allocated to a _ratings plus negative comments_ group, which provided the star ratings and any negative comments from the mystery shopper; and (4) 200 firms were allocated to a _ratings plus all comments_ group, which received star ratings and both positive and negative comments provided by the mystery shopper. Appendix B provides examples of each screen. Columns 4 onwards of Table 2 show the different treatment groups are well-balanced on most baseline observables.

> 4 Internet connectivity issues meant that only 685/757 firms (90.5%) were able to successfully connect to the marketplace and view and rate providers. We use intention-to-treat analysis, which considers the impact of being randomly assigned to see the marketplace.

> 5 We had intended to show them the customer ratings from firms that had used these firms in the GEM project, but instead the ratings from the mystery shoppers were entered. This ensures the same ratings were used as for the ratings plus comments groups.

10
WPS3511

# **Bank Privatization and Performance**

# **Empirical Evidence from Nigeria**

## **Thorsten Beck**<sup>**1**</sup> **, Robert Cull**<sup>**1**</sup> ***, and Afeikhena Jerome**<sup>**2**</sup>

1 The World Bank 2 University of Ibadan

**Abstract:** We assess the effect of privatization on performance in a panel of Nigerian banks for the period 1990-2001. We find evidence of performance improvement in nine banks that were privatized, which is remarkable given the inhospitable environment for true financial intermediation.  Our results also suggest negative effects of the continuing minority government ownership on the performance of many Nigerian banks.  Finally, our results complement aggregate indications of decreasing financial intermediation over the 1990s; banks that focused on investment in government bonds and non-lending activities enjoyed a relatively higher performance.

Keywords: Bank privatization; Nigeria; Bank performance JEL Codes: G21, G28, G34, O55

### World Bank Policy Research Working Paper 3511, February 2005

_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 http://econ.worldbank.org._

*Corresponding author: World Bank, Mail Stop MC 3-300, 1818 H Street NW, Washington, DC 20433, USA, Ph: 202-473-6365, Fax: 202-522-1155, E-mail: Rcull@worldbank.org.

We would like to
# **2.4 Post Basic Schooling and Working Age**

## **2.4.1 Tertiary Education**

**The prevalence of tertiary education varies dramatically across countries** . As shown in Figure V, tertiary educated workers are virtually absent in poor countries, while they represent half or more of the working age population in the richest countries. The rise of upper tail knowledge is thought to have played an important role in the historical development process of currently rich countries, by facilitating innovation and the adoption of new technologies (Mokyr, 2002; Squicciarini and Voigtl ̈ander, 2015). Today, in many countries inventors, entrepreneurs and executives in leading firms are educated disproportionately in a small set of universities (Bell et al., 2018; Levine and Rubinstein, 2016; Martellini et al., 2022).

Figure V: Tertiary Education and Development <!-- Start of picture text --> 80<br>RUS<br>60 USA<br>CAN<br>ARM ISRJPN ARESGP<br>UKR KORTWNDNK IRL<br>40 MDA LTUCYPEST ESPFRAFINGBRBELAUS SWE HKGNLD NOR CHE LUX<br>200 BDICAFCOD MWI NERLBRMOZ SLEHTITGOUGAMLIRWAGMBTZAYEMNPLZWESENBENZMBTJKCMRLSOKHMKENBGDSDNMRTCIVPAKCOGMMRKGZHNDNICVENGHAINDSYRVNMBLZLAOPHLBOLSLVMARGTMJAMSWZIDNJORIRQMNGTUNNAM GUY ECUALBPERPRYLKADZAEGYBRBCHNZAFFJIIRNCOLBRADOMBWAGABSRBTHACRIBGRMDVMEXMUSURYARGROUHRVCHL MYS KAZLVAPANHUNTURGRCPOLSVKPRTTTOSVNCZE MLT ITANZL ISLDEUBHRAUTSAUKWT MACQAT<br>7 8 9 10 11 12<br>Log GDP per worker in PPP (2015)<br>Share with Tertiary Education (%, 2015)<br><!-- End of picture text --> _Notes_ : The figure plots the share of the 25-64 population with some tertiary education against log GDP per worker (in PPP terms) in 2010. Data on educational attainment is taken from Barro and Lee (2013), while GDP per worker is taken from version 10.0 of the Penn World Tables (Feenstra et al., 2015).

