rafmacalaba/datause-encoder-data huggingface dataset:
While the linear regression model has often been applied to count outcomes, this can result in inefficient, inconsistent, and biased estimates. Moreover, count distributions are rarely statistically normal. Even though there are situations in which the linear models provide reasonable results, findings can be more robust with models specifically designed for count outcomes.

**Figure 5: Frequency of WATSAN PSP project counts**

<!-- Start of picture text --> 30 120<br>25 100<br>20 80<br>15 60<br>10 40<br>5 20<br>0 0<br>0 1 2 3 4 7 8 11 12 13 14 15 16 19 22 23 27 42 166<br>Observed Counts Cumulative Frequency<br><!-- End of picture text --> The univariate Poisson distribution is the foundation of regression models for counts. The Poisson regression model (PRM) extends the Poisson distribution by allowing each observation to have a different value of _μ_ , the unique value of the mean and variance of the Poisson distribution. In practice the PRM rarely fits due to _over-dispersion_ . That is, the model underestimates the amount of dispersion in the outcome. The negative binomial regression model (NBRM) addresses the failure of the PRM by adding a parameter _α_ that reflects _unobserved_ heterogeneity among observations.

The PRM and the NBRM have the same mean structure. That is, if the assumptions of the NBRM are correct, the expected rate for a given level of the independent variables will be the same in both models. However, the standard errors in the PRM will be biased downward, resulting in spuriously large _z_ -values and spuriously small _p_ -values (Cameron and Trivedi 1998). The NBRM thus appears preferable to the PRM. See Annex D for a full analysis of the regression of our dataset comparing the results of the PRM and of the NBRM, and Long and Freese (2001) for more details about count outcome regression models.

<u>We estimate our basic model (without the institutional variables) with the NBRM using STATA 8.2.</u>

|**Negative binomia**|**l regression**||Numb|erofobs.=56|LRchi2(5)=|62.83|
|---|---|---|---|---|---|---|
|DV: Number of Si|gned PSP Con|tracts|Pseud|o R2 = 0.2036|Prob > chi2|= 0.0000|
||Coefficient|StdError|Z|P>|z||[95% Con|f.Interval]|
|Population|.5880215|.1261757|4.66|0.000|.3407216|.8353213|
|GDPperCapita|.9950203|.2148994|4.63|0.000|.5738251|1.416215|
|ICRGRisk|3.25384|1.808789|1.80|0.072|-.2913212|6.799002|
|Coverage Gap|.0000794|.0000312|2.54|0.011|.0000181|.0001406|
|Debt/ GNI|.5546001|.3261312|1.70|0.089|-.0846054|1.193806|
|Constant|-28.54979|6.39603|4.46|0.000|-41.08578|-16.01381|
|Likelihood-ratio te|st ofalpha=0:|chibar2(01)|=36.43|Prob>=chibar2|=0.000||

Figure 6 plots the observed and predicted frequencies using the NBRM-fitted model. We can see that the model fits the data correctly even though it tends to slightly over estimate the number of zeros<sup>21</sup> .

> 21 The PRM fitted with the same data tends to underestimate zero counts.

13
5 Table 4: Welfare Reduction Indexes, Asian, African, Latin American, Europe’s transition economies and high-income regions<sup>a</sup> , all covered tradable farm products, 1960 to 2007

