rafmacalaba/datause-encoder-data huggingface dataset:
than 40 percent of exports between 2000 and 2005. A second category is countries in conflict, where armed conflicts affect food production and markets. These countries have great food needs but solutions to their problems lie beyond reforms in global trade regimes or agricultural policies; their food requirements and its distribution within the conflict areas require other support mechanisms. A third category is the small island states. They usually sell services and import most of their needs, including some food. Although their numbers are large, their trade is very small, along with their populations. Thus, in addition to the differentiation by income as middle and low income, three other subcategories were added to the developing countries classification; small island economies, oil exporters, and countries in conflict. Annex table 2 shows the classification of countries in each group.

In the following tables, we analyze the changes in net food imports, and see what has happened to the vulnerability of different groups of countries between 2000/01 and 2006/07. Here we use 2 separate definitions of vulnerability, in terms of external trade and as a part of their income. Table 14 shows the number of countries that are net food exporters and importers and the changes in them over this period.

**Table 14: Country Classification by Food Trade in 2000/01-2006/07**

||No. of Ne|t Exporters|No. of Net|Importers|Total No. of|
|---|---|---|---|---|---|
|CountryGroup|2000-01|2006-07|2000-01|2006-07|Countries|
|Industrial Countries|11|12|22|21|33|
|Middle Income, all|31|37|66|60|97|
|Oil Exporters|3|3|17|17|20|
|Civil Conflict States|0|1|4|3|4|
|Small Islanders|5|5|18|18|23|
|Other Middle income|23|28|27|22|50|
|Low Income, all|19|13|36|42|55|
|Oil Exporters|2|1|5|6|7|
|Civil Conflict States|0|0|7|7|7|
|Other Low income|17|12|24|29|41|
|Total|61|62|123|122|185|

Notes: Food is defined as raw food products, including meats and dairy products, grains and cereals, fruits and vegetables, but excluding cash crops and feeds, processed food, and seafood. The classification of income group is based on 2007 per capita GNI from World Bank WDI. Countries are excluding those small island economies that are colonized and dependent territories. Data is computed as two-year average of trade values in SITC Revision 3.

Source: Based on mirror data from UN COMTRADE Statistics.

24
This paper addresses the information gap by developing and applying an algorithm that generates species maps directly from GBIF occurrence data. The algorithm can automatically update maps as new occurrence data become available. We generate maps for all species whose data currently satisfy our computational criteria (over 600,000). Overlaying these maps with a high-resolution grid provides a view of global biodiversity that broadens the traditional focus on vertebrate animals to include more representation for invertebrates, other animals, plants, fungi and other non-plant and non-animal species.

We believe that the new maps are complements rather than substitutes for in-depth biodiversity assessments. Where more information is available, the policy process will undoubtedly benefit from incorporating it. At the same time, we believe that our approach can contribute in three ways. First, for currently-mapped species, fast updates using our algorithm can help identify cases where newly-reported occurrences suggest alteration of map boundaries. Second, for currently-unmapped species, our approach can provide useful new information for global biodiversity assessments. Third, our mapping exercise can yield new insights about the global distribution of endemic species and species that are particularly vulnerable to human encroachment.

The remainder ofthe paper<sup>isorganizedasfollows.Section2describestheGBIFoccurrencedata</sup> and<sup>the</sup> application of our mapping algorithm. In Section 3, we explore overall results for the 610,694 species maps generated by our exercise. Section 4 extends the results to a new view of global endemism, while Section 5 explores the distributions of small-occurrence region species that are particularly vulnerable to human encroachment. Section 6 summarizes and concludes the paper.

The Global Biodiversity Information Facility provides a constantly-updated open-source repository of geolocated, date-stamped reports of species occurrences from many public institutions and NGOs. These In addition, the GBIF identified reporting locations.

