rafmacalaba/datause-encoder-data huggingface dataset:
- 32 -

# TABLE 6. BANK OPERATIONS WITH EMPLOYMENT/PAY COMPONENTS

# BY LENDING INSTRUNENT

# (As of Februarv 1987)

|REGION|SALS|TALS|OTHER|TOTAL|
|---|---|---|---|---|
|West Africa|||||
|Approved<br>|6<br>|6<br>|0<br>|12<br>|
|Proposed|5|4|0|9|
|East Africa|||||
|Approved|1|1|0|2|
|Proposed|0|1|1|2|
|South Asia|||||
|Approved|0|1|1|2|
|Proposed|0|1|0|1|
|IAC|||||
|Approved|4|3|1|8|
|Proposed|1|2|0|3|
|EAP|||||
|Approved|2|0|0|2|
|Proposed|0|0|0|0|
|EMENA|||||
|Approved|4|0|0|4|
|Proposed|0|0|0|0|
|Total|||||
|Approved|17|11|2<br>|30|
|Proposed|A|A|1|la|
||23|19|3|45|
**<u>Table 5: Determinants of registered transactions at cell level</u>**

|||**OLS**||**Cell**|**level fixed ef**|**fects**|
|---|---|---|---|---|---|---|
||**All**|**Sales**|**Non-mon.**|**All**|**Sales**|**Non-mon.**|
||**Panel A:**|**Residential**|**land**||||
|Sector land manager appointed|1.113***|1.030***|0.077|0.575***|0.877***|-0.305*|
||(0.363)|(0.363)|(0.127)|(0.204)|(0.258)|(0.165)|
|Sector covered by land week events|-0.064|-0.102|0.034|0.042|-0.075|0.109|
||(0.212)|(0.188)|(0.095)|(0.113)|(0.119)|(0.110)|
|Sector land manager * Land week|-0.078|-0.004|-0.073|-0.061|-0.081|0.029|
||(0.589)|(0.570)|(0.143)|(0.271)|(0.342)|(0.191)|
|Distance to DLO in km|-0.019|0.006|-0.025***||||
||(0.020)|(0.015)|(0.008)||||
|No of residential parcels in the cell (‘000)|3.485***<br>(0.701)|2.479***<br>(0.582)|1.014***<br>(0.220)||||
|Ratio of urban population in the sector|9.041***<br>(1.262)|7.110***<br>(1.167)|1.995***<br>(0.377)||||
|Constant|-1.575***|-1.811***|0.238*||||
||(0.510)|(0.446)|(0.122)||||
|Number of observations|17,184|17,184|17,184||||
|R<sup>2</sup>|0.362|0.334|0.111||||
||**Panel B:**|**Agricultura**|**l land**||||
|Sector land manager appointed|0.973***|1.074***|-0.109|0.934***|1.225***|-0.296**|
||(0.226)|(0.219)|(0.094)|(0.165)|(0.232)|(0.123)|
|Sector covered by land week events|-0.137|-0.120|-0.022|-0.012|-0.107|0.095|
||(0.134)|(0.098)|(0.075)|(0.066)|(0.079)|(0.061)|
|Sector land manager * Land week|0.346|0.305|0.054|0.080|0.032|0.063|
||(0.429)|(0.406)|(0.102)|(0.213)|(0.284)|(0.140)|
|Distance to DLO in km|-0.033***|-0.011|-0.023***||||
||(0.013)|(0.009)|(0.006)||||
|No of agri. parcels in the cell (‘000)|0.127***<br>(0.042)|0.079***<br>(0.029)|0.049**<br>(0.020)||||
|Ratio of urban population in the sector|<br>2.359***|<br>1.547**|<br>0.832***||||
||(0.783)|(0.626)|(0.274)||||
|Constant|0.547*|-0.138|0.688***|0.822***|0.167***|0.660***|
||(0.294)|(0.240)|(0.127)|(0.040)|(0.051)|(0.026)|
|Number of observations|17,184|17,184|17,184|17,184|17,184|17,184|
|R<sup>2</sup>|0.136|0.168|0.033|0.095|0.136|0.024|

_Note:_ Dependent variable is the number of registered transactions resulting in a transfer of ownership for residential or agricultural land for Panels A and B, respectively.

