rafmacalaba/datause-encoder-data huggingface dataset:
# **I.  Introduction**

Graduate education in business administration was developed in the U.S. around the turn of the twentieth century. The number of Master’s in Business Administration (MBA) degrees awarded grew rapidly in the U.S., especially in the second half of the century. MBA and similar graduate-level business programs took hold more slowly in other countries, but the number of such programs expanded more rapidly from the 1960s onward. There is little question that business education and the MBA degree in particular have been instrumental in training better managers and improving the productivity of a wide range of firms. But to date, there has been scarce empirical evidence of the utility of such degrees throughout the world. Rather, much of the emphasis of research has been on the quality and utility of graduate business degrees obtained in the U.S. and Europe. This survey is an attempt to determine the quality of business training and the utility of graduate business degrees in developing and transition countries.

Recently the IFC has developed the Global Business School Network (GBSN) initiative, aimed at improving the training and quality of business managers in developing and transition countries. In an effort to determine what firms from these countries require from business school graduates, the IFC used its extensive contacts with these firms to conduct a survey of the quality of business education in these countries.

The survey was conducted over a period of two months (January – February 2003). More than 1100 surveys were sent to firms by email and fax, and 283 responses were received from 78 countries. The results of the survey are analyzed across various dimensions: across regions, across income categories, across sectors and across firm sizes. It is important to note that the results should be viewed in the light of factors such as response rates across regions and across countries. But keeping in mind these limitations, the results shed light on important and uncharted areas.

The study begins with a discussion of the previous research on the subject in Section II, a description of the survey in Section III, and a discussion on the response rates for the survey in Section IV. Sections V – X present basic analyses of the strengths and weaknesses of local MBA graduates and what factors affect the perceived quality and quantity of MBAs hired by developing and transition country firms. Section XI presents an econometric analysis of the factors affecting the perceived quality of local MBA graduate training. Finally, the conclusions from the survey are presented in Section XII.

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An interesting observation is that these poor households spend a non-negligible share of their expenditure on leisure, gifts, and entertainment. This type of spending is often related to religious festivals and life events of family members. Compared to these expenditures, spending on education is quite low. This is likely due to the small number of children in the households, about 1.6 on average, in line with the low total fertility rate (TFR) in the country.<sup>15</sup> An additional explanation is that many of these households are exempt from education fees due to stipends for primary and secondary schooling as well as school feeding programs.

Health care expenditures represent about 6 percent of the total household expenditures. Out-ofpocket expenditures on health care are an important measure of household well-being, because catastrophic health shocks and spending are often a major risk for household impoverishment. A rule-of-thumb approach recommended by the World Health Organization (WHO) is that the ratio of out-of-pocket expenditure on health care should not exceed 40 percent of household income net of subsistence needs. (See WHO, 2010.) Applying this rule to our data, the average ratio of health care expenditure to total household expenditure, net of food consumption, is about 13.4 percent. However, for about 6 percent of the sample households, the ratio exceeds 40 percent, a level of spending that suggests these households are experiencing catastrophic health care spending.

# **3.2.3 Labor market activities**

Labor market activities vary depending on the gender of the head of the household (Table 4). The share of the jobless, including the unemployed and out-of-the-labor-force individuals among household heads, is 3 percent and 30 percent for male and female headed households respectively. Nonetheless, the female household heads’ overall rate of employment (70 percent) is significantly higher than the national average of 30 percent (according to the Labor Force Survey 2013). Male heads of households tend to work as either self-employed or day laborers, whereas female heads are more likely to work as paid employees. Social assistance programs often require the heads of households to be day laborers or jobless, but many are self-employed probably in subsistence business activities.

> 15 Unlike other countries that are similar to Bangladesh in their development status, fertility rates and population growth have steadily declined in Bangladesh. The TFR has declined from 3.1 to 2.3 between 2000 and 2014.

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Our baseline specifications use a panel data structure, that is, we examine the identical firms before and after the policy intervention (Basel III implementation). We restrict our data set of firms to SMEs only, that is, to firms with fewer than 250 employees (and split the estimations for small and medium-size firms in some cases). If SMEs confront challenges in accessing finance that are different from those confronted by large firms, focusing on SMEs only could help sharpen the identification of Basel III effects from cross-country data. In addition to the panel, we form the data set of firms with repeated cross-sections. Although the drawback is that we cannot track the individual firms over time using this data set, we can examine a richer sample of countries and firms. Therefore, we trade off cleaner identification for greater statistical power between the panel and repeated cross-section estimations to gain complementary perspectives.