Species are traditionally identified as endemic if they are 100% resident in one country. We also explore the implications of lowering the endemism condition to 95% and 90%. ~~a a,~~ respectively. Our mapping algorithm requires at least this many observations to work reliably. This drastically reduces the size of the download dataset, since some species have millions of occurrence records. Detailed descriptions of the protocols and database elements are provided by the GBIF at
of domestic consumption) in products previously subject to non-tariff barriers; (ii) reduce export subsidies by 36 percent in value and 21 percent in volume; and (iii) reduce trade distorting aggregate domestic subsidies by 20 percent. The obligations were differentiated according to level of development. Developing countries were allowed to offer "ceiling bindings" (i.e., arbitrarily chosen maximum tariff level), and were required to apply lower reduction obligations (two-thiirds of the level in industrial countries) to be implemented over a longer transition period (ten years). Subsidies for food security, as well as for investment, input, transport, and marketing were exempt from reductions. In addition, least-developed countries were exempt from reduction commitments.

In this paper, I argue that while significant reforms in the rules were achieved, the extent of liberalization and tile degree of reduction in agricultural protection will be signilicantly less tilan expected. Based on extensive examination of the country schedules of **linal** commitments and estimates ot pre- and post-UR ad-valorem tariff equivalents of border measures, the analysis indicates that the specific and ad-valorem tariffs equivalents which many counlries hiave set in ilheir Schedules are significantly higiler than the wedge between actual domiiestic and world prices in the base period, hience providing highler protection than was prevaiiling in 196-88. Thus. the extent ol' liberalization as a result ol' the Round was ero(led since the post-UR taril'l' equivalents _(aid-voloreni)_ in major commondities were set at levels higiler than the border protection they replaced. More importantly. the wide occurrence of "dirty" tarit'lication in the OECD countries resulted in very little or no liberalization in major agricultural coimmoidities. Where larill' reductions would have had the most si-nilicant effect on traide. counitries opted lor **minimum** reductions. Relative to average protection levels in recent years and **tile** average during the last ten to 1if'teen years. the post-UR base and tinal laril'fequivalents and hindings **al-c** significantly higher in most countries.

52
29 _Growth(i,k)_ is the average annual growth rate of value added in industry _k_ and country _i_ , over the period 1980-90. _C_ and _I_ are matrices of country and industry dummies for all countries and industries, respectively. _Share(i,k)_ is the share of industry _k_ in manufacturing in country _i_ in 1980. _External(k)_ is the fraction of capital expenditures not financed with internal funds for U.S. firms in the industry k between 1980-90. _FD(i)_ is an indicator of financial development for country _i_ . RZ interact the external dependence of an industry ( _External_ ) with financial development ( _FD_ ), where the estimated coefficient on the interaction, d, is the focus of their analysis. Thus, if d is significant and positive, then this implies that an increase in financial development ( _FD_ ) will induce a bigger impact on industrial growth _(Growth(i,k)_ ) if this industry relies heavily on external finance ( _External(k)_ ) than if this industry is not a naturally heavy user of external finance.

This approach allows RZ (1) to study a particular mechanism, external finance, through which finance operates rather than simply assessing links between finance and growth and (2) to exploit within-country differences concerning industries.

RZ use data on 36 industries across 41 countries. To measure financial development, RZ examine (a) total capitalization, which equals the summation of stock market capitalization and domestic credit as a share of GDP and (b) accounting standards. As RZ discuss, there are problems with these measures. Stock market capitalization does not capture the actual amount of capital raised in equity markets. RZ use the accounting standards measure as a positive signal of the ease with which firms can raise external funds, while noting that it is not a direct measure external financing. Beck and Levine (2000) confirm the RZ findings using alternative measures of financial development.
when explaining the region’s high inequality or low productivity. It also requires that policymakers and academics recognize that the emphasis on superstar firms currently dominating the inequality debate in the developed world directs the spotlight away from the segment of the economy where the incomes of a vast majority of Latin Americans originate.