All the specifications include province-year dummies throughout but coefficients are not reported. Robust standard errors clustered at the sector level in parenthesis: *** significant at 1%; significant at 5%; * significant at 10%.

20
Figure 4: Demand Curve: Full Payment, full sample <!-- Start of picture text --> 0 1,000 3,000 5,000 10,000 15,000 20,000<br>Price in J$<br>BDM sample TIOLI sample<br>1<br>.8<br>.6<br>.4<br>Share that paid full price<br>.2<br>0<br><!-- End of picture text --> 43
the average COL inflation in their country and 2.3 percentage points higher than the inflation rate faced by households in the top decile of the consumption distribution. In Moldova, the inflation rate of the basket of goods and services consumed by households from the lowest decile is 1.8 percentage points higher than the average inflation in the country and 5.7 percentage points higher than the inflation faced by the richest households. In North Macedonia, the inflation rate for the lowest decile is 3.1 percentage points higher than the average inflation in the country and 5.2 percentage points higher than the inflation faced by the households in the top decile. A similar pattern holds for the other countries in the sample except for Belarus, where wealthier households seem to experience inflation that is higher than the mean inflation and inflation faced by the poor. The magnitude of the difference is relatively small, with households in the top decile facing inflation 0.3 percentage points above the average and 0.8 percentage points higher than the inflation of the bottom decile.<sup>14</sup> These differences in inflation between the lowest and highest deciles of the income distribution are within the ranges found by other studies – such as the 1.2 percentage points difference observed between the top and the bottom deciles in a sample of 25 European countries in 2001–2015 by <u>Gurer and Weichenrieder (2020) and the 1.5 percentage point difference found by Kints and Breunig (2021)</u> in Australia during a period of relatively low inflation (2011 – 2018). Kaplan and <u>Schulhofer-Wohl (2017)</u> find that lower-income households in the US faced higher inflation than wealthier households in 2004 - 2013, with an annual interquartile difference of 6.2–9.0 percentage points.

Qualitatively similar patterns of regressive inflation are observed for EU/EEA countries (Figure 8, panel b), for which information is available only by quintiles. In this sample, the widest inflation gap between the poorest and wealthiest households (about 8.1 percentage points) is observed in Latvia. Norway shows no clear relationship between household consumption level and COL inflation rates. The average difference in inflation between the poorest and richest households in

> 14 In Belarus, the consumption categories with the largest price increases were transport (21.2 percent annual increase by December 2022), followed by miscellaneous goods and services (17.6 percent) and alcohol and tobacco (16.8 percent). The first two are categories for which the share of consumption allocated by richer households is larger than for poor households. In the case of alcohol and tobacco, the share is larger for poorer households than for richer ones, but the difference in small.

14
# **A1.13 World Bank/University of E3asel (WDR)**

The World Bank is an internal:ional organization dedicated to eradicating poverty in the world. It was founded in 1945 and is located in Washington. As of May 1999, the World Bank can be reached at httPJl/www.worldbank.orq.

As background for the 1997 VVorld Development Report (WDR) on the role of the state, the World Bank conducted a survey of approximately 3000 enterprises on 69 countries. This survey was designed to measure the perception of firms on the constraints that government actions impose on them. The results of this survey are in the public domain and may be downloaded from http:l/www.worldbank.orp/html/edi/gac/ ubs.htm.

In a separate effort, the same questionnaire was implemented by Aymo Brunetti and Beatrice Weder of the University of Basel, in five additional countries. Data and information on this research project can be found at: http://www.unibas.chlwwz/wifor/survev In the governance database, we combine information from these two sources and use the country average responses to the questions listed in Table Al .13.