||||(per|cent)|||||||
|---|---|---|---|---|---|---|---|---|---|---|
||1960-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|2005-07|
|**Import-competing products**|||||||||||
|Africa|59|52|53|47|51|98|43|32|30|na|
|Asia|36|45|46|50|48|62|48|44|48|na|
|Latin America|54|34|27|37|47|40|46|26|32|na|
|All developing countries|49|46|43|44|44|54|36|28|30|na|
|Europe’s transition economies|na|na|na|na|na|na|60|44|45|43|
|High-income countries|79|87|71|100|106|123|102|91|87|50|
|World|74|76|65|85|81|100|78|65|65|na|
|**Exportable products**|||||||||||
|Africa|37|44|48|49|48|55|58|41|40|na|
|Asia|24|43|34|34|48|45|24|10|7|na|
|Latin America|28|22|36|32|36|33|29|12|15|na|
|All developing countries|31|38|38|36|46|44|26|11|10|na|
|Europe’s transition economies|na|na|na|na|na|na|37|33|31|42|
|High-income countries|12|20|16|12|12|25|22|11|11|10|
|World|16|27|26|24|34|39|26|13|12|na|
|**All covered farm tradables**|||||||||||
|Africa|52|52|52|49|51|82|52|37|36|na|
|Asia|27|43|39|42|47|45|28|19|16|na|
|Latin America|43|25|38|36|44|39|42|20|22|na|
|All developing countries|44|44|42|42|47|47|31|19|18|na|
|Europe’s transition economies|na|na|na|na|na|na|47|40|40|44|
|High-income countries|49|48|46|64|69|70|51|38|37|22|
|World|48|47|45|55|57|57|41|28|27|na|

Source: Authors’ calculations based on product NRAs and CTEs in Anderson and Valenzuela (2008). a Regional aggregates are weighted using the average of the value of production and the value of consumption at undistorted prices.
Argentina is the long-term transformation of its workforce. In Gran Buenos Aires between 1980 and 2002, the fraction of all workers who are informal salaried workers more than doubled, from 15 to 32 percent, while the share of self-employed workers remained roughly constant at around 26 percent. The shift from formal to informal employment happened steadily over the whole period. Table 7 documents how this increase divides up between fulltime and part-time workers. Even though most of the increase happens among part-time workers, it is also substantial among full-time workers, which are the object of analysis in this paper.

World Bank (2007) documents the evolution of informal employment in Argentina, exploring possible explanations for the steady increase in rates of informal employment over time. Possible causes include macroeconomic policy and privatization, economic structure and demographic change, trade and technological change, labor regulations and institutions, as well as tax evasion, enforcement and weak public confidence. In a simply supply and demand framework, decreasing wages in light of increasing demand for informal workers could be a natural result of large increases in the supply of informal workers. The most likely candidate explanation for such changes in supply would be changes to the labor market induced by structural or demographic changes, such as an augmented entering of women into the labor force. However, in separate analyses, Gasparini (2002) and World Bank (2007) find that structural and demographic changes cannot explain the increase in levels of informal employment. World Bank (2007) also shows that changes in the minimum wage and unionization are very unlikely candidates to explain the decreasing informal-formal wage gap. Additionally, changes in trade patterns since 1980 can explain only a small portion of increases in the size of informal salaried employment (Goni and Maloney, 2007).

Several hypotheses imply that high levels of informal employment are fundamentally driven by increased demand for informal work arrangements. Such an explanation would be paired most naturally with increased relative wages in the informal compared to the formal sector. However the wages of informal and self-employed workers relative to formal workers have not increased consistently. Between 1980 and 1992, relative wages of informals were indeed rising. However, Table 9 shows that in the 1990s, the relative wages of informal workers fell substantially, by approximately 21 percentage points for men and 26 percentage points for right to receive a pension, which has been shown to be highly correlated with registration in the social security system. (World Bank 2007) 20
**Figure 3: Employment growth and informal competition relationship (Median values)**

<!-- Start of picture text --> Figure 3A<br>0<br>Low informal competition High informal competition<br>-0.2<br>(residuals; roughly below (residuals; roughly above<br>-0.4 median) median)<br>-0.6<br>-0.8<br>-1<br>-0.94<br>-1.2<br>-1.4<br>-1.35<br>-1.6<br>Figure 3B<br>0<br>Low informal competition High informal competition<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.5 percentile values) percentile values)<br>-1<br>-0.91<br>-1.5<br>-2<br>-1.93<br>-2.5<br>Employment growth rate (%,<br>residuals, group median values)<br>Employment growth rate (%,<br>residuals, group median values)<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data. Note: 1) Employment growth rate is calculated as the log difference in the level of employment (log of final year employment minus log of initial year employment) divided by the number of years between the initial and last year and multiplied by 100 to convert to percentage terms. The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against the residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment (logs) and (log of) GDP per capita. Informal competition variable equals the proportion of firms in the country (excluding own firm) that report competing against informal firms. 3) Group averages (median values) of the residuals are shown. Close to the median value of the informal competition residuals is used to categorize the two groups in Figure 3A. The cut-off value used for the two groups in Figure 3B is close to the 75<sup>th</sup> percentile value of informal competition residuals.

