rafmacalaba/data-use-mentions-extended huggingface dataset:
imbalance in the regression analysis by using weights equal to the inverse of number of regions in each country).

The dominant approach in the literature has been to analyze separatist conflicts using countries or country-years as units of analysis. This approach often fails to establish a close connection between theoretical expectations and empirical results. Several findings from country-year studies of civil war are based on spurious correlations. For example, a positive correlation between civil war and a measure of a country’s resource-dependence is thought to support the hypothesis that civil wars occur as a result of armed groups’ motivation to loot. But it is often the case that civil wars in resource-dependent countries take place in a resource-poor part of the country with no clear link between resources and violence. By disaggregating the data to sub-national regions, we can establish a closer correspondence between theory and evidence. Another approach has been to use ethnic groups as units of analysis. This also has limitations for our study. First, it is not clear which are the relevant groups. Most studies pick linguistic groups, but racial, religious, or regional cleavages may be more salient in different countries and may generate sovereignty demand. Second, claims for self-determination are also often made by non-ethnic groups or aggregations of ethnic groups, which we would not be able to account for using ethnic groups as units. Third, ethnic groups demanding sovereignty usually have a geographical referent in mind and pre-defined political boundaries are often useful in establishing such a geographical referent.

For these reasons, we find it advantageous to focus on administrative regions. Regions are governance units which can help groups organize their demands for sovereignty, so it is important to capture regional characteristics and not simply group differences. As a practical matter, many of our explanatory variables, such as income and inequality, are simply not measurable at the group level. While many group-level studies of separatism claim to use measures similar to ours –for example, of the degree of economic inequality among ethnic groups—household income data that are necessary to measure group-based
precipitation and storms), we restrict attention to firms in those cities, covering approximately a quarter of the full sample. Conclusions concerning non-climatic variables remain qualitatively unchanged in the full sample.

According to ISIC factor estimates, disruptions of **electricity** supply are significantly more prevalent within the mining sector, manufacturing of fabricated metal products as well as coke and petroleum refinement. By contrast, for air transport service providers, post and telecommunications as well as producers of telecommunication equipment, total annual blackout durations are significantly below average. This suggests that effective measures are taken to shield critical infrastructure to prevent outage-inflicted damage from spreading further. Moreover, larger and private firms tend to experience fewer outages while at the same time many outages cause firms to invest in own back-up generation. On the macro level, dynamism, high population density and growth is associated with more vulnerable electricity infrastructure while on the other hand outages and the business cycle are negatively related. Natural disasters do not seem to have a systematic impact on blackouts on the global level.

**Water** infrastructure disruptions tend to occur within the chemical industry, gastronomy and the production of food and beverages while wood and paper industries suffer significantly less water supply disruptions. As with electricity, larger firms experience less disruptions, while all other firm-level variables show no significance performance. On average, economies with higher urbanization are associated with fewer water supply irregularities. Concerning natural hazards, extreme precipitation and storms are

> 1 For a detailed description on how disaster risk variables have been constructed from hourly climate data, see appendix A.2.

8
Africa, the trucking industry in East Africa is more competitive and mature. Freight transportation rates are determined by market forces rather than government regulations. In Rwanda, deregulation of the trucking industry illustrates the close correlation between high transport prices and cartelization (box 2.1). The largest professionalized trucking companies claim 20 percent of the market share, a rate comparable to Europe and North America. There are 20 large companies that operate over 100 trucks each. The largest Kenyan company owns a fleet of 600 trucks.

> 1 LPI is an annual survey of international freight forwarders conducted in 155 countries. The survey aggregates the responses of survey participants concerning several factors—among them transport, warehousing, and border clearance—into an index that is used to rank and rate global logistics for trade.

10
|.01|.06|.10|.16|.14|.07|.15|.10|.09|

Note. All numbers come from the author's computation based on _A Survey of State Enterprises: 1980-89._ share of output under the government's mandatory plan, 8which was reduced from 64% in 1984 to 57% in 1989 for the firms in our sample.

