rafmacalaba/datause-encoder-data huggingface dataset:
goats, sheep, pigs, oxen, and chickens. Some farmers owned additional animals such as camels, duck, guinea fowl, horses, bees and doves. The major livestock products sold were milk, beef, goat products, sheep products, eggs, wool and leather (see Appendix A). Other more minor livestock products include butter, cheese, honey, skins and manure.

Along with a large array of breeds,<sup>4</sup> the fragile market structure in Africa makes it difficult to obtain accurate prices of animals. In our study, we used the median prices for each animal in each district in order to make our prices as robust as possible. When median district prices were not available, we used provincial median prices. If there were no provincial median prices, we used the national median price. Generally the prices for animals in Egypt and South Africa were the highest (see Appendix B). By contrast, Cameroon, Egypt, and Ethiopia had the highest prices for animal products.

Part of the problem with prices in the survey was measuring consistent units. For example, some households reported ‘number of eggs sold’ while others reported ‘dozens of eggs sold’. Some households reported ‘kg of beef per year’ while others reported ‘kg of beef per week’. By cleaning the data carefully, with the help of each country team, we were able to correct many observations but had to drop several cases that were unresolved.

The temperature data came from satellites which measure temperatures twice daily via a Special Sensor Microwave Imager mounted on US Defense Department satellites (Basist et al. 1998). The precipitation data was interpolated from weather stations throughout Africa by the National Oceanic and Atmospheric Administration (World Bank 2003). In both cases we used long-term measures of climate, not weather. For the satellite data we used the average temperature from 1988 through 2004. For the weather station data we used the average precipitation for 1960– 1990 (Mendelsohn et al. 2006).

Soil data were obtained from the FAO digital soil map of the world CD ROM and extrapolated to the district level using a GIS (Geographical Information System). The dataset reports 116 dominant soil types, which we explore in the analysis.

> 4 See _Breeds of livestock_ , Department of Animal Science, Oklahoma State University (www.ansi.okstate.edu/breeds/).

10
# **Appendix A: Data Construction**

## **A.1  Construction of Key Explanatory Variables**

**<u>Exporters</u>** were identified for 1993-2000 based upon reporting that the share of output exported was nonzero, and for 2001-2006 based upon a variable identifying whether _any_ output was exported (both variables are not available in the data for all years).

**<u>Firm age</u>** was constructed using the difference between the survey year and the year the firm reported the start of production.

**<u>Foreign owned firms</u>** <u>based upon whether foreign ownership was reported to be non-missing and non-zero.</u>

**<u>Government owned firms</u>** were defined the same way based on both local and national government.

**<u>Minimum wages</u>** <u>. Minimum wages were obtained from BPS as monthly provincial minimum wages set by</u> each province (or averages where there is within-province variation across districts) for each year. These reflect the nominal rupiah amount that all formal sector workers are required to be paid at or above. To obtain minimum wages in real terms, we deflated provincial minimum wages by the corresponding provincial CPI obtained from BPS. We thank David Newhouse for making these data available to us.

**-** **<u>Wages (firm level average)</u>** Wages are defined as the average wage for production or non-production workers, constructed as the total wage bill for either group divided by the number of workers of either respective group. The total wage bill for production (non-production) workers was defined as the sum of cash wages/salary and in-kind benefits for production (non-production workers) deflated to 1993 rupiah using the national consumer price index obtained from the World Development Indicators. Real wages are constructed by deflating the nominal wage bill reported to BPS by provincial CPI obtained from BPS.

40
government policy and public finances. Consistent with this, about three-quarters of respondents expressed concern about the fiscal situation in their country, and most people who answered the question “Have you ever read the report of the fiscal rules monitoring body in your country?” (90 respondents skipped the question) said “yes.” Of those who said they had not read the report, only 21 percent said the reason was a lack of interest.