# **Appendix 2: Components of Aggiregate Governance Indicators**

**Table Gl. Voice and Accountability**

|**Code**|**Table**<br>**Row**<br>**(Appendix 1)**|**Concepts measured**|
|---|---|---|
|**Represe**|**ntative sources**||
|EIU|A 1.5<br>3|Change in government, orderly transfer|
||9|Legal system, transparency, fairness|
|FH|A 1.6<br>2|Civil Liberties: Freedom of speech, of assembly and<br>demonstration, of religion, equal opportunity, of<br>of excessive governmental intervention|
||1|Political Rights: free and fair elections, representative legislative,<br>free vote, political parties, no dominant group,|
|||respect for minorities|
||3|Free Press: Laws and practice, independence, and violations)|
|PRS|A 1.10<br>7|Military in politics: Reduces accountability|
||11|Democratic accountability: Responsiveness of the government to<br>its people, free and fair elections|
|WDR|A 1.13<br>3|Business is kept informed of important developments in<br>rules and policies|
||4|Business has a voice to express its concems over<br>changes in laws or policies|
|**Non-rep**|**resentative sources**||
|FHNT|A 1.6<br>4|Political process: Elections, party configuration, political competition,<br>and participation|
||5|Civil society: Volunteerism, trade unionism, professional<br>associations.|
||6|Independent media|
|PERC||Media: independence and quality|
|||Transparency of the business environment|
|WCY98|A 1.12<br>10|Transparency: The government communicates its|
|||intentions successfully|
**24 |** P a g e

The change in terms of trade in favor of resource intensive products and the shift away from EU markets might have exaggerated the role of resource‐intensive products in BiH exports. Prices of raw materials and resource intensive products like, for instance, steel, bauxite and aluminum, have increased vis‐à‐vis many manufactures. In consequence, their volumes might have increased less than their exports in value terms. Furthermore, the rise in price of raw materials exported mainly to SEE‐5 countries may be a major factor explaining the fall in the share of the EU‐27 in total exports from 76 percent in 2003 to 63 percent in 2006 and 59 percent in 2007.<sup>10</sup> Indeed, there are two different pictures of the development in technology and labor content of EU‐oriented and ROW‐oriented exports. Since the EU foreign trade statistics rank among the best in the world, and the EU takes more than half of BiH total exports, the conclusions drawn on their base may reflect closer the actual reality of BiH sources of improved competitiveness. Running the EU data through Landesman’s and Stehrer’s filter yields a completely different result. The share of BiH exports characterized by low‐technology, labor intensive production processes significantly fell in 2003‐07, whereas the share of medium‐ to high‐tech products increased almost 20 percent. EU‐oriented exports of these products posted the strongest growth with their value of almost three‐times higher in 2007 than in 2003. In marked contrast, the dominance of resource intensive products in ROW‐oriented exports significantly expanded from 55 percent of the total ROW‐destined exports in 2003 to 68 percent in 2007. The share of both low‐tech, unskilled labor intensive and medium to high‐tech intensive products in ROW‐destined exports dramatically contracted in this period; the latter posted a larger reduction than the former (Table 15).

**Table 15: Technology content of EU and ROW imports from BiH in 2003 and 2007 (in percent and millions of US dollars)**

||EU-<br>2003|27<br>2007|Index 2007<br>2003=100|RO<br>2003|W<br>2007|Index 2007<br>2003=100|Share of EU<br>2003|in total<br>2007|Index 2007<br>2003=100|
|---|---|---|---|---|---|---|---|---|---|
|Low-tech, labor intensive|33.4|28.9|87|23.0|16.1|70|82.1|72.3|88|
|Resource intensive|43.0|43.2|100|54.6|68.0|124|71.3|48.0|67|
|Medium to High-tech|22.7|27.0|119|21.8|14.5|66|76.6|73.0|95|
|Total|100|100|100|100|100|100|75.9|59.2|78|

Source: Own calculations based on EU reporting to UN COMTRADE database.

Exports to the EU not only provide a stimulus to industrial restructuring and moving up the value added but are also critical to creating and retaining highly paid jobs in BiH. While nothing short of

> 10 Calculations are based on partners’ statistics reported to the UN COMTRADE database, which are not complete for 2007 at the time of this writing.
their health, there could be a tendency for them to become more adherent. Therefore, we cannot conclude definitely as to the impact of the crisis on adherence between these two years, as we do not have a good counterfactual, that is, we do not know how adherence would have evolved without the crisis.

# **5. Is there an impact of the food crisis on the frequency of visits to the health facility?**

As mentioned in the introduction, cost of transportation to get a prescription refill at a health facility is a strong barrier to adherence. Essential expenditures such as food, schooling and health are competing inside the household budget. An economic crisis, by reducing income and increasing food prices can shift the equilibrium in the household allocation of resources and reduce expenditures devoted to health. In addition to the food crisis, a sharp increase in fuel prices potentially increased even more the cost of transportation for patients. In this context, another way to look at the impact of a food crisis on adherence to treatment and on the livelihoods of people living with HIV/AIDS is to consider the change in the frequency of visits to the health facility. The frequency of visits can also be understood as a proxy for good adherence to treatment.

