rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3:** **_Gini Coefficients before and after Banking Crises_**

|**(a) Bef**|**ore, after end of, and long after end**|**of all of the crises i**|**n each**|**country**|||
|---|---|---|---|---|---|---|
|||Befor|e any|Soonafterend|Long af|terend|
|DZA|Algeria|40.1|_1988_|35.3 _1995_|||
|BGD|Bangladesh|25.9|_1983_|31.8 _2000_|||
|BWA|Botswana||||60.4|_1989_|
|BRA|Brazil|59.5|_1985_|59.2 _2001_|||
|BDI|Burundi<br>|33.3|_1992_||||
|CHL|Chile||||56.3|_1997_|
|COL|Colombia|59.1|_1980_|59.3 _1998_|59.3|_1998_|
|CRI|Costa Rica|42.5|_1990_|47.9 _1998_|||
|CIV|Côte d’Ivoire|40.1|_1986_|36.8 _1994_|||
|DOM|Dominican Republic|49.2|_1995_||||
|EGY|Egypt||||33.5|_1997_|
|SLV|El Salvador||||53.3|_2000_|
|ETH|Ethiopia||||34.1|_1992_|
|GMB|Gambia, The||||49|_1995_|
|GTM|Guatemala||||58.4|_1993_|
|HND|Honduras||||56|_1993_|
|IND|India||||32.5|_1990_|
|IDN|Indonesia|33.7|_1990_||||
|IRN|Iran||||44.5|_1992_|
|JAM|Jamaica|38.9|_1990_||||
|JOR|Jordan<br>||||38.6|_1992_|
|LSO|Lesotho||||59|_1992_|
|MDG|Madagascar|46.8|_1980_|46.1 _1993_|42.3|_2000_|
|MYS|Malaysia|48.6|_1984_||||
|MAR|Morocco||||39.5|_1998_|
|NAM|Namibia||||74.3|_1993_|
|NPL|Nepal|31.7|_1984_|36.7 _1995_|||
|NGA|Nigeria|38.7|_1985_|50.6 _1996_|||
|PAK|Pakistan||||32.2|_1993_|
|PAN|Panama||||56.6|_2000_|
|PGY|Paraguay|39.7|_1990_||||
|PER|Peru||||49.8|_2000_|
|RWA|Rwanda||||28.9|_1984_|
|SLE|Sierra Leone|62.9|_1989_||||
|ZAF|South Africa||||57.8|_1996_|
|LKA|Sri Lanka|32.5|_1985_|34.4 _1995_|||
|LCA|St Lucia||||42.6|_1995_|
|SWZ|Swaziland|60.7|_1994_||||
|THA|Thailand|45.2|_1981_||||
|TTO|Trinidad||||41.4|_1990_|
|TUN|Tunisia||||41.5|_1993_|
|UGA|Uganda|43.8|_1990_|43.1 _1999_|||
|YEM|Yemen|39.4|_1992_|33.4 _1998_|||
|ZMB|Zambia|56.9|_1992_|51.2 _1997_|||
|ZWE|Zimbabwe|39.4|_1990 _||||

19
WPS3182

# **The International Finance Corporation’s MBA Survey: How Developing Country Firms Rate Local Business School Training**

**Azam Chaudhry Global Business School Network International Finance Corporation**

## World Bank Policy Research Working Paper 3182, December 2003

_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._
of GDP in 1988 to 14.8% in 1992. In the US and Switzerland pension spending amounted respectively to 4.8% and 7.3% of GDP. It should be noted that pension spending absorbs 14.8% of GDP in Austria and 10.8% in Germany.

A comparison between Hungary and Argentina shows that the two countries (prior to the recent Argentine reform) had broadly similar system dependency ratios and average replacement rates, thus implying similar required contribution rates for financial equilibrium of their respective systems (Vittas 1993c). The much higher pension expenditure to GDP ratio in Hungary implies that covered wages are a much higher fraction of GDP in Hungary than in Argentina. This is a result of the wider pension coverage and perhaps also of the smaller relative importance of profits and other nonlabor income in Hungary. Covered wages amounted to 42% of GDP in the Czech Republic, 37% in Hungary, 41% in Poland, and 29% in Russia. In Argentina, the corresponding ratio was estimated at 20% in 1990. In Chile, covered wages in the new reformed pension system amounted to 34% of GDP in the same year. One consequence of this feature is the potentially much higher transition cost of pension reform in Hungary and other Eastern European countries.