36
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE significant socioeconomic, health, and environmental benefits such as new income-generation opportunities for small businesses.

Given the capacity constraints faced in South Sudan, one possible approach to explore on the efficiency front would be the involvement of the private sector through a management contract with targeted incentives to improve some of the key efficiency parameters. In the Democratic Republic of Congo, for example, management contracts are being developed for the power and water utilities to address inefficiencies. A similar approach is being used for the Liberia Electricity Corporation.

In the medium to long term, tapping into the Eastern Africa Power Pool’s (EAPP’s) energy market might also provide energy solutions for South Sudan. Leveraging cheap hydropower produced in Ethiopia through interconnections either through Kenya and Uganda will provide access to greater amounts of lessexpensive hydropower for South Sudan.

In the long run, South Sudan could harness its massive hydropower capacity through the development of additional hydro-based generation. Exploiting the hydropower potential offers South Sudan three key benefits. First, in the long run the cost-recovery situation in South Sudan is more attainable (figure 19). Using a model that simulates optimal (least-cost) strategies for generating, transmitting, and distributing electricity in response to demand increases, it is estimated that the long-run marginal costs of producing power in Sudan would be about $0.13 per kWh, a substantial decrease from the existing $0.37 per kWh. The prevailing tariff will be able to recover long-run costs including capital costs.

**Figure 19. Existing tariffs for power are insufficient to recover huge operating costs and long-run marginal costs**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Existing total Existing Effective tariff Lon-run Average Existing capital<br>cost operating cost Marginal Cost revenue cost<br>US$ cents per Kwh<br><!-- End of picture text --> _Source:_ AICD calculations.

Second, developing South Sudan’s rich hydropower potential elevates South Sudan’s role as a key player in regional power trade. Expanding cross-border trade—trade expansion—to leverage lower-cost energy resources that are available in the region as a whole with the addition of cross-border transmission capacity to facilitate the flow of power from production to consumption locations. A counterfactual scenario—trade stagnation—assumes that no cross-border interconnectors will be built and countries will meet the incremental power demands through expansion of their domestic power sectors. South Sudan can boost its own energy supplies, reduce its reliance on oil-based generation, and wheel power to its East African neighbors through the development of over 1,500 MW of power generation and additional 33
Table A.8: Technology sophistication in Apparel - Design