**Recent work shows that the quality of tertiary educated workers also varies substantially across countries.** Martellini et al. (2022) use data on the earnings of migrants
specific LSMS-ISA Uganda National Panel Survey (UNPS) survey documentation, all individuals older than 15 years that are biologically related to the head of household who leave a sampled household are found and interviewed, as long as they do not move out of the parish where they are not tracked and their characteristics of interest are not recorded. However, referring again to were initially located. The latter rule implies that people who left their parish after time ttthe specific example, it is useful to note that regardless of the tracking rules, UNPS survey<sup>∗</sup> enumerators often track all those who remained within the Uganda border, even if they moved out of the original EA or parish. is re-contacted for the survey, the enumerator identifies all of their household members and collects the survey data. This ensures tt<sup>∗</sup> that the study captures changes in the household composition (due to births, deaths, marriages, Once an individual selected in the original sample SS . tt<sup>∗</sup> etc.) of all the individuals tracked in survey SS that were tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> Thus, starting from the original sample SS , the longitudinal sample SS. of mm tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt<sup>∗∗</sup> ←tt<sup>∗</sup> tt<sup>∗</sup> households with nn individuals is formed. SS contains all persons of SS Current time tracked, along with all members of their households at time tt<sup>∗∗</sup> are tracked and surveyed. The rules of tracking and data collection are the same as those tt<sup
to 1992 and source l1l, Code<br>2:_employment_office_statistics_and include persons in employment who are seeking a change of job or extra work<br>and are therefore also registered at employment offices. This clearly underestimates the number of unemployed<br>people in Sudan.|
|Swaziland|Paid employment in non-agricultural<br>activities is drawn from the ILO's Yearbook of Labor Statistics 1995 and refers<br>to 1992.|
|Tanzania|Central Govemment and Local Govemment employment are taken from document provided by Vedasto<br>Rwechungura (Tanzania Resident Mission) and relate to 1996.<br>Education employment is also taken from<br>information received from Vedasto Rwechungura (Tanzania Resident Mission). It indicates that Primary School<br>teachers are estimated at 97,001 as of July 1, 1996 and secondary education teachers were estimated at 19,020.<br>Universities in Tanzania are autonomous, therefore employees are not on the Govemment payroll. The total<br>education employment is therefore estimated at 116,021. Health employment has also been provided by Vedasto<br>Rwechungura (Tanzania Resident Mission). As of July 1, 1996, paramedical staff (Nurses, medical attendants,<br>technicians, etc..) on the payroll are 23,483. Rare professionals such as doctors are approximately 2,000.<br>Accordingly, health employmen**t**otals 25,483 persons. Military personnel is expected to be reduced to 25,000 in<br>1996/97. Data do not include enrollment in paramilitary units such as the Police Field Force (1,400), the Police Air<br>Wing and Marine Unit, and the Citizens' Militia, the reserve corps.<br>GDP at market Prices is taken from World Tables 1995 and relates to 1993. Central Government wage bill is taken<br>from Tanzania: Role of Government: Public Expenditure Review of June 17,
.

We follow three alternative approaches to measure conflict intensity and map it into a persistent productivity loss. First, we proxy conflict intensity by the per capita number of battle related deaths. To mitigate the effect of very intense conflicts on our loss measure, we prefer, however, to categorize conflicts in two groups: intense versus non intense conflicts. We use a dummy that takes a value of 1 if the number of per capita battle related deaths exceeded the level suffered in Nigeria during the Biafra civil war.<sup>31</sup> In addition, we use data on the stock of refugees provided by the UNHCR. We believe that flows of refugees are a good proxy for civilian suffering during conflict. We consider therefore a dummy that takes a value of 1 if there is a net outflow of refugees in a given year. However, we are aware that a big challenge of this proxy is that some conflicts by nature cause more internal movements or no movements at all because people are just not able to flee the affected areas.

The loss for a worker of age _h_ at year _t_ in country _i_ is calculated as: and the total loss in country i at year t is given by where _Shti_ is the share of workers of age _h_ in total labor force, _It−h_ + _j,i_ is a measure of

> 31We discuss the effect of a change in the threshold below.

34
to this strand of literature is twofold. First, we depart from the literature on catalytic finance that has mainly discussed the catalytic role of the IMF by focusing specifically on the role of MDBs, which have been greatly overlooked to date and can instead be an important player in light of the 2030 Development Agenda. Second, while the literature has mainly focused on case studies or country-aggregate data, our analysis is based on loan-level data from the international market of syndicated loans and covers a large sample of more than 100 countries over 25

> 5We focus on the potential substitution between corporate bonds and syndicated loans, as they constitute two similar sources of financing from a firm’s perspective (Altunbas ̧ et al., 2010), while the role of the equity market in developing countries is still relatively limited (World Bank, 2015; Cortina et al., 2018). We also look at potential displacement effects on aid and other development finance flows, finding no evidence that MDB mobilization effects crowd-out these flows. 6The last set of results is discussed in Appendix A.