**Figure 2. Respondents to Survey on Fiscal Rules in the Western Balkans**

<!-- Start of picture text --> Affiliation Country of residence<br>Media, 21 Albania, 19<br>Other, 40 Bosnia and<br>Herzegovina,<br>46<br>Kosovo, 26<br>Academia, 28<br>Private sector, Montenegro,<br>40 31<br>Serbia, 40<br>Public sector,<br>68<br>Macedonia,<br>FYR, 35<br><!-- End of picture text -->

# **Most respondents said they understood the fiscal rules in place in their country, though the**

**answers also suggest some misunderstandings** . Many respondents knew what a fiscal rule was and were able to identify some or all of the fiscal rules in place in their countries. In Albania, the debt-reduction rule was sufficiently well-known to be identified by two-thirds of the respondents, and in Kosovo a small majority were aware of both the debt rule and the deficit rule. But several respondents did not distinguish fiscal rules from annual budget limits or medium-term fiscal targets (Figure 3). Consistent with this, most North Macedonian respondents thought their country did have a fiscal rule, whereas by the definition used in this paper it does not. And in many countries, the fiscal rules did not seem to be very well understood even among the presumably relatively well-informed respondents. In Serbia, most respondents (incorrectly) thought the country had a debt-reduction rule along the lines of Albania’s—though a large majority were aware of the fiscal council’s role in monitoring compliance with fiscal rules, and most respondents said that they had read the survey. Overall, the results are consistent with the view that public awareness of fiscal rules is only moderate, and that in some countries the rules are too complicated to be understood well.

17
WPS3123

**PRODUCTION AND COST FUNCTIONS AND THEIR APPLICATION TO THE PORT SECTOR**

**A LITERATURE SURVEY**

# **Beatriz Tovar*, Sergio Jara-Díaz** and Lourdes Trujillo***

# **Abstract**

Seaports provide multiple services to ships, cargo, and passengers. These services can be performed by a combination of public and private initiatives. Usually, the role of public sector institutions is to regulate and supervise private firms. In performing that task public sector institutions need to know firms’ cost structure deeply. This paper offers a review of the literature about ports’ cost structure and of its implications for regulation. The paper argues that the operation of port terminals should be analysed by means of multiproduct theory. This approach allows the calculation of several cost indicators (economies of scale, scope, and so forth) which are key tools to help regulators.

**Key words:** multiproduct, economies of scale and scope, port and cargo handling.

JEL Classification system: L9

# World Bank Policy Research Working Paper 3123, August 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._

> * Universidad de Las Palmas de Gran Canaria, Spain. ** Universidad de Chile.
**<u>Vignette 2</u>**

|(1)|(2)<br>PCE +<br>household||
|---|---|---|
|PCE|characteristics||
|Coef.<br>se|Coef.|se|
|Log expenditure per capita<br>0.057<br>0.036|0.047|0.059|
|_Household Demographics_|||
|Female headed household|-0.024|0.056|
|Age of household head<br>|0.009<br>|0.011<br>|
|Age of household head squared|-0.000|0.000|
|Household size|-0.018|0.014|
|Number of children|0.023|0.019|
|Number of older adults|0.042|0.053|
|Number of migrants|0.037|0.031|
|_Ethnicity (Reference: Tajik)_|||
|Uzbek|0.006|0.050|
|Russian|0.063|0.165|
|Other|-0.072|0.147|
|_Education (Reference: No Education)_<br>|||
|Primary|0.093|0.113|
|Basic|0.154|0.108|
|General Secondary|0.182|0.116|
|Special Secondary|0.216*|0.119|
|Technical Secondary|0.152|0.116|
|Higher Education|-0.330|0.596|
|Graduate School|-0.024|0.056|
|_Employment Characteristics of Household, Head’s Occu_|_pation_||
|<br>_(Reference for Occupation: Unemployed)_|||
|Number of employed|0.041**|0.020|
|Agriculture / Fishing / Forestry|-0.032|0.070|
|Manufacture / Mining|-0.051|0.094|
|Services|0.223|0.139|
|Construction|0.110|0.087|
|Public Administration / Education / Health|0.021|-0.017|
|Sales and Services|0.000|0.067|
|Other|-0.113|0.111|
|_Agriculture (Reference for holdings: No Land)_|||
|<br>Small Holding|-0.164**|0.066|
|Medium Holding|-0.137|0.084|
|Large Holding|-0.124|0.084|
|Anylivestock, poultry, etc|0.013|0.059|
|_Geography (Reference: Dushanbe)_|||
|<br>Sogd Urban|0.232***|0.077|
|<br>Sogd Rural|0.164**|0.083|
|<br>Khatlon Urban|0.743***|0.110|
|Khatlon Rural|0.263***|0.081|
|RRP Urban|0.070|0.105|
|RRP Rural|0.067|0.077|
|Gbao Urban|-0.269*|0.139|
|Gbao Rural|0.175**|0.085|
|Number of observations<br>4,771|4,771||
|Pseudo R2<br>0.000|0.018||