The biomedical data, which collected health related information on 498 HIV positive patients of the survey, allows us to calculate the number of weeks between two visits, with one to six observations for each patient (data on the last seven visits). For the analysis we keep only those observations that are in the proper timeframe, that is, between the two waves of the survey. The time between two visits in itself is not a sufficient indicator of regularity because the visit protocol varies across health facilities. Therefore, the outcome studied is the standardized deviation from the mean frequency of visits in the health facility of the considered observation. A positive number means that the patient visited the facility less often than average. We merged the biomedical records with the living standards survey to regress the frequency of visits on welfare and nutritional status measures. The equation estimated using OLS is: 15
1996. The sample size varies between 119,000 and 140,000 depending on the regression model specification, due to missing values for certain variables.

Because height and weight measures come from self-reports, they likely suffer from reporting error (Bound et al. 2001; Cawley et al., 2015). We adjust these measures for reporting error using data from the National Health and Nutrition Examination Survey (NHANES) III (1988-1994), which include both self-reports and measurements of weight and height and are from a similar time period as our NHIS data (Burkhauser and Cawley 2008).<sup>7</sup> From 1982-96, NHIS respondents were asked whether they had ever served in the US military and, if so, during which period (e.g. World War II, Korean War, Vietnam War). We do not use NHIS data from before 1982 because in those earlier years the NHIS data do not include month of birth, which we use to construct a more accurate measure of induction risk. We do not use NHIS data from after 1996 because in those later years the NHIS only asked whether respondents had been “honorably discharged” from the military, not whether they had served during the Vietnam War era (or served at all but without an honorable discharge). We focus only on the years in which we have the most complete military data (1982-96), however we extend our sample through 2012 as a sensitivity analysis.

Our sample is limited to individuals born in the calendar years 1937 to 1956. This is to avoid any contamination of our main analysis sample with individuals serving in the Korean War, which ended in July 1953. Thus, any individual born in 1937 would not have been eligible

> 7 Using the NHANES data for the same age range as our NHIS data (age 25-59), we estimate the following regressions separately by gender: ������������h�� = �� + ������������� + �������������� + �� + �� (1) Where SelfReport refers to self-reported weight, and X includes age and race (non-Hispanic Black, non-Hispanic other race, and Hispanic).  We then save the constant and coefficient estimates from these regressions and create an adjusted measure for weight in the NHIS sample by multiplying the NHIS self-reported weight values and demographic characteristics by their coefficients from NHANES regressions, and adding them together, along with the constant from the regression model. We perform a similar adjustment for self-reported height. Using these constructed values, we create an adjusted measure of BMI and obesity (BMI≥30) which we use throughout the rest of this manuscript. We also estimate all models using the non-adjusted BMI and obesity measures and find slightly larger and more statistically significant results using unadjusted variables.

8
**Table 2. Malnutrition Indicators for Ethiopia, by Region**

||**_Height fo_**|**_r Age_**|**_Weight _**|**_for Age_**||
|---|---|---|---|---|---|
||% below<br>–2SD|Mean Z|% below<br>–2SD|Mean Z|Number of<br>Children|
|**Residence**||||||
|Urban|41.6|-1.7|34.0|-1.4|1,067|
|Rural|52.3|-2.1|48.6|-1.9|9,328|
|**Region **||||||
|Tigray|55.3|-2.1|47.9|-1.9|689|
|Affar|47.6|-1.9|50.5|-1.9|94|
|Amhara|57.0|-2.3|51.8|-2.0|2712|
|Oromiya|47.2|-1.9|42.4|-1.7|4288|
|Somali|46.2|-1.7|44.3|-1.6|83|
|Benishngul-Gumuz|41.3|-1.7|42.3|-1.7|101|
|SNNP|55.4|-2.3|53.7|-2.0|2237|
|Gambela|37.0|-1.3|39.0|-1.6|23|
|Harari|37.3|-1.5|27.1|-1.4|21|
|Addis Ababa|26.8|-1.1|14.1|-0.9|165|
|DireDawa<br>Notes: Reported means|30.5<br>are adjusted fo|-1.1<br>r sample probab|30.8<br>ilities|-1.3|36|

The two most important risk factors contributing to malnutrition are insufficient food intake and repeated infectious illnesses. Table 3 shows the proportion of children malnourished and with diarrhea and fever by age groups and by the mother’s level of education. The proportion of children malnourished increases significantly with age— particularly between the first and second year of life. The World Health Organization (WHO) recommends exclusive breastfeeding for children under 6 months and the introduction of solid and semi-solid food around the age of 6 months. The Ethiopia data shows that most children under the age of 6 months are predominantly breastfed, although only about a third are exclusively breastfed, as it is recommended by the WHO.