3. More discretion for managers of state enterprises to determine employees' wages. Traditionally the government set an employee's wage as an almost deterministic function of his or her age, education, location, tenure and gender, leaving managers with little leverage to induce employee effort. This was reinforced by the employment guarantee.<sup>9</sup> To give more discretionary power to managers of state enterprises, the Chinese government granted some firrns _managerial wage discretion,'_<sup>_0_</sup> controlling only the growth rate or aggregate amount of wages for the firm. The proportion of firms with the _managerial wage discretion_ mainly increased in the latter 1980s: from 0.5% in 1980, to 5% in 1984, 20% in 1987, and 35% in 1989.

8A state enterprise's output fell into three categories: mandatory plan by the government, which was set up by the government and must be fulfilled, directive plan that was suggested by the government, and own plan which was under the discretions of the manager.

9Part of the reason was that the "fall-back position" for the fired employees would be quite unattractive: it was very difficult for the gurantee to find jobs outside the incumbent firm due to a rigid labor market; in addition, since the social security and welfare function were carried out by the state enterprises rather than the market or the state, the dismissed employees would lose much of their firm-specific benefits such as housing, tenure wage, pension plan if they left the incumbent firms.

10It is called a firm-specific wage
# **I – Introduction**

Measurement of poverty rates, average per capita consumption, the size of the middle class, and other welfare indicators is traditionally conducted using survey data. To allow for frequent monitoring and to contain the costs of gathering detailed information, such surveys usually visit only a small sample of the population. When this sample of the population is representative, welfare surveys provide reliable estimates of poverty incidence for the entire population at a small fraction of the cost that would be required to survey each person in the country.

However, this approach necessarily leads to sampling errors, and consequently, a typical household income or expenditure survey cannot produce statistically reliable welfare estimates for small geographic units. This study starts with nationally (and regionally) representative data from each of the four Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan and Uzbekistan) for which they are available. The analysis then proceeds to sharpen the reliability of the survey estimates to allow reporting at a level below what is traditionally reported (moving from “oblast” level estimates, to “rayon” level estimates). Because no such survey data are available for Turkmenistan, the country is excluded from the analysis.

There are two survey types used in the approach described in this report. In Kazakhstan and the Kyrgyz Republic, welfare estimates are derived from the Household Budget Surveys (HBS) conducted by the national statistical agencies. In Tajikistan and Uzbekistan, HBS data were either inappropriate for this analysis, or unavailable. Instead, the welfare estimates for these countries are derived from the baseline surveys for two World Bank studies: Listening to Tajikistan (L2T) and Listening to the Citizens of Uzbekistan (L2CU).

Each of these four surveys are designed to be representative at the national level in the respective country, for rural and urban areas, and at the region (oblast) level. For this reason, the national official rates of poverty monitored in each country are not usually published for administrative units below the regional level (with some exceptions)
the platforms. However, firms using e-commerce, even the small ones, are from a selected group: they are owned and managed by individuals who are more educated and younger than the owners and managers of more typical firms.

The findings on the two case studies of this paper are encouraging as they seem to tilt the balance of evidence towards a positive impact on small, informal firms. That being said, these results cannot be easily generalized given the selective use of e-commerce among firms. While many small firms are using e-commerce platforms to reach a larger consumer base, the vast majority of them are still reliant on traditional brick and mortal retailing. Potential causes of this selective adoption of e-commerce include limited access to digital infrastructure among small firms and their target customers, informational barriers to the adoption of new technologies by firms, and limited capability of firms to profit from e-commerce. Understanding these causes better will be important for designing policies to broaden the development impact of e-commerce.

The rest of the paper is organized as follows. Section 2 describes the two e-commerce platforms examined in this paper and the key descriptive characteristics of the surveyed firms, which sell on them. Sections 3 and 4 discuss the main results of the analysis of the surveys of sellers operating on Chaldal and Daraz. Section 5 discusses how the sellers are a select group of firms. Section 6 concludes the study.

# **2 Setting and Data**

## **2.1 Two Digital Platform Models**

Chaldal is an online grocery service operational in four cities in Bangladesh. Established in 2013, it offers 1-hour delivery on over 6,000 products. Chaldal is currently operational in 4 cities. Chaldal operates its own warehouse system in each city to guarantee timely supply to the platform. Chaldal 4
**Figure 2.2: Original Altman Z-score model**

## **Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E**

Where: A = Working Capital / Total Assets B = Retained Earnings / Total Assets C = Earnings Before Interest & Tax / Total Assets D = Market Value of Equity / Total Liabilities E = Sales / Total Assets _Source_ : Altman, 1968.