In terms of the business size distribution, our work is closest to Poschke’s (2018), which departs from the use of firm-level datasets by using information at the entrepreneur level from the Global Entrepreneurship Monitor and Amadeus. Our use of the official employment/household surveys, the most comprehensive data sources on workforces at the national level, permits a full coverage of businesses of all sizes and motivations –including survival entrepreneurship lacking employment generation potential–. It also enables us to tie business size to the earnings of individuals associated with different-sized businesses. The prevalence of self-employment without employees as a defining characteristic of less developed economies, and its relationship with survival –rather than transformational– entrepreneurship, has been documented, among others, by Schultz (1990); Pietrobelli et al. (2004); Gindling and Newhouse (2014). Our analysis merges that fact into a more comprehensive view of the business landscape in LDCs versus AEs, and of how its characteristics are reflected in individual incomes.

The paper proceeds as follows. Section 2 presents the data. Section 3 shows our results, first characterizing the overall business size distribution (Section 3.1.) and then the relationship between personal earnings and the size of businesses where those earnings originate (Section 3.2.). The implications of our results are discussed in Section 4. Throughout the paper, we maintain a comparative perspective, contrasting LATAM with European economies, the US, and some Asian economies, including both those more and less advanced. The sample of comparators is dictated by data availability.

# **2 Data**

The use of comprehensive data on the productive activities where individuals obtain their income, either as self-employed or as salaried workers or firm owners, is crucial to our research question. For this reason, we rely on microdata from the surveys used in different countries to generate official labor market statistics, which we refer to as ”employment surveys” even though in some countries they are labeled as household surveys. Given their nature and purpose, they are designed to be representative of the entire labor force in each country. Beyond collecting information on the work status and income of each adult individual in the household, the surveys frequently ask for the size, in number of workers, of the business where the person works or, in the case of employers, the number of people they employ.

These data are crucial for questions on how attributes of the business sector impact development or inequality. Their unparalleled strength is their comprehensiveness in covering all 4
- 13 -

III. Bergsten and Cline (1985) 21. Following Chenery (1960), Bergsten and Cline (1985), using data for eleven industrial countries plus the EEC as a region for the years 1974-1984, estimated the ratio of imports of goods and nonfactor services to GNP as a function of income, population, natural resource endowments, and transport costs incurred in importing goods from trading partners. In this exercise, all EC member countries were grouped as one region, and only the imports from outside the region were taken as EC imports. Data on imports of goods and nonfactor services and on CNP were both in national currencies. GNP data were in real terms at 1974 dollars as adjusted for cross country differences in purchasing power. Based on the regression analysis using these data, Bergsten and Cline concluded that the ratio of imports to CNP for Japan was explainable by the size of GUP, the natural resource endowments and the transport costs involved in trading and that there was nothing abnormal in Japan's import behavior. 1/ 22. Several issues were raised with the study by Bergsten/Cline (1985). First, the use of per capita income instead of CNP itself is preferable. Second, combining all EC member countries into one "country", 1/ The regression results obtained were as follows: Z = 0.7731 - 0.0628 log Y + 0.0100 log P - 0.0378 log L (28.4) (26.2) (7.57) (7.25) + 0.0414 I - 0.00069 T e 0.0082 Dj, R2 = 0.8950, (3.96) (9.7) (0.47) where Z is the ratio of imports of goods and nonfactor services to CNP, Y is CUP its real terms at 1974 dollars, as adjusted for cross-country purchasing power differences, P is crude oil production per capita, L is arable land per capita, I is a dummy variable for presence of significant iron ore reserves (1 if present, zero if not), T is the index of transportation costs discussed above, D. is a dummy variable for Japan (1 for Japan, zero otherwise), and log is ihe natural logarithm; t- statistics are in parentheses. This equation is from annual data for 1974 through 1984, for the pool of 11 countries plus the EEC, giving 132 observations. See Bergsten and Cline (1985), p. 77.
**Table 7: Results Exploiting Variation over Time, Across Products, and Across Origin/Destination**