||**Manual**<br>**design**<br>**and**<br>**hand**<br>**drawing**|**Digital or semi-digital de-**<br>**sign using specialized 2D**<br>**drawing software**|**Computer**<br>**Aided**<br>**Design**<br>**(CAD), 3D design, virtual**<br>**prototyping**|
|---|---|---|---|
|**Functionality**|Fundamental<br>capabilities<br>for<br>drawing and drafting, relying<br>on user skill and precision|Robust capabilities for vector<br>graphics,<br>layout design,<br>and<br>technical drawing|Comprehensive 2D and 3D de-<br>sign<br>features,<br>including<br>ad-<br>vanced modeling,<br>simulation,<br>and rendering|
|**Integration**|No digital integration;<br>stan-<br>dalone and physical|Limited integration;<br>can im-<br>port/export various fle types|High integration with CAM,<br>CAE, and other tools for seam-<br>less workfow|
|**Automation**|No automation; all aspects are<br>manual|Moderate<br>automation<br>with<br>snap-to-grid, alignment tools,<br>and reusable components|High level of automation with<br>automatic dimensioning, con-<br>straint management, and gener-<br>ative design|
|**Rank**|3|2|1|
|**Reason for Ranking**|Lowest sophistication due to re-<br>liance on manual input, lack of<br>integration, and absence of au-<br>tomation|Moderate sophistication with<br>strong 2D design capabilities,<br>but lower integration and au-<br>tomation<br>compared<br>to<br>CAD<br>tools|Highest sophistication due to<br>advanced functionality, seam-<br>less integration, and high au-<br>tomation, ideal for complex ap-<br>parel design|
|**Technology Examples**|Drafting<br>Table,<br>Mechanical<br>Pencil, Drawing Paper|Adobe<br>Illustrator,<br>Corel-<br>DRAW, AutoCAD LT|AutoCAD,<br>SolidWorks,<br>Au-<br>todesk Fusion 360|
|**Cost**|$2 - $300|$20.99/month - $499 (one-time)<br>or $198/year|$60/month - $3,995 (one-time)<br>+ $1,295/year maintenance|
|**Launch Year**|N/A|1987 - 1993|1982 - 2013|
|**ChatGPT ranking**|1|3|4.5|
|**Estimated time in a task**|8-10 hours|4-6 hours|2-3 hours|

Source: The table draws upon various sources of information including desk research, and websites of companies like Autodesk, SolidWorks, CorelDraw, and Adobe. *CHATGPT estimates the time for designing a simple dress, following similar characteristics of a given firm. The prompt for these estimates and CHATGPT ranking is available in the appendix.
crop revenue for each household. Net crop revenue here is the gross crop revenue (or total sales for each crop) less all expenditures for production, including expenditures on seed, fertilizer, irrigation, pesticide, machinery, plastic sheeting, hired labor and custom services. All of the output that was consumed by each household was given a value based on a price of the output as if it was sold on the market. Neither family labor nor a household’s rent for contracted land is counted as an expenditure. Therefore, net revenue is a measure of returns to land and family labor. Based on the total cultivated land of each household, we can calculate net crop revenue per hectare.

The data set also includes a number of other household and village characteristics. These variables are important from a theoretical point of view since they can give us measures of fixed factors which belong in Ricardian regressions. Using the data, we are able to construct variables that measure the education level of members of the farm household, each family’s land area, a number of indicators about the topographical environment of each village (e.g., if it is located on a plain or in a mountainous region), each household’s irrigation status (measured as the share of area that is irrigated in the village) and the ease of access to markets (e.g., the presence of paved roads between the village and key services; the distance to each township’s government). Such variables are used as control variables in the regressions. Descriptive statistics of the key variables are in Table 1. The Table provides key data about the entire sample as well as two important subsamples: farms that rely on irrigation and farms that do not (rainfed).

In addition to information about climate and socio-economic conditions, the characteristics of a region’s soils are also important determinants of net crop revenue. To account for soils, we downloaded a soil map from FAO’s website. There are three major soil types—clay, sand and loam soils. The final set of variables for our analysis was created by generating a variable measuring the share of cultivated area with each type of soil. These soil variables are used directly in the regression. We also include county elevation data into the regression to control the influence of elevation on net crop revenue.

In order to proceed with our analysis of the effect of climate on agriculture, we need to match the climate data with the socio-economic data of each farmer. Although there are 752 counties with meteorological stations and 533 counties in which CNBS collected HIES data, there are only 124 counties in which there are both meteorological stations and CNBS samples.

7
obviously requires more research but it highlights the possible global effects of misreporting among the top that have been already noticed in individual countries.

The shape of the global income distribution has also changed during the 20 years considered here (referring to the baseline scenario without adjusting for missing top incomes). In 1988, the global income distribution displayed a twin-peak shape which has since disappeared mostly thanks to the high growth of China whose deciles have “filled up” the area between $PPP 2,000 and $PPP 6,000 that was relatively “hollow” in 1988. The period has also witnessed a remarkable increase in what may be called a “global median class”, with incomes ranging from $PPP 2 per capita per day to $PPP 16 per capita per day: the share of the global population belonging to that group has increased from some 23% to 40%.

