rafmacalaba/datause-encoder-data huggingface dataset:
household own-production of food along with in-kind gifts and offerings on the other hand. Price adjustments are undertaken on three levels. First, prices from the expenditure surveys are used to estimate the Jordanian dinar (JD) value of own-production and gifts. Second, spatial prices are calculated using the official monthly price data collected by DoS and used to take into account the price variations among governorates. Third, per capita consumption is calculated in real terms using inflation derived from the official consumer price index (CPI) computed also by DoS with 2010 selected as the base year. Real per capita household consumption is therefore used to calculate poverty and inequality incidences, and conduct micro and macro decompositions<sup>10</sup> . Furthermore, the poverty line calculated in this paper is estimated at JD813.7 per capita per year in 2010 prices. The line is based on the 2010 consumption data and uses the minimum calories intake and calories requirement methodology to compute it, and is adjusted to adult equivalency scale<sup>1112</sup> . Given the aforementioned points, the paper moves on to discuss the trends of welfare growth and consequent changes in poverty and inequality that occurred in Jordan throughout the last decade. The paper focuses primarily on the periods 2002-2010 and 2008-2010.

# **III. Patterns of Welfare Growth, and Poverty and Inequality Changes**

# _III.1. Patterns of Welfare Growth_

As a result of robust economic growth (average of 6.4 percent) during 2002-2010, household per capita consumption significantly increased by 28 percent over the same period (Table 1). However the strong increase was not evenly distributed among the different regions of the Kingdom. With a 31 percent increase, household per capita consumption was strongly depicted for households residing in rural areas, compared to a growth of 25.7 percent for their urban resident counterparts. This is mostly linked to three phenomena: the population movements over the past decade and the consequent internal migration from rural areas to urban

> 10 The Datt-Ravallion (1991) ones as described later in the paper.

> 11 This poverty line was calculated jointly by World Bank and DoS staff and the details on the methodology for its computation can be found in the background paper for the Jordan Poverty Reduction Strategy: _"The Hashemite Kingdom of Jordan: A Note on Updating Poverty Measurement Methodology."_ , World Bank 2012, Washington D.C. 12 It should be noted that the poverty incidences in this study do not correspond to the official figures published by the Department of Statistics. This is mainly due to the difference in the base year selected and the usage of consumption data and not solely expenditure as DoS publications signal. It should also be noted that this paper is more interested in analyzing consumption, poverty and inequality trends than depicting nominal values.

5
Table 2a: Persistence of specialization/diversity levels

||1989|1994|2000|2005|2010|
|---|---|---|---|---|---|
|A. Specialization in|dex for districts - C|orrelation over t|ime of district valu|es||
|1989 value|1.00|||||
|1994 value|0.18*|1.00||||
|2000 value|0.05|0.16*|1.00|||
|2005 value|-0.03|-0.02|0.31*|1.00||
|2010 value|0.04|0.01|0.04|0.25*|1.00|
|B. Diversity index|for districts - Correl|ation over time|of district values|||
|1989 value|1.00|||||
|1994 value|0.16*|1.00||||
|2000 value|0.11*|0.24*|1.00|||
|2005 value|0.10*|0.07|0.22*|1.00||
|2010 value|0.12*|0.11*|0.22*|0.34*|1.00|

Notes: See Table 1a. An asterisk denotes a correlation is statistically significant at the 10% level.

Table 2b: Table 2a considering urban areas only

||1989|1994|2000|2005|2010|
|---|---|---|---|---|---|
|A. Specialization in|dex for districts - C|orrelation over t|ime of district valu|es||
|1989 value|1.00|||||
|1994 value|0.21*|1.00||||
|2000 value|0.03|0.17*|1.00|||
|2005 value|-0.03|0.06|0.27*|1.00||
|2010 value|0.03|0.06|0.25|0.30*|1.00|
|B. Diversity index|for districts - Correl|ation over time|of district values|||
|1989 value|1.00|||||
|1994 value|0.18*|1.00||||
|2000 value|0.10*|0.18*|1.00|||
|2005 value|-0.03|0.05|0.13*|1.00||
|2010 value|0.10*|0.13*|0.07|0.22*|1.00|

Notes: See Table 2a.
Bank of Tanzania (2007), Tanzania Tourism Sector Survey: The 2005 International Visitors’ Exit Survey Report, October.