When based on internal data to be estimated, a statistical model can capture specific risks the government is exposed to and be based on actual performance by beneficiary entities. Calibrating a model based on internal data requires a significant amount of historical information.

# **_Scenario analysis_**

In scenario analysis, various scenarios for key risk drivers (e.g. macroeconomic variables, revenue drivers, cost drivers, etc.) are constructed. Risk managers then estimate how scenarios impact financial performance of beneficiaries and their ability to service debt.<sup>19</sup> A scenario analysis model has to make assumptions about how key risk drivers interact with each other and how they affect an entity’s performance. Also, risk managers may need to define a threshold beyond which financial performance translates into a credit event, such as a default.

Scenarios can be constructed using deterministic or stochastic processes. Scenario analysis based on deterministic processes usually uses a smaller number of discrete scenarios and often no probabilities are attached to the respective scenarios. Risk managers may define a base case (most likely scenario) and several risk scenarios defined by an adverse development in individual risk drivers or a combination of a few risk drivers. If stochastic processes are constructed, a probability distribution for key risk drivers and their dependent relationships are estimated. A large number of scenarios can be simulated (e.g. using Monte Carlo simulation).<sup>20</sup> The outcome of scenario analysis can be an estimate of the frequency of default events. A default probability distribution
_<u>Table 9: Factor price changes due to CAFTA</u>_

|<br>**_Non-Farm Segment:_**|<br>Δ**P**|<br>Δ(**P/CPI)**|**% of Full**<br>**Lib**|
|---|---|---|---|
|**Skilled Labor**|0.7|1.2|60|
|**Unskilled Labor**|1.0|1.6|55|
|**Capital**|0.9|1.5|85|
|**Sk/Unsk wage gap**||-0.4||
|**_Farm Segment:_**||||
|**Skilled Labor**|-2.0|-1.4|87|
|**Unskilled Labor**|-4.5|-3.9|100|
|**Capital**|4.1|4.7|92|
|**Sk/Unsk wage gap**||2.6||
|**_Price indexes:_**||||
|**Food price index**|-1.4||39|
|**Non food price index**|0.0||-1|
|**CPI**|-0.6||24|

Sources: Author calculations from model results. Notes: The first column, Δ **P** , represents the percent variation of the price of each factor with respect to the initial levels, and Δ **(S/CPI)** is the percent variation of the price deflated by the Consumer Price Index. The column, **% of Full Lib** ,
with ideology, their degree of self-interest, and other personal characteristics, their demand for redistribution.

To the extent that we are unable to completely observe ideology and self-interest, part of the correlation between these variables and demand for redistribution or equality perceptions will be captured by the error term. In other words, e1i and e2i will be correlated. Similarly, we expect that the e1i might be correlated with equality perceptions, generating issues of classical endogeneity.

# 3 Data description

The Social Inequality surveys of the International Social Survey Programme (ISSP) are the main data source for this paper. We use all available waves covering the years 1987, 1992, 1999, and 2009. The initial sample of 9 countries (1987) was expanded in each wave to reach 26 countries in 2009. The samples are representative at the country level, with sample sizes per country and year varying between 1,000 and 2,000. These surveys include almost all the information needed to estimate the model described above. They include the two dependent variables: perceptions of inequality and demand for redistribution, as well as information on voting or political preferences to construct the ideology variable and information on income and education used to account for self-interest. Finally, they record a host of individual socio-economic characteristics – employment, gender, age, location of residence – to act as additional controls. A mix of other data sets, described in detail below, are used as sources for the objective levels of inequality, poverty, unemployment, government expenditures, which together represent the economic context variable.

In 1992, 1999, and 2009, the ISSP surveys asked individuals to choose among five different pictures the one that best described the _type_ of society of the country in which they live. More in detail, the specific question and possible multiple-choice answers are shown below in figure 1.

The diagrams and the short descriptions below each of them implies a ranking from the most unequal society, depicted by the ‘Type A’ diagram to the most equal, ‘Type
final section of the paper.