|**Vars**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|Lagged Predicted Log Tourist|0.277|0.187||-0.254|-2.418*|
||(0.301)|(0.599)||(0.239)|(1.237)|
|Lagged Predicted Log Tourist*Touristic Export<br>Products||||||
||1.494***|1.380**||0.713**|2.989**|
||(0.252)|(0.489)||(0.291)|(1.237)|
|Log World Imports of the Same Product|0.456***|0.423***|0.286**|0.246***|0.255***|
||(0.0735)|(0.0831)|(0.112)|||
|Log Real Exchange Rate|-0.32***|-0.32***|0.148|||
||(0.0864)|(0.102)|(0.0849)|||
|Lagged Log Expenditure per day Tourists||0.0782|-2.10***|||
|||(1.185)|(0.342)|||
|Lagged Log Expenditure per day<br>Tourists*Touristic Export Products||||||
|||0.214|2.061***|||
|||(1.121)|(0.373)|||
|Lagged Actual Log Tourists|||-0.959|||
||||(0.657)|||
|Lagged Actual Log Tourists*Touristic Export<br>Products||||||
||||1.337|||
||||(0.742)|(0.0779)|(0.0792)|
|Constant|-19.1***|-15.8***|-2.912**|-4.341|-3.179|
||(2.394)|(3.863)|(1.041)|(2.282)|(2.580)|
|Year Dummies|No|No|No|Yes|Yes|
|Year Dummies*Touristic Product Dummy|No|No|No|No|Yes|
|Product - Tourism Origin Fixed Effect|Yes|Yes|Yes|Yes|Yes|
|Observations|2,465|2,430|1,579|2,465|2,465|
|R-squared|0.161|0.161|0.048|0.212|0.223|
|Number of Product-Origin|204|204|197|204|204|
|F-test (P-value) Lagged Pr.Log Tourists +<br>Lagged Pr Log Tourists*T=0|||||0.024|

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

Finally, the fifth column introduces a set of time dummies interacted with the touristic product dummy to control for possible macro shocks that disproportionately affect exports of touristic goods and the 18
Table 5: Husband Characteristics

|Dependent variabl|e = 1(Ever Bee|n Beaten)|||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Work|0.374*|0.111**|-0.075|-0.078|
||[0.193]|[0.046]|[0.060]|[0.060]|
|Age First Marriage|-0.008||||
||[0.006]||||
|Work X Age First Marriage|-0.024*||||
||[0.012]||||
|Age Difference Husband-Wife|0.000<br>[0.003]||||
|Work X Age Difference|0.006<br>[0.006]||||
|Education||-0.017***<br>[0.005]|||
|Work X Education||-0.021**<br>[0.009]|||
|Education Difference Husband-Wife||-0.006<br>[0.005]|||
|Work X Education Difference Husband-Wife||0.002<br>[0.008]|||
|Husband Work|||-0.028|-0.031|
||||[0.034]|[0.034]|
|Work X Husband Work|||0.108*|0.060|
||||[0.063]|[0.081]|
|Work X Husband Work||||0.056|
|X Relative Wage Wife/Husband||||[0.064]|
|Observations|1,320|1,215|1,215|1,215|

_Standard errors in brackets, clustered at the level of the village. *** p<0.01, ** p<0.05, * p<0.1. Columns 2 and 3 lose sample size because the education variable is unavailable if husband was away from hh at the time of the survey and thus not classified as a hh member and surveyed_
# _Indirect Costs_

The _indirect_ costs of health externalities of DDT, on the other hand, depend on the valuation of the burden of the disease; and can be assessed using a number of epidemiological parameters such as incidence, prevalence, disease specific mortality, and disability caused by the disease. In this analysis, the burden of disease was estimated based on disability adjusted life years (DALY) approach.<sup>19</sup> DALY is a widely used approach to estimate the consequences of a disease over the lifetime of the patient and combines the effects of both increased morbidity (years of life lived with disease) and mortality (years of life lost due to premature mortality from the disease) into one composite index in terms of years of useful life lost. The DALYs for various disease categories by gender and age categories are published periodically by the WHO in different regions of the world as a measure of global burden of the disease.