> 7Theoretically, Corsetti et al. (2006) and Morris and Shin (2006) show that IMF catalytic financing can reduce the incidence of panic-driven liquidity crises. Empirically, Eichengreen and Mody (2001), Mody and Saravia (2006) and Eichengreen et al. (2006) use transaction data of individual bond issuances and find the IMF programs can have a catalytic effect, conditional on country fundamentals being only moderately bad. In a review of economics and politics of the IMF, Bird (2007) warns against any generalization from the empirical evidence, as results do not appear robust and consistent across methodologies, samples and economic conditions. More recently, Erce and Riera-Crichton (2015) use aggregate data on gross capital flows to show that while the IMF does not appear able to catalyze foreign capital, there is substantial evidence that it does affect the behavior of resident investors, who are less likely to place their savings abroad and more likely to repatriate their
average around three quarters of their production. This contrasts sharply with State enterprises, where only about 12% of production is exported (according to information from the Ministry Of Finance). Thus, while the capital-intensive State sector is mainly inward-looking, export markets are clearly the main engine of private sector growth.

Table 6: Private manufacturers are labor-intensive and export-oriented

|Nu|mber of firms|Share of total<br>firns (%)|Exports/output (%)<br>(unweighted average)|
|---|---|---|---|
|Garments and Textiles|159|34.8|80.5|
|Food and Beverages|71|15.5|63.2|
|Wood Products|65|14.2|75.1|
|Other Non-metallic Products|39|8.5|73.2|
|Leather Products|34|7.4|85.0|
|Rubber and Plastic Products|22|4.8|75.0|
|Basic Metals|9|1.9|n.a.|
|Chemical Products|9|1.9|20.0|
|Others|49|10.7|74.4|
|TOTAL|457|100|75.3|
|_Source:_Data from MPDF (1999)||||

Under what conditions do domestic private companies compete in international markets and whom do they employ? A visit to factories in Haiphong shows that many Vietnamese private companies producing garments or footwear for export operate as subcontractors for foreign companies located abroad, which later re-export their products to Europe and to the U.S. This means that many foreign investors associate themselves with domestic firms without formally registering as joint-ventures. Given the present dual pricing system in Vietnam -which requires foreign investors to pay higher prices for land, utilities, labor, and transportation- this substantially reduces production costs. In addition, it avoids
<!-- Start of picture text --> 0.6 — Sentiment Index<br>/\<br>|<br>o4 \<br>/j |<br>03 / \\<br>/ \<br>Of<br>0.1<br>-4 -2 0 2 4 6<br><!-- End of picture text -->

Notes: This figure present the distribution of the maim sentiment index measure. This measure captures the average sentiment in every country-year across all sources and languages. It is constructed using the polyglot project lexicons of positive and negative words.
In most of the nine countries, multi-segment firms are less profitable than their single-segment counterparts. As in Table 2, multi-segment firms in seven of the nine countries exhibit lower mean (median) EPM and the differences in means (medians) are statistically significant in four (seven) countries. Multi-segment firms in Indonesia and the Philippines appear to have higher EPM, but the mean difference is not statistically significant, while the median difference is only significant for the Philippines.

# _4.2 Relatedniess_

We use two variables to capture the degree of relatedness (vertical and complementary) between the primary and secondary segments of a firm. The vertical relatedness variable measures the degree to which a firm integrates forward and/or backward into its secondary segment(s), given its primary segment. For example, if a car manufacturer takes over a car upholstery business, this would reflect a high backward vertical relatedness. If, in contrast, an electricity generation company takes over an electricity distribution business, this would reflect a high forward vertical relatedness. The complementarity variable measure the degree to which the primary and the secondary segments complement each other (forward) in marketing and distribution and/or (backward) in procurement. An example of forward complementarity is a peanut butter producer taking over (or expanding into) strawberry jelly production - the two products do not use the same inputs, and are not vertically related, but that use the same distributors and marketing agents. An example of backward complementarity would be merging together a gin producer and an aspirin producer, as they both use glass containers for their products. To ensure consistency across countries and to provide a common benchmark, the two variables are constructed from information in the US input-output tables.<sup>9</sup> The procedure for constructing the variables is detailed in Fan, Lang and Stulz (1998). It entails three steps. First, we create four matrices of inter-industry relatedness coefficients. This involves computing the coefficients between each pair of over 400 industries defined in
, and presented in the local People’s Congress at the beginning of each year.