The list of countries included in our analysis, together with the years of pre-treatment and posttreatment Enterprise Surveys, is available in table A.1 in the appendix. Overall, our sample contains 32 countries—29 countries with a panel structure of firms and three additional countries with repeated cross-sections. In our sample, 12 countries implemented Basel III, and among those,, nine countries have a panel structure.

Our panel data set contains 4,475 firms from 29 EMDEs—nine of these countries introduced the Basel III framework, and we consider them as the treated group, while the remaining 20 countries comprise the control group, which had not implemented the Basel III at the time when the posttreatment Enterprise Survey was conducted (therefore, the number of observations for the panel is 4,475 x 2 = 8,950). Our repeated cross-sectional data set contains 33,449 observations.

Our dependent variable, _access to finance as a constraint_ , is a categorical variable that represents the firm’s overall perception of its access to finance and reasonably aggregates different elements of the constraints in accessing finance (lending rate, loan volume, maturity, and collateralization). The values range from 1 to 5—that is, from access to finance being a very severe obstacle to it being no obstacle at all (we inverted the original scale for ease of interpretation). In the panel 11
# **Table 3. Characteristics of employed workers: 1983, 1989 and 2002**

||**Age**|**School**|**Hours**|**Wage**|
|---|---|---|---|---|
|Jan-83|||||
|Informal Self Employed|40.08|5.34|38.56|0.76|
|Informal Salaried|29.89|4.89|42.44|0.29|
|Formal Sector|32.39|7.17|39.59|1.00|
|Dec-89|||||
|Informal Self Employed|40.29|6.17|39.19|1.00|
|Informal Salaried|30.33|5.54|41.09|0.40|
|Formal Sector|32.99|7.67|38.69|1.00|
|Nov-02|||||
|Informal Self Employed|42.06|8.18|40.98|1.06|
|Informal Salaried|33.72|8.09|40.46|0.59|
|Formal Sector|35.63|9.68|40.87|1.00|

Author’s calculations using the PME. The table shows the mean age, years of schooling (school) weekly hours of work (Hours) and the relative average wage with respect to the formal sector.

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**Figure3: Old and young age dependency ratios in Russia compared to Eastern and Western Europe, 1990-2060**

<!-- Start of picture text --> 1990 2000 2010 2020 2030 2040 2050 2060 1990 2000 2010 2020 2030 2040 2050 2060<br>Year Year<br>Western Europe Eastern Europe Russia Western Europe Eastern Europe Russia<br>Source: UN World Population Prospects: The 2010 Revision. Projections after 2010, median variant. Source: UN World Population Prospects: The 2010 Revision. Projections after 2010, median variant.<br>50<br>50<br>40 40<br>30 30<br>20 20<br>Old-Age Dependency Ratio<br>Young-Age Dependency Ratio<br>10 10<br><!-- End of picture text --> Three factors account for the rapid aging of Russia’s population in the coming decades (Figure 4). First, fewer people are entering the labor force today due to the sharp decline in fertility rates in the early 1990s. Second, life expectancy, which declined between around 1985 to the 2005 before increasing in recent years, is projected to continue to rise in the future. Third, this decade, the large cohorts born in the mid- and late 1950s are set to retire. In the late 2030s and 2040s, the large cohorts of children born in the 1980s in response to Soviet family policy will retire.

**Figure 4: Total fertility rate and life expectancy in Russia compared to Eastern and Western Europe, 1990-2010**

<!-- Start of picture text --> 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br>Year Year<br>Western Europe Eastern Europe Russia Western Europe Eastern Europe Russia<br>Source: World Development Indicators Source: World Development Indicators<br>2 80<br>1.8<br>75<br>1.6<br>1.4<br>70<br>1.2<br>Life Expectancy at Birth (Years)<br>Total Fertility Rate (Birth Per Woman)<br>1 65<br><!-- End of picture text --> _Regional trends in Russia_ We explore the link between demographic trends and economic growth at the regional level in Russia.<sup>6</sup> This regional focus is motivated by three factors. First, cross-country analyses of growth are often unable to factor in country-specific circumstances. Therefore, we investigate differences within a country, while at the same time exploiting the heterogeneity of demographic trends among regions. For example, the share

> 6 For this, we use Rosstat data and Russian definitions – where working age population is from 16-59 years for men and 16-54 for women.