Bank of Tanzania, Directorate of Banking Supervision Annual Report 2006, 2007.

Bank of Tanzania, Balance of Payment Data for Services for the Years 2000-2006, unpublished data.

- Blalock, Garrick and Paul Gertler ( 2008), “Welfare Gains from Foreign Direct Investment through Technology Transfer to Local Suppliers,” _Journal of International Economics_ , 74:401-21

- Broda, Christian and David Weinstein (2004), “Variety, Growth and World Welfare,” _American Economic Review_ , 94 (2), May, 139-144.

- Broda, Christian, Josh Greenfield and David Weinstein (2006), “From Groundnuts to Globalization: A Structural Estimate of Trade and Growth,” National Bureau of Economic Research Working Paper No. 12512. Available at: http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/BrodaGroundnuts.pdf .

- Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), “Liberalization of Services Trade,” in  W. Martin and L. A. Winters, eds., _The Uruguay Round and the Developing Countries_ , Cambridge: Cambridge University Press.

- Brown, Drusilla and Robert Stern (2001), “Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services,” _Review of International Economics_ , 9(2): 262-286.

- Chinitz, B. (1961), “Contrast in agglomeration: New York and Pittsburgh,” _American Economic Review, Papers and Proceedings_ , 51:279-89.

- Coe, David T., and .Elhanen Helpman (1995), "International R&D Spillovers,” _European Economic Review_ 39 (5): 859-887.

- Coe, David T., .Elhanen Helpman and Alexander W. Hoffinaister (1997), "North-South R&D Spillovers", _Economic Journal_ , Vol 107, 134-149.

- Coe, David T. and Alexander W. Hoffinaister (1999), "Are There International R&D Spillovers Among Randomly Matched Trade Partners?- A Response to Keller,” International Monetary Fund Working Paper.

- Cox, David and Richard G. Harris (1986), “A Quantitative Assessment of the Economic Impact on Canada of Sectoral Free Trade with the United States,” _The Canadian Journal of Economics_ , Vol. 19 (3), August, 377-394.

Daniels, P.W. (1985), _Service Industries: A Geographical Appraisal_ , New York: Methuen & Co.

Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), “Measuring the Costs of Barriers to Trade in Services,” in _Trade in Services in the Asia-Pacific Region_ , Takatoshi Ito and Anne Krueger (eds.), Chicago: University of Chicago Press.

- Dihel, Nora, Ana Margarida Fernandes, Aaditya Mattoo and Nicholas Strychacz (2010), “Reform and Regional Integration of Professional Services in East Africa,” Economic Premise No. 32, September, The World Bank. Available at: www.worldbank.org/economicpremise.

- Dixit, A. and J. Stiglitz (1977), “Monopolistic Competition and Optimum Product Diversity,” _American Economic Review_ , 76(1):297-308.

51
coordination in the management of assets and liabilities across institutions while maintaining each institution’s independence. Full implementation of ALM in the public sector or for the sovereign is rare but a number of countries have been implementing a partial approach.

The findings of this paper are based on a survey conducted in 2017 by the World Bank in collaboration with the OECD and the IMF, among 28 countries.<sup>5</sup> The survey comprised 6 sections and focused on the various approaches underpinning the management of sovereign assets and liabilities.<sup>6</sup> It assessed how integrated and operational balance sheet frameworks are applied across countries, looking at the production of an actual balance sheet as well as at the objectives, priority areas and challenges of the management of the balance sheet.

This paper is organized as follows. Section A presents the participants’ responses to the survey in regard to the production, coverage, periodicity and publication of sovereign balance sheets. Section B examines the SALM practices, the entities in charge, the objectives of management, the reporting mechanisms, as well as the risk framework under which the respondents operate. Section C examines how the respondents have been able to implement such an integrated approach to managing sovereign assets and liabilities, the challenges they are facing and the plans they have for the future.