Table 3 Pension Expenditures, 1992

|RCR|CWGDP|PEGDP|
|---|---|---|
|Czech Republic<br>24|42|10.2|
|Hungary<br>29|37|10.6|
|Poland<br>36|41|14.8|
|Russia<br>16|29|4.6|
|USA<br>9.6|50|4.8|
|Switzerland<br>11.6|63|7.3|
|RCR:<br>Required Contribution Rate|||
|CWGDP:<br>Share of Covered Wages in GDP|||
|PEGDP:<br>Share of Pension Expenditures in GDP|||

Source: World Bank Staff Estimates

**Strategic Manipulation and Dispersion of Pensions.** The pension systems of the four countries also suffer from strategic manipulation and growing dispersion of pensions. Strategic manipulation is encouraged by using shorter than lifetime career earnings for determining initial pensions. In the Czech Republic pensions are based on the best 5 years of the last 10 years of employment, in Poland on the best

II
on a third or a quarter of the indicators on average in most regions, while the disclosure rate is even lower in Oceania at only 15.3%.

**Table 9: Average disclosure rate by region**

|**Region**|**_All_**|**_MNEs_**|**_Large _**|**_SMEs_**|
|---|---|---|---|---|
|Africa|40.8%|45.0%|41.3%|26.9%|
|Asia|46.0%|49.8%|46.5%|25.8%|
|Europe|44.6%|45.6%|47.0%|31.8%|
|Latin America & the Caribbean|46.9%|45.1%|49.6%|34.2%|
|Northern America|43.8%|45.4%|43.9%|32.8%|
|Oceania|36.3%|44.5%|33.5%|15.3%|

Finally, Table 10 shows the average disclosure rate by a country’s income status. Focusing on the full sample, the average disclosure rate is highest in non‐OECD high‐income countries (47.7%) and upper‐ middle‐income countries (47.2%), which is surprisingly followed by least developed countries (47%). However, the latter category only includes four companies from Bangladesh and one company from Angola and is thus not representative. Surprisingly, companies in OECD countries show somewhat lower disclosure rates (44.1%). Other low‐income (only Zimbabwe included) and lower‐middle‐income countries show the lowest disclosure rates. Among MNEs, the average disclosure rates exceed 45% across all income categories except for lower‐middle‐income countries (37.9%). Among large domestic companies, disclosure rates are lowest in the lowest income categories, while SMEs generally tend to show low disclosure rates except for SMEs based in non‐OECD high‐income countries (42.2%).

**Table 10: Average disclosure rate by country’s income status**

|**Income/Country status**|**_All_**|**_MNEs_**|**_Large _**|**_SMEs_**|
|---|---|---|---|---|
|DAC‐LDC|47.0%|63.0%|36.4%||
|DAC‐LMICT|38.0%|37.9%|46.2%|15.2%|
|DAC‐OLIC|13.0%||13.0%|13.0%|
|DAC‐UMICT|47.2%|45.1%|49.6%|34.3%|
|Non‐OECD / Non‐DAC|47.7%|51.3%|43.7%|42.2%|
|OECD|44.1%|45.3%|46.0%|31.1%|

Note: OECD = Organisation for Economic Co‐operation and Development, DAC = Development Assistance Committee, i.e. a country receives development aid through the OECD DAC, LDC = Least Developed Countries, LMICT = Lower Middle Income Countries and Territories, OLIC = Other Low Income Countries and Territories, UMICT = Upper Middle Income Countries and Territories.

In sum, we find that MNEs and large companies show the highest disclosure rates in our sample, while disclosure rates of SMEs are substantially lower. Focusing on the 15 sectors with the highest disclosure rates, MNEs and large companies show a large overlap with some exceptions. Consumer durables show the highest disclosure rates for both types of companies. However, retail, mining, and telecommunications only show high disclosure rates for MNEs, but not for large companies. By contrast, water utilities, media, forest and paper products, and energy show high disclosure rates for large companies, but not for MNEs.