The identification of the population facing exposure to DDT and the estimation of increased incidence of disease resulting from such exposure for the indirect cost estimation were done in the same way as discussed in the section on direct cost estimations. Since the DALY measures for various disease categories are available by age categories and gender, the increased incidence of the disease was also developed using the population ratios in each age and gender category. The extra DALY due to the increased incidence of a specific disease resulting from DDT was then determined using the DALY rates in each of the age and gender groups. This process of computation was repeated for each disease category separately as the DALY rates vary across each disease. The increased DALYs from each disease category for a country were then added to calculate the total probable DALY loss due to the exposure to DDT in that country. The DALY estimates for each disease category, which are reported in the WHO global

> 19 In the literature, a number of alternative approaches have been used to assess burden of disease ranging from the Cost of incidence (COI), Human capital approach, Willingness to pay (WTP), Value of Statistical Life (VSL), Quality-adjusted life year (QALY) and DALY.

13
|Security costs (% of sales)|-0.011***|-0.019***|
|---|---|---|
||(0.004)|(0.006)|
|Establishment competes against unregistered or informal firms Y/N|-0.051|-0.127***|
||(0.032)|(0.043)|
|Number of years that firm was unregistered at start|-0.006**|-0.007**|
||(0.003)|(0.004)|
|Formal training Y/N|0.049|0.138***|
||(0.031)|(0.038)|
|lnLlnK|-0.008***||
||(0.001)||
|lnLlnM|-0.035***||
||(0.001)||
|lnKlnM|0.002**||
||(0.001)||
|lnL<sup>2</sup>|0.042***||
||(0.002)||
|lnM<sup>2</sup>|0.032***||
||(0.001)||
|lnK<sup>2</sup>|0.005***||
||(0.001)||
|Constant|3.994***|6.045***|
||(0.140)|(0.335)|
|Region (within country) Fixed Effects|Yes|Yes|
|Sector (2 digit) Fixed Effects|Yes|Yes|
|Number of observations|16,847|16,847|

Note: Survey weights have been applied to all regressions, using Stata’s svy prefix. Linearized Taylor standard errors that account for survey stratification are indicated in parentheses. *** p<0.01, ** p<0.05, * p<0.1

# **Table 4: Water shortages and Sales by Income Group**

|**Y = Log of sales**|**High and Upper Middle**<br>**Income**<br>**(1)**|**Low and Lower Middle**<br>**Income**<br>**(2)**|
|---|---|---|
|Log of labor (lnL)|0.884***|0.751***|
||(0.126)|(0.026)|
|Log of capital (replacement value) (lnK)|-0.029|0.023**|
||(0.066)|(0.010)|
|Log of raw materials (costs) (lnM)|0.177***|0.242***|
||(0.061)|(0.012)|
|No of water shortages per day in a typical month|-0.225|-0.082**|
||(0.154)|(0.036)|
|Average duration of water shortage|-0.001|0.004|

25
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: http://www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models to the models page (http://www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). 2010. ECOWAS’s Infrastructure: A Regional Perspective. http://www.infrastructureafrica.org.

- Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2009. ―Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.‖ AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank. http://www.infrastructureafrica.org

- Measure DHS. 2006. _Demographic and Health Survey Niger, 2006_ . http://www.measuredhs.com.

- OECD (Organisation for Economic Co-operation and Development). 2007. _Niger Country Profile. African Economic Outlook_ . Paris.

- World Bank. 2007a. _Implementation Completion and Results Report (IDA-31300 IDA-3130A) on a Credit in the Amount of SDR 14.0 Million ($ 18.6 million equivalent) to the Republic of Niger for a Privatization and Regulatory Reform Technical Assistance Project_ . Report No: ICR000042, World Bank, Washington, June 29.

- ———. 2008a. _International Development Association Country Assistance Strategy for the Republic of Niger for the period FYOS–FY11_ . Report No. 43443-NE, World Bank, Washington, DC, May 2.

- ———. 2008b. _Doing Business: Country Profile for Niger._ Washington, DC: World Bank.

- ———. 2009a. _Niger: Putting the Economy on a Path of Higher Growth. Series IDA at Work._ Washington, DC: World Bank.

- ———. 2009b. _Enterprise Survey_ . Washington, DC: World Bank.

- ———. 2011. _Doing Business 2011: Making a Difference for Entrepreneurs_ . Washington, DC.

44