As indicated above, 28 countries responded to the questionnaire,<sup>7</sup> most of them high‐income economies that are members of the OECD (80 percent) with a smaller share of middle‐income countries (20 percent). In terms of geography, 50 percent of the respondents are from an EU member country. The income level of the respondents implies that the countries producing a sovereign balance sheet and fully or partially implementing a SALM framework have well developed governance and institutional structures. As indicated by the survey respondents, countries face a number of challenges in the implementation of a SALM framework and production of a sovereign balance sheet, chief among them the complex institutional arrangements and coordination between actors that is required. As demonstrated in the results, the objectives, main actors, reporting format, and metrics used to produce the sovereign balance sheet vary widely across countries. At a fundamental level, data availability and valuation of the assets (and choosing which assets and liabilities to include) remain key constraints.

> 5 See Annex 1 for the list of responding countries.

> 6 See Annex 2 for the survey.

> 7 Among the 28 countries, one responded only to the first section of the survey covering the sovereign balance sheet. The remaining 27 responded to the full survey.

6
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 28. Consumption of infrastructure services in Mali is highly differentiated by household budget**

a. Mode of water supply, by income quintile <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>piped water standposts<br>wells/boreholes surface water<br>b. Prevalence of connection to power grid among Kenyan population, by income quintile<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>% of population<br>% of population<br><!-- End of picture text -->

- b. Prevalence of connection to power grid among Kenyan population, by income quintile

Legend: Q1 – first budget quintile, Q2 – second budget quintile, etc. Source: Banerjee and others 2009.

Operational inefficiencies of power and water utilities cost Mali $52 million each year, or 0.8 percent of GDP. Mali’s power utility faces distribution losses of 24 percent (around twice best practice levels). As a result, Mali’s power utility generates major hidden costs for the economy. The utility’s collection rate is comparatively high at around 96 percent. In the case of water, revenue collection inefficiencies are comparatively low, and distribution losses stand at 29 percent compared to the best practice benchmark of 20 percent. Due to the smaller financial turnover of the water sector, these hidden costs weigh less heavily on GDP (figure 29).

46
we consider several political variables to adjust for any independent influence of party politics. First, we construct a variable POLITY that is a composite measure of the general openness of political institutions in the country. This variable is calculated by taking the average for the years 1990-99 of the Polity IV composite index of the country. Polity IV is the updated version of the cross-country database on political regime characteristics and transitions during the period 1800-2002. The Polity database was originally designed by Ted Robert Gurr and has been updated by Monty Marshall and Keith Jaggers. The database is maintained at the Center for International Development and Conflict Management of the University of Maryland. This database has been widely used by researchers as a source of data on political-institutional characteristics.

We also construct a variable DEMOC that is the average for the years 1990-99 of the Polity IV democracy index, and a variable AUTOC that is the average for the years 1990-99 of the Polity IV autocracy index. DEMOC ranges from 0 to 10 with a higher score indicating more democracy (and general openness of political institutions). AUTOC also ranges from 0 to 10 with a higher score indicating a more autocratic government (and general closedness of political institutions). The Polity IV composite index equals the difference between the Polity IV democracy index and the Polity IV autocracy index. POLITY ranges from –10 to 10 with a higher score indicating more democracy and a lower score indicating more autocracy. The polity index is based on measures of the degree of competitiveness of political participation, the regulation of political participation, the openness and competitiveness of executive recruitment, and constraints on the chief executive.

9
marital status and number of children, but this applies to the proxy assignment, and not to the short – detailed assignment, where characteristics are balanced.

The identifying variables for the control function estimation are obtained from CWIQ data collected in the same communities during the same year, but covering different households. We use the community mean distance to primary and secondary school to proxy the local supply of education at the time of the individual’s schooling.

Before discussing the results, we consider the differences in wages across treatments. The plots in Figure 1 present the kernel density for the log of daily earnings for the different subsamples. Figure 1A(i) and 1B(i) draw the earnings obtained from the detailed and short modules for men and women respectively, with the distribution generated by the latter mostly to the left of that generated by the former. Figure 1A(ii) and 1B(ii) suggests that the difference is less outspoken for the wages generated by proxy and self treatments.