Focusing on different organization types, average disclosure rates are highest for private companies and partnerships both in the MNE and large company samples, while they are highest for subsidiaries in the 14
# A Appendix

## A.1 Sampling Strategy

The starting point for the sample frame is the QSDS data set for 2000 and the second round of QSDS data for 2004 (Reinikka and Svensson, 2005c). The QSDS data set consists of a total of 155 health facilities. The sample design for the QSDS was governed by three principles. First, the attention was restricted to dispensaries (i.e., health centre III) to ensure a degree of homogeneity across sampled facilities. Second, subject to security constraints, the sample was meant to capture regional di¤erences. Finally, the sample had to include facilities from the main ownership categories: government, private non-pro...t, and private for-pro...t providers. These three considerations led to the choice of a strati...ed random sample, which was based on the Ministry of Health facility register for 1999. The register includes government, private non-pro...t, and private for-pro...t health facilities, but is known to be inaccurate with respect to the latter two. A total of 155 health facilities were sampled. On the basis of existing information on private-for pro...t and non-pro...t, it was decided that the sample would include 81 government facilities, 44 private non-for-pro...t facilities, and 30 private forpro...t facilities. As a ...rst step in the sampling process, 8 districts (out of 45) had to be dropped from the sample frame due to security concerns.<sup>41</sup> From the remaining districts, 10 districts, strati...ed according to geographical location, were randomly sampled in proportion to district population size. Thus, three districts were chosen from the Eastern and Central regions, and two from the Western and North regions.

### A.1.1 Part 1: Sampling of Villages

Our initial sample frame for the household survey thus consists of 81 government facilities and their “catchment”areas. The catchment area of a facility is operationalized as the ...ve-kilometer radius around the facility. For di¤erent reasons, all these facilities/catchment areas could not be included in the sample. First, three government facilities in Soroti could not be surveyed in the second round of the QSDS due to security concerns. Second, detailed maps (covering at least the ...ve-kilometer radius around the facility) and the corresponding census data could not be collected for three units.<sup>42</sup> Third, for some facilities, a signi...cant part of the catchment area lies outside the facilities’administrative boundaries. These facilities/catchment areas were there-

> 41The eight districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

> 42Uganda Administrative Maps from the Cartography Department at the Uganda Bureau of Statistics. These maps are drawn with the sub-county level as the highest administrative unit and the village as the smallest unit. The maps were drawn in September 2001 (some earlier) as a preparation for the 2001/2002 Census.

30
consumption tax needed to redistribute all SSM duty revenues. We then use the resulting prices to assess the poverty impacts of the SSM duties with and without this revenue redistribution.

# **Data**

We obtained information on the historical distribution of yields for maize, rice and wheat from the FAO's FAOSTAT database which contains information on crop yields for 211 countries and regions for the period 1961–2009. From the WTO's Integrated Database we obtained tariff-linelevel information on bound and applied tariff rates for member countries. Using the reported unit values of cereal imports from the combined FAO database, we expressed all tariffs in their _ad valorem_ equivalents.

We used a set of 31 household surveys representing globally distributed low- and middleincome countries (Table 2) collected between 2002 and 2010. All of the surveys contain household-level information on production and consumption of the four crops considered in this study (maize, rice, soybeans and wheat) as well as total household expenditures which allow us to assess the impact of changes in productivity and prices for these commodities on household welfare.

# **Methodology**

## **Stochastic modeling of yields**

Our analysis of the distribution of yields for our sample of 31 countries shows that the median standard deviation of yields from their average of the previous 3 years is lowest for rice (12.8 percent) and highest for soybeans (19 percent). Of course, many countries in our sample— particularly those where crops are grown under dry land conditions—experience much greaterthan-average volatility, especially for wheat where a quarter of the countries experience volatility greater than 25 percent.

The univariate distribution of individual yields is not enough to capture their impacts on the economic variables of interest because crop yields are correlated across regions and commodities in response to correlated shocks such as those resulting from weather. These correlations raise the probability that Q-SSM measures will be introduced on more than one product in the same year, raising the overall impact of the measure in that year and the variance of the overall effects of the Q-SSM on the poor. To capture this important feature of the world, 8
and do not include information on quality of service. We have access to the databases for 2002, 2003, 2004 and 2006.

The original household sample covers 2,400 households in 135 villages in 16 of the poorest municipalities of the Philippines (Chase and Holmemo, 2005). The second survey was fielded in the same 135 villages and the survey team managed to re-interview 2,092 households (about 87.2 percent of the original number of households). In this paper, we use data on the households which were interviewed in both rounds and were engaged in farming activities during both periods. We focus on farmers, given that we have identified clear channels through which mobile phones could positively affect their welfare.