Particularly important is the shape of the anonymous and quasi non-anonymous growth incidence curves for the period 1988-2008. They both show large relative gains around the median of the global income distribution that accrued mostly to the middle or upper-middle income deciles from Asia, and especially from China. By contrast, the lowest real income gains were registered in the area around the 80-85<sup>th</sup> global percentiles where low income deciles from the mature economies were overrepresented. A striking fact is that among the percentiles in 1988 that turned out twenty years later to have been the most successful part of the global income distribution, 90% of people came from Asia. Among the 1988 percentiles that 20 years later turned out to have been the least successful part of the global income distribution, 86% came from mature economies.

The paper has offered another contribution. It created a new database, which (a) consists almost entirely of data derived from household survey micro data (b) keeps the household survey concept (income or consumption) constant over time for a given country, (c) uses only the most recent and most robust PPP exchange rates, and (d) allows a balanced panel analysis across the country-deciles. The latter aspect is particularly important since it has not been used before. In other words, while we knew that China’s growth rates were high and that the process was prorich (with higher deciles growing faster), we could not directly compare the growth of China’s top urban decile with (say) its 8<sup>th</sup> rural decile, and even less with the growth of deciles from the UK, Spain or Kenya. This is now all possible, and we have exploited here only a few aspects 49
numbers of observations were further reduced in IV estimations, when one spell was used as an instrument when necessary. Summary statistics for selected variables used in the regressions analyses are listed in Appendix 1.The details for the sample of spells are summarized in Appendix 2.

# **3.1 Data Sources**

Most of the data are taken from the secondary data of various international institutions: major socio-economic data from World Development Indicators (WDI) of the World Bank (World Bank, 2012), institutional indicators from the ICRG of the PRS Group (PRS Group, 2012), and aid data from OECD Stat Extracts (OECD-DAC, 2013a). The variables and their data sources are listed in Appendix 3.

# **3.2 Aid Data**

Aid data by sector are obtained from the OECD’s Creditor Reporting System (CRS). The CRS provides an aid activity database, which contains detailed quantitative and descriptive data on individual aid projects and programs. The CRS data has made it possible to analyze the sectoral and geographical breakdown of aid for selected years and donors to examine development effectiveness. In the CRS, data on the sector of destination are recorded using purpose codes. There are eight main categories: (I) social infrastructure and services; (II) economic infrastructure and services; (III) production sector; (IV) multi-sector/cross-cutting; (V) commodity aid and general program assistance; (VI) action relating to debt; (VII) humanitarian aid; and (VIII) unallocated/unspecified.<sup>15</sup> We used the sectoral data of (I) social infrastructure and services, social aid as we called it, and (II) economic infrastructure, as economic aid, in comparison with the aggregate aid of (I)-(VIII). According to the aid classification of CRS, social aid covers efforts to develop the human resource potential and ameliorate living conditions in aid recipient countries (OECD-DAC, 2007, 2013b). It includes, but is not restricted to: education such as educational infrastructure, services and investment in all areas; health and population such as assistance to hospitals and clinics, other medical services, including disease control and vaccination programs, and reproductive health and family planning; and water supply, sanitation and sewerage. Economic aid covers assistance for networks, utilities and services that facilitate economic activities. It includes, but is not restricted to: transportation, that is, equipment or infrastructure for road, rail, water and air transport; energy, production and distribution of energy; communication for television, radio and electronic information networks; and banking, financial, and business services. In addition, we used aggregate aid, which is the sum of the eight main categories mentioned above.

The current study used post-1995 economic and social aid commitment data, due to data limitations in prior years. The completeness of CRS commitments for DAC members has improved from 70% in 1995 to over 90% in 2000, and reached nearly 100% by 2003 (OECD-DAC, 2013c). On the other hand, 15 The contributions of (V)-(VIII) cannot be broken out by sector and are reported as non-sector allocable aid. 11