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available for most countries so results from such studies are questionable. By contrast, we can use data from household surveys and population censuses to compute measures of intra- and inter-regional income differences as well as measures of regional distinctiveness.

There are, however, some limitations to our approach. Since we exploit regional variation, country-wide factors are only captured by fixed effects in the analysis, so we have to bracket some theoretically interesting factors. Another, already-mentioned, issue is that internal borders can be endogenous to demands for sovereignty. This, however, is not a problem for our analysis, since we condition our expectations on a given set of borders. Different complications arise if a group’s homeland extends beyond existing administrative boundaries so the group might seek sovereignty in two or more adjacent regions, none of which individually has characteristics that our theory would associate with sovereignty demand. Alternatively, larger regions may include two competing sovereignty movements. We deal with these complications with appropriate right-hand-side controls in robustness tests.<sup>31</sup>

# **_Proxying Actual Sovereignty_**

We propose an economic measure of sovereignty: the share of regional expenditures that can be financed out of regional revenues. We have these data for 603 regions. The ratio ranges from almost 0 to 1 with a median level of 0.57, pooling all regions and countries. In some cases, the ratio is above 1, but we truncate the index since in those cases we consider that there can be effective autonomy.<sup>32</sup> An unbound version of the index is also used in robustness tests.

31 We code regions with more than one sovereignty movement; adjacent regions with the same sovereignty movement; groups that are not the majority in any single region, but are concentrated in two or more adjacent regions; and we check how these cases influence the results of the analysis.

32 Data for this variable come from national statistical yearbooks and the variable is coded for the year for which we have household survey data on income or the closest year, in a few cases before the year income is measured. We treat the 0-1 index as a continuous variable. The results presented later are robust to the use of Tobit regression, which accounts for the truncation of the variable at 1 (see supplement).

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seen as an intermediate (downward-biased) step towards global interpersonal inequality when survey data for measuring within-country distributions is not available (Milanovic, 2005).

Anand and Segal (2008) distinguish between two reasons why we might be interested in measuring the extent of global inequality. First, out of a concern for “global justice” we might be intrinsically concerned about the distribution of resources amongst the world’s citizens which mirrors the concern for inequality at the country-level (Pogge, 2002, Singer, 2002). This cosmopolitan view of the world assumes a global social welfare function which treats persons the same irrespective of their country of residence (Atkinson and Brandolini, 2010).<sup>9</sup> Second, changes in global inequality capture some of the effects of globalisation. Over the twenty-year period analysed in this paper, the world economy has become more integrated. We want to stress that any estimate presented here cannot be given a causal interpretation since there exist no counterfactual world economies. In addition, 1988 was certainly not the start of globalisation. However, since then the pattern of global trade and capital flows has changed dramatically, with the integration of China (Haskel et al., 2012), other developing countries (Goldberg and Pavcnik, 2007) and Russia into the world economy. Bourguignon (2012) also considers the effects of globalisation on global inequality, but he focuses on its effects on within-country distributions.<sup>10</sup> Ideally global inequality would be measured from a single representative global household survey, which would be analogous to measuring country-level inequality from national household surveys. In the absence of such a survey, we have to rely on combining national household surveys. Most of the literature on global inequality uses (i) distributional information (e.g. Gini indices) from secondary datasets, such as Deininger and Squire (1996), (ii) assumes that incomes or consumption are everywhere distributed according to a lognormal distribution, and (iii) uses average incomes from national accounts (e.g. Bourguignon and Morrisson (2002) and Sala-iMartin (2006)). Point (iii) implies a rejection of the often available mean income or consumption

> 9 This world view is not shared by everyone. For example, Bhagwati (2004) calls the concern with global interpersonal inequality a “lunacy”, because the individuals around the world “do not belong to a ‘society’ in which they compare themselves with the others” (p.67). Using a simulated world income tax, Kopczuk et al (2005) find that the current levels of foreign aid are consistent with preferences which value foreigners much less (or with the assumption that most international transfers are wasted).