The panel dataset contains detailed information on mobile phone ownership ( _mobile_ ), poverty status, consumption, shocks experienced, gifts received from friends and relatives, and migration patterns. We have information on the years of education of the household head ( _Edu Head_ ), of his/her spouse ( _Edu Spouse_ ) and of the most educated household member apart from the head and his/her spouse ( _Max Edu_ ), on the number of household members ( _HH Size_ ), on the number of household members above the age of sixty ( _Above Sixty_ ) and under the age of five ( _Under Five_ ), on the age of the household head ( _Age Head_ ) and his/her spouse ( _Age Spouse_ ), and on whether the household owns land for purposes other than residence ( _Own Land_ ).

Using the data available, we construct a measure of per capita monthly consumption ( _pc cons_ ). For each household member above five years of age, we have detailed information on whether they were working in the three months prior to the survey and, if so, in which sector. We can thus calculate the number of household members working in the farm and in the non-farm sector ( _Working Farm_ and _Working non-Farm_ ).

During the second survey, detailed information on migration patterns was collected. We use this information to generate, for each original household for which at least one original member still lived in the village, a dummy equal to one if at least one household 6
15 Soviet subsidy to Hungary foi. any estimted future price of a barrel of oil." 4

# 4. COMPARISON WITH OTHER ESTIMATES

The Price of Hungarian Exports Before comparing our estimates to others, we first discuss some data that will be useful in assessing other estimates. Despite the fact that Hungarian exports are of lower quality than Western products, our investigation, summarized in table 3, finds that Hungarian exporters would have been able to obtain considerably more in domestic currency by selling for convertible currency in the Soviet Union than by selling in transferable roubles.'5 In column 3 of the export section of the table, one can observe (by sector) the number of forint an untaxed Hungarian exporter (or importer) is expected to earn (pay) by exporting and selling for dollars compared to transferable roubles. For the exporting enterprise itself, its incentives are dependent on the system of trade taxes and subsidies. In column 6, we note that all sectors except the food processing industry expected to earn more in domestic currency after the switchover. t<sup>6</sup> These data show that the microeconomic incentives to Hungarian

**4As** mentioned above, part of the increased switch-over costs attributible to using updated energy prices derives from a reduction in the TR price of oil in 1990 relative to 1988. Thus, only adjusting the dollar price of oil will not totally eliminate the difference in the estimate of the switch-over costs between 1988 and 1990.

'<sup>6</sup> Based on a recent survey of Polish enterprises, Rosati (1990) has found similar results in Poland.

'It is assumed in column 6 that the subsidies and taxes on trade among the former CMEA countries will be eliminated. (We ignore MFN tariff rates and any export subsidies which will be uniformly applied.) The actual situation may vary as well because of the costs of the exporting (continued...)
- _The Demographic and Health Surveys (DHS)_ : these surveys cover seven out of the eight countries in the region; they are nationally representative and provide data on women and their children’s health and other outcomes. The data collected in different countries is comparable because the surveys implement near-identical questionnaires across countries. Three core questionnaires are used to collect data by the DHS: a household questionnaire, a women’s questionnaire, and a men’s questionnaire. In this study, we mostly focus on the women’s questionnaire.

- _World Value Survey (WVS)_ : WVS is a cross-national opinion survey that monitors cultural values, attitudes, and beliefs using nationally representative surveys. It uses a rigorous high-quality research design to collect data using a standardized questionnaire in almost 100 countries. The WVS measures, among others, support for gender equality, attitudes toward work and family, and subjective wellbeing, and uses agree-disagree Likert scales to measure socio-cultural and political attitudes from respondents.

For the DHS we focus on six South Asian countries —Afghanistan, Bangladesh, India, Maldives, Nepal and Pakistan. For WVS, we focus on three South Asian countries —Bangladesh, India and Pakistan—for which data are available. The DHS and WVS data have been collected in several waves roughly over a period of 20 years, from 1990 to 2018. The coverage however, varies by country (see Table A2). For instance, while DHS data is available for Bangladesh in eight waves, there is only one wave of data, 2015, available for Afghanistan. The DHS collects data from women aged 15-49 years. The age range in WVS is wider, however, so we restrict our sample to 15-49 for comparability with DHS. The length and breadth of coverage of the DHS and WVS data across countries in South Asia allow us to examine changes in key gender-related outcomes and social attitudes. We trace women born over half a century, from 1940 to 2000.