The descriptive statistics in Table 3 reveal further differences. While the detailed module yields a lower proportion of labor force participants compared to the short module for both men and women, it generates a higher relative proportion of wage workers for both sexes. The detailed module also produces lower mean daily earnings than the short module. Proxy response, on the other hand, leads to relative lower labor force participation and a lower proportion of wage workers, but produces higher mean wages compared to self response, again for both sexes. Although the differences in wages between treatments may seem small, they are actually substantial, as is perhaps more clear when expressed as monthly rather than daily wages. The average daily wage reported in Tanzanian Shilling (TSH) in Table 1 for men (3987, 4781, 3634, 6255) correspond to monthly income expressed in USD of respectively 75USD, 89USD, 68USD and 117USD, while the respective daily wages in TSH for women (3912, 4388, 3552, 5367) correspond to the monthly wages of 73USD, 82USD, 66USD, 100USD.

16
33 spending in the Baltics were cut by 45 percent. Meanwhile, in Central Europe, real pension was reduced by 20 percent, and real pension spending was flat. Therefore, despite relatively low pensions at the onset of the transition, and sharper declines in real GDPs, the Baltics ended up with the constant, and Central European countries with the sharply increasing, share of pension spending in GDP. The explanation for the difference in the outcome lies probably in the sphere of political economy.

Three elements may explain "Baltic exceptionalism".

®Strong feeling of discontinuity with the previous regime reflected in the feeling that it was foreign occupation. The implicit contract between the individual who made pension contributions and the state was much less compelling that in other countries where the feeling of country’s "ownership" in the decisions by which pensions were determined was stronger. As an illustration, consider a statement by Latvia’s Minister of Welfare who addressed the pensioners as follows: "you do not need big pensions, because you worked under the Communist regime, and your work accomplished nothing".® It is unthinkable that such a statement could have been made elsewhere—without any apparent effect on the minister (although the pensioners associations did protest).

@The fact that many losers who are ethnic Russians are not citizens and cannot vote. Their political clout is negligible. Although their demographic structure is not much different from the rest of the Baltic population, many more of them are located in cities with few complementary sources of income. In contrast, more of the older native Baltic inhabitants (who are citizens and vote) live in rural areas where they have access to other income sources. Pension cuts affect them less.

©The fact that pension-wage ratio in the USSR was lower than in Eastern Europe made it easier to introduce a low flat rate at around 30 percent. However, this is only a partially satisfactory argument because it explains only why low flat rate was acceptable, not why flat rate as such was acceptable. Central European countries could have established a higher flat rate.

# Some preliminary conclusions

# The pension changes to date suggest that:

®The pension reform is not primarily a technical but rather a political economy issue. The difference in outcomes between, for example, the Baltic states and Central Europe, cannot be explained in terms of the difference in the initial conditions, population shares of pensioners,

> “The 45 percent reduction in pension spending is calculated from the unweighted pension expenditures, that is, pension spending in 1987-88 in each country, regardless of its absolute amount, is considered as 100.

*See Baltic Observer BM:
_Figure 2 - Focus group discussion with community members of Sabasaba on March 18, 2018_

# 2.2.4 Generalizing the results to the entire city

To generalize the results to a city level, sample weights were applied that had been calculated based on flood risk strata. Each household is assigned a sample weight corresponding to the number of households in the city that the sampled households represent, based on selected strata and population. By applying the weights, results can be scaled up to city level. Since we oversampled in areas affected by flood risk as per the methodology described above, we need to generate and use weights to “adjust” the sample to represent the city of Dar es Salaam in terms of flood risk. By using flood risk specific strata for weighting, we also generate more accurate results of the scaled-up results (number of affected people in the city and total losses from floods, etc.) by assuming that households within each stratum are facing similar risks and impacts of floods. Next, we will describe how we generated the flood risk weights.

The flood risk strata were identified using a machine learning (ML) approach, based on the reported occurrence of flood exposure by households in the collected data. The information from the Ramani Huria maps was complemented with elevation and population density by EA to more precisely and transparently categorize areas of Dar es Salaam according to their flood risk.<sup>8</sup> This information was used to build a risk model using an ensembled decision tree algorithm (Figure 3). The model identifies critical thresholds of the variables for different levels of risk of being affected. For example, “extra high risk” is defined as EAs for which area is covered by the Ramani Huria flood tag by 67% or more, located below 5.06 meters elevation and with a population density higher than 0.08 (people/square meter). Using these thresholds, four categories of risk level were identified – low risk, medium risk, high risk and extra high risk. The

> 8 This was done after sampling because it was found that many households in the “low risk” category using Ramani Huria maps only were affected due to unknown factors. In addition, it was found that surprisingly many households in the higher risk categories based solely on Ramani Huria maps were reporting not being affected. By adding population density and elevation it was possible to better capture affected households in flood risk categories and understand what was driving the flood risk categorization.