32
# Measuring Inequality from Top to Bottom

## Tania Diaz-Bazan

World Bank<sup>_∗_</sup>

> _∗_ I am grateful to my thesis advisor Alberto Porto for early discussions on the topic, and for his constant and helpful supervision in developing the idea. I am also grateful to Stefano Giglio for his thoughtful suggestions and to Louise Cord, Lea Gimenez Duarte, Carlos Rodriguez Castelan, and Wendy Cunningham for many helpful discussions. Email: tdiazbazan@worldbank.org and bdiaztania@gmail.com.
there is a need to improving women access to private business, and access to and control over financial services including banking, and business development. It is very important to establish small and medium enterprises and moving into product development” (Elsheikh and Elamin 2016).

Unlike Sudan’s two different systems of property rights, each of which legally precludes women’s participation, Sudan’s banking system does not legally preclude women’s participation.<sup>18</sup> Rather, as Kevane and Stiansen (2006) note, “The mechanisms of exclusion are subtle or implied since they are not the result of specific legislation. Examples of cultural practices that prevent women from gaining access to formal financial institutions are the predominant role of males in the household, inheritance rules that favor male family members, and the gendering of economic spheres.” This is reflected in the data.

While the share of women and youth with bank accounts increased from 2011 to 2014, men are still twice as likely to have an account compared to women. A lower share of the Sudanese population has an account at a financial institution, compared to the average for Sub-Saharan Africa (15 and 34 percent, respectively) (Global Findex 2014). Both women and youth shares for having an account at a bank or a financial institution or for using a mobile money service increased by 6 percentage points from 2011 to 2014. Men, adults, individuals in the labor force, and the better-off shares have increased significantly compared to other categories. Nearly half of those who reported having an account borrowed to start, operate, or expand a farm or business. Only one in three women who reported having an account borrowed to start a business, compared to half of the men. About 40 percent of the young people who have accounts borrowed to expand or start a business, compared to 47 percent for adults (Figure 15).

> 18 The first modern bank established in Sudan was Barclays, a British bank, in 1913. For the remainder of Sudan’s colonial period, that is, until 1956, Sudan’s banking system was a ‘branch’ of Egypt’s banking system. Following independence, Sudan’s Egyptian National Bank first became the Bank of Sudan and then subsequently became the Central Bank of Sudan. The newly independent country also established commercial banks as well as some specialty banks, such as the Agricultural Bank of Sudan. Sudan briefly nationalized its banks in the early 1970s. Then, it introduced its first Islamic bank, Faisal Islamic Bank, in 1978. As mentioned earlier, in 1983, Sudan adopted Sharia law. The following year, all Sudanese banks were obliged to adopt Islamic banking practices (Kevane and Stiansen 2006; Ismail 2019).

38
digit disaggregated level of the Statistical Classification of Economic Activities in the European Union (NACE); and (2) by occupational categories up to the three-digit level of the International Standard Classification of Occupations (ISCO-08). Because the ISCO classification scheme changed significantly between 2010 and 2011,<sup>7</sup> we restrict our final sample to the period from 2011 to 2018. In addition, the dataset includes information on the wage decile of the worker in the national wage distribution for individuals who are wage employees.<sup>8</sup> Finally, the dataset provides information for each observation within destination countries at a subnational, regional level: the European Commission’s second level of nomenclature of territorial units for statistics (NUTS2). This allows us to measure immigrants’ presence and labor market outcomes at a rather disaggregated geographical level, and to conduct our estimation at the regional level within European countries. For our analysis, we restrict the sample to the 124 NUTS2 regions in Western Europe, which includes the EU15 countries, Switzerland, and Norway. The share of migrants in the New Member States (NMS13) countries is very small, so the sample captured in the EU Labour Force Survey (EU-LFS) can be noisy.