Paddy is the dominant crop in the Kharif season and the largest crop across all three seasons in terms of the cropped area. All farmers in our sample grow Kharif paddy. Pulses, millets, groundnut, and maize are other common Kharif crops. Vegetables dominate the cropped area in the Rabi and the summer season. Potato, tomato, peas, onion, and cabbage are the most commonly grown Rabi crops while tomato, long beans, and squashes are common in Summer. Table A.14 in the appendix shows the cropping pattern of the farmers in our sample in all three seasons.

> 4 Data reported in this paragraph have been taken from the ‘Power for All Factsheet’ available at: <u>https://www.powerforall.org/application/files/9615/8470/4205/FS_Jharkhand_sets_to_improve_rural_electricity_service_and_pr ovide_green_jobs_with_renewables7.pdf</u>

13
_Sources_ : Original estimations based on migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the destination composition of emigrants for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migration stock relative to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) was at its lowest level since 1960. Each lightly colored lines represents the trajectory for a particular country to low-return (neighboring or low-income countries) or high-return (high-income countries) destinations. The solid lines show the locally linear fit between increases in incomes and changes in emigration rates to low-return or high-return destinations compared to the baseline year. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middle-income countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

The fact that emigration from low-income origin countries to high-income destination countries increases very slowly with development — both relative to middle-income countries as well as in absolute magnitudes — suggests very high costs of emigrating to these destinations. Much larger income gains are required for many people in those countries to be able to afford migration to high-income destinations.

# **Increased global integration**

Economic development in recent decades has been associated with greater integration with the global economy. Such integration could take the form of increased trade (figure 4) and/or increased capital through foreign direct investment (FDI
>(per year, %)|ng access||Population gaining access<br>(per year, %)|
||1999–2003|2003–07||1999–2003<br>2003–07|
|Piped water|0.7|(0.2)|Flush toilets|0.3<br>0.7|
|Stand posts|1.4|1.6|Improved latrines|4.4<br>1.7|
|Wells/boreholes|(0.8)|4.7|Traditional latrines|(2.2)<br>1.9|
|Surface water|2.3|(2.2)|Open defecation|1.0<br>(0.1)|

_Source:_ WHO Joint Monitoring Program 2010 from Demographic and Health Surveys for 1999 and 2003 and Questionnaire à Indicateur de Base du Bien-Etre for 2007.

Extending access to higher-end options, which in the case of water means piped water and stand posts, results in high historical costs. Despite this, ONEA has very successfully achieved cost recovery— but as a consequence, Burkina’s tariffs are among the highest in the continent (figure 16). For additional extensions of utility water, while maintaining cost-recovery policies, costs will need to be either controlled or reduced.

> 3 WSP (2010) discusses the differences among norms in detail.

26
J.‐J., Hirn, M. and Straub, S. (2011) ‘Explaining enterprise performance in developing countries with business climate survey data’, _The World Bank Research Observer_ , 26(2), pp. 258–309.

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Scarce water<br>resources<br>Ot|her developing<br>regions|
||**2004**|**2008**|||
|Residential tariff (at 10 m<sup>3</sup>)|**6.4**||60.26|3.0 – 60.0|
|Non-residential tariff (at 100 m<sup>3</sup>)|**107.2**||120.74||

* Based on DHS 1999 and MICS 2006.

Source: Banerjee and others 2009; Morella and others 2009, derived from AICD water and sanitation utilities database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.**

# **Challenges**

Despite SODECI’s strong historical record, in recent years its financial performance has deteriorated as the real value of the tariff has eroded. The 1987 concession contract provides for tariff revisions every five years. Yet the scheduled revision for 2001 was delayed until 2004 during the recent crisis period,, and as a result the hidden costs due to under-pricing rose substantially (table 10). Currency appreciation amplified the effect of the 2004 tariff hike, and in 2008 the average effective tariff was $0.89 per cubic meter, almost double the 2001 level, but still significantly short of the full costs, which had escalated to $1.17..As a result of these tariff adjustments, the hidden costs associated with under-pricing of SODECI’s services have fallen from around 0.4 percent of GDP in 2001 to less than 0.2 percent of GDP today, but still remain an issue for the sector (figure 8). As of 2001, SODECI’s hidden costs were 94 percent of revenues, comparable to Ghana’s water utility and among the worst in the region. By 2008, however, hidden costs had fallen to 55 percent of revenues and