Table A2: Data availability for DHS and WVS surveys:

||**1990**|**1991**|**1992**<br>**1993**|**1994**<br>**1995**|**1996**<br>**1997**|**1998**<br>**1999**<br>**2000**<br>**2001**<br>**2002**<br>**DHS**|**2003**<br>**2004**<br>**2005**<br>**2006**||**2007**<br>**2008**<br>**2009**<br>**2010**<br>**2011**<br>**2012**|**2013**<br>**2014**|**2015**<br>**2016**|**2017**<br>**2018**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**AFG**|||||||||||x||
|**BGD**||||x|x|x|x||x<br>x|x||x|
|**IND**|||x|||x|x||||x||
|**MDV**|||||||||x|||x|
|**NPL**|||||x|x|x||x|||x|
|**PAK**|x|||||||x||x||x|
|||||||**WVS**|||||||
|**BGD**|||||x|x||||||x|
|**IND**|x|||x||x|x||x||||
|**PAK**|||||x|x|||x|||x|

51
Diener, Ed, Eunkook M. Suh, Richard E. Lucas & Heidi L. Smith. 1999. “Subjective Well-Being: Three Decades of Progress.” Psychology Bulletin 125(2):276—303.

Diener, Ed & Robert Biswas-Diener. 2000. “New Directions in Subjective Well-Being Research: The Cutting Edge.” (mimeograph).

Elbers, Chris, Jean O. Lanjouw & Peter Lanjouw. 2003. “Micro-Level Estimation of Poverty and Inequality.” Econometrica 71(1):355—64.

Fafchamps, Marcel, Christopher Udry & Katherine Czukas. 1998. “Drought and Saving in West Africa: Are Livestock a Bu ff er Stock?” Journal of Development Economics 55(2):273—305. Fafchamps, Marcel & Agnes R. Quisumbing. 2003. “Social Roles, Human Capital, and the Intrahousehold Division of Labor: Evidence from Pakistan.” Oxford Economic Papers 55(1):36— 80.

Fafchamps, Marcel & Forhad Shilpi. 2003. “The Spatial Division of Labor in Nepal.” Journal of Development Studies 39(6):23—66.

Fafchamps, Marcel & Forhad Shilpi. 2005. “Cities and Specialization: Evidence from South Asia.” Economic Journal . (forthcoming).

Fafchamps, Marcel & Forhad Shilpi. 2008. “Subjective Welfare, Isolation, and Relative Consumption.” Journal of Development Economics . (forthcoming).

Ferreira, Francisco H.G., Peter Lanjouw & Marcelo Neri. 2003. “A Robust Poverty Pro fi le for Brazil Using Multiple Data Sources.” Revista Brasileira de Economia 57(1):59—92.

Frey, Bruno S. & Alois Stutzer. 2002. “What Can Economists Learn from Happiness Research?” Journal of Economic Literature XL:402—435.

35
all. Unfortunately, typically little if any effort is devoted to estimating, let alone reporting, the substantial margins of error in any other governance and/or investment climate indicators – objective or subjective, aggregate or individual. A key advantage of the WGI is that we are explicit about the accompanying margins of error, whereas in most other cases they are often left implicit or ignored altogether.<sup>3</sup> Despite these margins of error, the WGI are sufficiently informative that many cross-country comparisons result in statistically (and likely also practically) significant differences in estimated governance. In comparing governance levels across countries, for example, we document that over 65 percent of all cross-country pair-wise comparisons using the WGI for 2007 result in statistically significant differences at the 90 percent significance level, and nearly 74 percent of comparisons are significant at the less stringent 75 percent significance level. In assessing trends over time, we find that 31 percent of countries experience significant changes over the decade 1998-2007 in at least one of the six indicators (roughly evenly divided between significant improvements and deteriorations). This highlights the fact that governance can and does change even over relatively short periods such as a decade. This should both provide encouragement to reformers seeking to improve governance, as well as warn against complacency in other cases as sharp deteriorations in governance are possible.

The aggregate indicators that we report constitute a useful way of organizing and summarizing the very large and disparate amount of information on governance embodied in all of our underlying data sources. The specific aggregation procedure we use also allows us to calculate explicit margins of error to capture the inherent uncertainties in measuring governance. At the same time, we recognize that for some purposes the information in the many individual underlying data sources can be of interest to users. For example, several of these provide highly specific and disaggregated information about particular dimensions of governance that could be of interest for monitoring particular reforms. For this reason, we report country scores on the individual indicators underlying our aggregate governance indicators, on the WGI website at <u>www.govindicators.org. These disaggregated underlying indicators are</u>

> 3 The only other governance-related indicators that we are aware of that now report margins of error are the Transparency International Corruption Perceptions Index and the Global Integrity Index.

2