# 3.2. Measuring Economic and Health Exposure to COVID-19 and Correlation with the Task Content of Occupations

Labor-force surveys are not yet available to provide post-COVID-19, harmonized information of labor-market outcomes for the European Union, at the sub-region level. We categorize economic exposure to COVID-19 using information on workers’ occupations and labor market outcomes _prior_ to the pandemic. This approach reflects the method used in the literature (e.g., Dingel and Neiman, 2020; Garrote Sanchez et. al., 2020) to measure risk exposure before an event takes place. We observe the detailed occupational categories in the data, including the three-digit ISCO-08 categories, which we use to estimate the economic exposure of different types of workers to COVID-19.

> 7 The scheme changed from ISCO-88 to ISCO-08. 8 The dataset does not include information on the exact wage of individuals.

9
<!-- Start of picture text --> consumption is more muted. On impact, output increases by merely 0.2 cents<br>for every dollar spent by the government, implying that more than 80% of<br>government expenditure is crowded out by other components of GDP (con-<br>sumption, investment or net exports). This estimate is statistically different<br>from zero, but is lower by a statistically significant margin from our estimate<br>of the fiscal multiplier in high-income countries. In the long run, govern-<br>ment consumption appears to be almost fully crowded out, with the long run<br>multiplier for developing countries estimated at 0.15. This estimate is not<br>different from zero by a statistically significant margin, but can be said to be<br>lower than 1 with 99% confidence.<br>Long-run estimates of the government expenditure multiplier are similar<br>to those found in IMV, but the impact multipliers in both these groups of<br>countries are larger in this study. There are different reasons for these diver-<br>gent results in the case of high-income countries and in the case of develop-<br>ing countries. In high-income countries, the current sample is significantly<br>smaller than that used in IMV, with only 6 of the 19 countries from IMV in-<br>cluded in this sample. A bivariate regression with government consumption<br>and GDP as in IMV yields similar results as those reported here, when only<br>including the six countries that are in both studies.<br>While the sample of developing countries is not perfectly overlapping with<br>that of IMV (only 17 of the 20 countries in IMV are included here), this is<br>not the main cause for the difference in the estimates of fiscal multipliers.<br>The exclusion of tax revenues from the regression in IMV may have under-<br>stated the short-run impact of government consumption on GDP (and lead<br>to estimation of a negative impact multiplier). In fact, as shown in Figure<br>11, taxes are found to rise in response to government consumption increases,<br>which may bias results of estimations that exclude taxes.<br>Turning to the effects of taxes on output, Figure 5 re-affirms results seen<br>18<br><!-- End of picture text -->
12 -

-

preferences for certain firms and industries, and compulsory military service. Finally, rent-seeking activities grow around the military because of its non-competitive allocation of resources. In this way, _over and above_ their depressing effect on the level of investment, military expenditures may exert a direct adverse impact on the economy's productive efficiency.

These considerations suggest that the net effect of a rise in military expenditure on a country's growth rate and its steady state level of capacity output is likely to be negative. Therefore, one would expect to find evidence of this negative impact in longer-run economic data both across countries and over time. However, it is obviously difficult to disentangle empirically the potential positive short-run effect of the demand stimulus associated with an increase in military spending from the depressing effect of high military spending on the longer-run growth path of capacity output, particularly if the estimation work fails to exploit both the time-series and cross-section dimensions of the data. The striking ambiguity of past econometric results in the face of strong anecdotal evidence on the long-run economic benefits of lowering military expenditure suggests that weaknesses in the econometric techniques used to test these hypotheses may be a problem.

Thus it is not surprising that a number of past attempts to subject the relation between military spending and growth relationship to empirical testing--Benoit (1973, 1978) and Frederiksen and Looney (1982)--seem to have uncovered empirical support for the thesis that military expenditures were not detrimental to growth. Benoit (1978), using data for 44 developing countries over 1950-65, finds a positive association between military
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49