> 10 In addition, our methodologies are quite different, since he uses GDP per capita combined with distributional data from PovcalNet and the OECD.

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|Formal Salaried|36.9|35.5|
|---|---|---|
|Informal Salaried|23.9|25.4|
|Formal Self-employed|3.1|2.2|
|Informal Self-employed|34.9|35.5|
|Unpaid Worker|1.2|1.3|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS. Notes: Official poverty lines are COP 269,362 and COP 281,384 per person in 2008 and 2009, respectively, according to MESEP (2010).

**Table 2: Socioeconomic and Labor Characteristics of Formal and Informal Workers in Colombia**

|||||**2008**|**Informal**||||**2009**|**Informal**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Formal**<br>**Salaried**|**Informal**<br>**Salaried**|**Fromal Self-**<br>**employed**|<br>**Self-**<br>**employed**|**Unpaid**<br>**Worker**|**Formal**<br>**Salaried**|**Informal**<br>**Salaried**|**Fromal Self-**<br>**employed**|<br>**Self-**<br>**employed**|**Unpaid**<br>**Worker**|
|**Gender**|**Male**|59.4|53.2|64.2|58.9|40.0|57.9|51.4|76.8|57.1|35.8|
||**Female**|40.6|46.8|35.9|41.1|60.1|42.1|48.6|23.2|43.0|64.2|
||**Until Primary Complete**|9.0|28.4|7.6|29.9|27.4|10.0|26.5|12.1|27.7|11.8|
|**Education Level**|**Until Secondary Complete**|44.1|53.0|38.6|45.2|46.6|42.7|50.3|32.3|46.0|55.9|
||**Tertiary or more**|46.9|18.5|53.8|24.9|26.0|47.2|23.2|55.6|26.3|32.4|
||**12 to 18**|0.7|5.1|0.7|1.7|8.2|0.6|4.3|0.0|1.3|13.2|
|**Age Group**|**19 - 35**|50.7|47.7|16.6|27.7|27.4|49.5|45.7|14.1|25.5|23.5|
||**36 - 64**|48.2|45.0|75.9|62.9|49.3|49.4|47.5|81.8|65.0|52.9|
||**Agriculture/Mining**|1.6|0.9|1.4|0.9|0.0|1.6|1.6|2.0|0.8|0.0|
||**Manufacturing**|15.3|14.4|17.2|10.8|12.3|17.9|14.4|16.2|11.4|12.9|
|**Economic Sector**|**Construction**|2.5|9.3|4.8|9.1|2.7|2.7|7.0|7.1|7.4|2.9|
||**Retail**|11.5|13.5|17.9|26.0|20.6|12.8|12.6|20.2|29.0|21.4|
||**Services**|56.1|54.4|55.9|45.9|34.3|51.6|54.7|44.4|41.5|48.6|
|**SISBEN Level**|**SISBEN 1 - 2**|3.7|46.0|0.0|37.4|33.8|3.8|41.8|1.4|36.7|30.1|
||**SISBEN 3+**|96.3|54.0|100.0|62.6|66.2|96.2|58.3|98.6|63.3|69.9|
|**Pt Ll**|**Below or at**|45.4|67.5|26.9|62.7|82.2|56.9|69.8|32.3|62.9|79.4|
|**overy eve**|**Above**|54.6|32.5|73.1|37.3|17.8|51.5|30.2|67.7|37.1|20.6|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