# **2.3 Evidence of manipulation at the subnational level**

The quality and credibility of China’s officially released economic data have been scrutinized despite the lack of direct evidence. In recent years, a growing number of Chinese local governments significantly adjusted their economic data. In 2017, the provincial governor of Liaoning province acknowledged having inflated fiscal revenue by 20% per year in 2011-14. In 2018, the government of Tianjin released a revised GDP figure of 2016 that had been adjusted downward by one-third of the previously published number, while Inner Mongolia has reduced the 2016 data of fiscal revenue and industrial output by 25% and 40% respectively.<sup>19</sup>

> 17 This stands in stark contrast to the tertiary sector, whose value-added data can be divided into six subsectors. 18 The targets set by the upper-level governments, which can serve an important function of transmitting superiors’ preferences on the relative importance of economic growth to subordinates, are taken quite seriously by the lower-level governments. This also results in the top-down amplification of GDP growth targets (Li et al. 2019). 19 Similar cases have been found in 10 cities and districts in Hunan, Yunnan and Jilin provinces as well as in Chongqing. For detailed reports and analyses, see https://www.ft.com/content/0361c1a4-bcfe-11e6-8b45b8b81dd5d080; https://asia.nikkei.com/Economy/Chinese-local-governments-rush-to-admit-fake-data, and

9
exclusive concentration in the garment sector.

**Table 4: Employment contribution of EPZs**

||**EPZ employment (2008)**|**EPZ employment as % of national**<br>**industrial sector employment**|
|---|---|---|
|**Bangladesh**|218,299|3%|
|**DR**|124,517|30%|
|**Honduras**|130,000|30%|
|**Vietnam**|1,172,000|19%|
|**Ghana(Tema)**|2,025|35%|
|**Ghana(single units)**|26,534|.|
|**Kenya (EPZs)**|15,127|15%|
|**Kenya (single units)**|15,551||
|**Lesotho**|45,130|>80%|
|**Nigeria**<br>**(Calabar‐ est)**|1,156|<1%|
|**Nigeria**<br>**(Onne‐ oil &gas)**|20,000|N/A|
|**Tanzania**|7,500|2.5%|

Source: EPZ employment based on data from individual country EPZ authorities; national industrial employment from various sources Again the bigger concern about the African zones is not so much the scale of their programs at this stage, but the evidence that their growth may already be slowing. Vietnam and Bangladesh have managed to shift to exponential job growth over the last decade. But for African zone programs, the story of jobs is similar to that of exports – rapid growth in the first half of the decade followed by stagnation. Indeed, for African zones dependent on the garment sector (
of poverty and an increase in overall productivity – even if the jobs created pay little more than a subsistence wage or the employment is in less green industries and skill classes.

Some definitions focus on a subset of industries producing environmentally desirable outputs. Thus some studies, notably by the European Commission’s Environment Directorate, have used the OECD/Eurostat definition of the environmental goods and services industry (OECD, 1999), comprising “activities which produce goods and services to measure, prevent, limit, minimize or correct environmental damage to water, air and soil, as well as problems related to waste, noise and eco-systems. This includes technologies, products and services that reduce environmental risk and minimize pollution and resources.” That covers pollution management (e.g. air pollution control) and resource management (renewable energy plants and water supply). On this basis, green jobs constitute a small but significant share of total employment – 1.7% of total paid employment in Europe (EC, 2007). That is probably a higher fraction than a global estimate along UNEP lines would suggest; as UNEP notes, most of the documented growth in green jobs has so far occurred mostly in developed countries.<sup>1</sup> Jobs in the nuclear power sector are not included, and these are not generally regarded as ‘green’, although they are in a lowcarbon industry. Employees in many jobs might find that their jobs are not counted as ‘green’ despite the nature of the goods and services that they help produce. For example, jobs in the car industry are excluded, even though some may be devoted to developing lowcarbon vehicles.

Some studies have developed their own terminology, using data from detailed employment statistics or detailed company databases. The Pew Center, for example, defines the ‘clean energy economy’ as follows: “The ‘clean energy economy’ generates jobs, businesses and investments while expanding clean energy production, increasing energy efficiency, reducing greenhouse gas emissions, waste
income from labor and capital divided by income from labor and capital.

*** Comprehensive Income Tax: capital income is taxed under the personal income tax.

Source: TARKI, 1995; and own calculations.

We can conclude by saying that neither theoretical considerations nor empirical evidence build a strong case for more taxation of capital income for high income earners in Hungary. The simulations above show that the distributive impact would be small. Even if we are optimistic about APEH's tax collection rate, the tax office is severely limited in its ability to collect revenue, 28 For example, the TARKI study shows that 82% of all accounted expenses happen to be in the highest income decile. This source of income is the most unevenly distributed of all. The results show that, first, evasion of income taxes is highest and, second, by far the most tax evasion takes place in the highest income decile (probably as much as 90%).