7
**Table 5 – Tobacco-related Deaths, by Gender and Age-Group, Indonesia, 2016**

||||**Female**|||||**Male**|||
|---|---|---|---|---|---|---|---|---|---|---|
||**25-34**|**35-54**|**55-74**|**75+**|**Total**|**25-34**|**35-54**|**55-74**|**75+**|**Total**|
|**Total Tobacco-related deaths**|**148**|**3,797**|**8,649**|**5,147**|**17,740**|**3,901**|**40,160**|**69,136**|**22,172**|**135,368**|
|Ischemic heart disease|65|1,808|5,092|2,720|9,684|1,705|18,017|26,742|7,890|54,354|
|Cerebrovascular disease|38|1,264|2,714|2,107|6,123|1,165|14,888|27,204|7,634|50,892|
|Tuberculosis|11|153|357|283|804|810|4,279|5,626|2,162|12,876|
|Chronic obstructive pulmonary disease|9|184|282|31|507|76|916|5,057|3,281|9,329|
|Tracheal, bronchus, and lung cancer|12|199|114|3|328|68|1,196|3,051|839|5,153|
|Cervical cancer|4|62|17|0|83|-|-|-|-|-|
|Lip and oral cavity cancer|3|32|17|0|52|18|150|179|52|399|
|Larynx cancer|0|20|25|1|46|5|111|348|90|554|
|Stomach cancer|2|18|7|0|27|8|103|194|55|359|
|Nasopharynx cancer|1|16|7|0|24|11|167|244|38|460|
|Colon and rectum cancer|1|16|6|0|24|10|48|50|13|121|
|Esophageal cancer|1|15|7|0|22|3|96|152|31|282|
|Bladder cancer|0|4|3|0|7|2|37|118|52|209|
|Other chronic respiratory diseases|0|3|1|0|4|4|14|4|1|23|
|Leukemia|0|2|1|0|3<br>|8|48|51|10|117|
|Liver cancer due to other causes|0|1|1|0|2|7|90|118|24|240|

_Source:_ GBD Results Tool (database), Global Burden of Disease Study 2016, Global Health Data Exchange, Institute for Health Metrics and Evaluation, Seattle, http://ghdx.healthdata.org/gbd-results-tool.

9
with _struggling_ (See Table 6). Also Gallup’s _food and shelter (FS) index_ , a two-item indicator measuring whether people have enough money to buy food and shelter for their families (alpha=0.86, range 0-100), has a strong negative correlation with _suffering_ and _struggling,_ confirming the importance of being able to fulfill basic needs for an individual’s subjective well-being.

In Table 7 (Column 3-5) these three measures are included in the OLS regression where the dependent variable is again the number of demonstrations and strikes. The effect of satisfaction with _standards of living_ and the expectation that _standards of living are getting better_ are negative and statistically significant, whereas the coefficient of the FS index is not statistically significant. This is not surprising, because individuals who live below a certain subsistence level, as indicated by a low FS index, might be unlikely to engage in civil resistance even if they are _suffering;_ their first priority is to survive on a day-to-day basis.

We also include a measure of the provision of community basics. The provision of these basics is often regarded as one of the core responsibilities of the state. Hence, individuals who experience grievances due to inadequate provision of these goods can hold the state responsible by participating in nonviolent uprisings. We measure the perceived quality of community basics with the _Community Basics (CB) index_ developed by the Gallup World Poll. This index combines satisfaction rates of 7 different basic goods: the public transportation system, roads and highways, quality of air, quality of water, the availability of good affordable housing, the educational system and the availability of quality health care (alpha= 0.90, range 0-100). When included in the OLS model (Table 7, Column 6), the coefficient is negative but insignificant. Hence, we find no evidence that dissatisfaction with the provision of basic public goods affects nonviolent resistance.

33