Economic shocks increase for all categories, especially among formal workers, for whom their incidence during 2009 doubles with respect to the year 2008 (Table 3). Other categories also see increases in the vicinity of 50 percent with respect to levels in 2008. Interestingly, there is a (modest) decrease in the incidence of health shocks between both years across all categories. These findings point to distinctive dynamics of shocks by their nature. Also, despite the increase in the incidence of shocks expected from the crisis, perceptions about the risks of losing employment or incomes and risk of death have not changed from 2008 to 2009. This is especially true across formal workers. This may indicate that either 10
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Figure 3 Income Distributions <!-- Start of picture text --> Cumulative percent of population<br>100<br>40-<br>20-<br>0<br>3 7 11 15 19 23 27 31 36 39 43 47 51 55 59<br>Monthly Income per person (Rp'OOO)<br>1984 ' 1987<br><!-- End of picture text --> Figure 4 Non-Food Consumption Shares <!-- Start of picture text --> Cumulative percent of population<br>100<br>60<br>20-<br>20<br>0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Non-food consumption share<br>- Rural 1984 ->- Rural 1987 + Urban 1984 A Urban 1987<br><!-- End of picture text -->
# **_For most freight forwarders a critical delay in clearance is 1 additional unexpected day_**

A delay is defined in our survey as the additional unexpected time that an import shipment has to spend at the border/terminal to be cleared such that the freight forwarder is at risk for breaching its contractual obligations. A quarter of the freight forwarders indicate that a critical delay consists of 1 additional unexpected day, but the other three-quarters are evenly distributed across very different time categories. When disaggregating the data by freight forwarder size some differences are identified in Figure 8. The majority of medium/large freight forwarders (63%) consider a delay to be an additional 3 to 10 unexpected hours at the border post or terminal while the majority of small freight forwarders (60%) consider a delay to be either staying overnight at the border post or terminal or clearance to take up to five additional unexpected days.

Additionally, we find that freight forwarders consider delays to be of relatively similar magnitude regardless of their main import sectors: for any sector between a third and (almost) a half consider a typical delay as having to stay overnight or at least 24 hours at the border post or terminal.

**Figure 8. Additional Unexpected Time Spent at Border/Terminal for Clearance by Freight Forwarders**

<!-- Start of picture text --> Share of freight forwarders<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>11%<br>1‐2 hours 9%<br>11%<br>14%<br>3‐5 hours 27%<br>15%<br>11%<br>5‐10 hours 36%<br>13%<br>19%<br>Overnight 9%<br>18%<br>27%<br>At least 24 hrs. 18%<br>27%<br>14%<br> 2‐5 days 0%<br>13%<br>4%<br>More than 5 days 0%<br>4%<br>Small Medium/Large Total<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

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Adding Space to the International Business Cycle Girum Dagnachew Abate and Luis Serv ́en<sup>_∗_</sup> The World Bank Group

**Keywords:** Growth, business cycle, common factors, spatial dependence **JEL classification:** F44, C23, F62

> _∗_ Email addresses: gabate@worldbank.org, lserven@worldbank.org. We are indebted to Paul Elhorst for helpful comments. The views expressed here are only ours and do not necessarily reflect those of the World Bank, its executive directors, or the countries they represent.
enhance the efficiency of expenditures and to rationalize government spending. However, in the mid-2000s, some of the efforts appear to have weakened.

From the early 2000s to 2007, real fiscal revenue growth in ECA was high and rising. As a share of GDP revenues were 33.6% during 1995-2000, and 32.5% during 2000-2004. As GDP accelerated, real fiscal revenue growth in ECA was high and rising and surpassed GDP growth in 2005-07 to be 35.2% of GDP. As a ratio to GDP, revenues rose the most in the LLMIC group (outside of the oil related revenues accruing to the OGE), and the least in the EU+ countries, even though real growth was 7.5% in 2007 for this group. When averages are weighted by GDP however, the OGE revenue to GDP ratio is fairly constant (implying that in the larger countries, growth was slower), though the LLMIC come out stronger. Among the EU10 countries, the revenue share to GDP was fairly constant when weighted, but rose for the unweighted average as small countries experienced a rising share. In countries where revenues followed patterns in imports, they would have exhibited more volatility relative to GDP. In 2007, 30% of ECA countries had real fiscal revenue growth above 10%<sup>6</sup> : Armenia, Azerbaijan, Belarus, Bulgaria, Georgia, Kazakhstan, Kyrgyz Republic, and Montenegro. Just under half the countries had real revenue growth over 10% in 2006, and 40% in 2005. By 2007, fiscal revenues to GDP were 36.4%, though the OGE were at 39%- in weighted terms (Figures 3.1a and 3.1b).

> 6 The GDP deflator is used in calculating real values.

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