The mother’s education level is likely to be a significant factor influencing child feeding practices. The data show the introduction of other liquids takes place earlier than at the suggested 6 months of age—with children as young as 2 months of age consuming other liquids. Almost thirty percent of children under 6 months are also given plain water or other water based liquids or juices. The children of mothers with a primary education are breastfed 8
- 17 -

1 1 i i 2 y + 2 h h h + pp pp −pp yy 1 kk wwkk kk kk wwkkww kk ln cc = ∑αα ln cc + ββ ln yy++ 2ββ ln yyln yy+ ∑ββh h hln ww+ ∑∑ββ ln ww+ ln ww ∑ββkk yywwh kk ln yyln wwh kk + ∑ββhkk+zzkk ln zzkk ∑∑ββkk zzkkzz ln zzkk ln zz (2) + ∑ββkk yyzzkk ln yyln zzkk kk wwkkzz kk ii ii ∑∑ββ ln ww ln zz uu + uu + εε To have a well-behaved cost function, the following symmetry and homogeneity restrictions are imposed: h h h h h h h = 0 wwkkww (3) ww wwkk zzkkzz zz zzkk kk wwkk kk wwiiww kk yywwkk kk wwkkzz ββ = ββ , ββ = ββ , ∑ββ = 1 , ∑∑ββ = 0 , ∑ββ = 0 , ∑ββ To estimate Equation (2), the Arellano-Bond (1991) dynamic panel data model is applied. By applying the dynamic panel model, the unobserved firm-specific fixed-effects _ui_ are eliminated, and the potential endogeneity among the variables in the equation is also addressed simultaneously. Given the parameters estimated, the price elasticity of factor demand can be calculated using the Allen’s partial elasticities of substitution between inputs _k_ and _h_ (e.g., Uzawa, 1962; Berndt and Wood, 1975): The paper also investigates the inventory and agglomeration effects measured by _Q_ , _N_ or _M_ . A simple EOQ model suggests that the optimal inventory increases with the demand size (represented by firm’s total production or sales, _y_ ) and decreases with transport connectivity _vk_ and interest rate ( _INT_ ):
Consistent with the overall expenditure figures, Uganda’s share of social spending is lowest among the benchmark group. At 3.9 percent of GDP (or 4.2 percent if we include public pensions), the country spends less on direct transfers, education, and health than Tanzania (6.3 percent), Ghana (7.4 percent) and Kenya (9.5 percent), a result driven by low education expenditure (see Figure 4a). Social spending is also lower than Ethiopia (at 7.2 percent), driven by education and in direct transfers.

_Figure 4. Public expenditure in different countries_ _a. Public expenditure as percent of GDP b. Composition of social public expenditure as percent of GDP_ <!-- Start of picture text --> 55% 20%<br>45%<br>15%<br>Tanzania South 4.1%<br>35%<br>Africa 10%<br>Ghana<br>2.0%<br>25% Kenya 1.2% 1.7% 7.0%<br>5% 1.6%<br>15% Ethiopia 4.6% 5.7% 3.8% 4.6% 1.3%2.4% 6.4%<br>Uganda 0% 1.3% 1.1%<br>5% Ethiopia Ghana South Tanzania Uganda Kenya<br>0 5,000 10,000 15,000 20,000 25,000 (2010) (2012) Africa (2011) (2016) (2015)<br>(2010)<br>GDP per capita (2011 PPP)<br>Direct transfers Education Health<br>% of GDP % of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers. For Kenya, the source is World Bank (2018a), and the value reported for direct transfers corresponds to social protection expenditure of the national government._ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, for Kenya 2015/16._

# 2.4.1. Public Pension System

Uganda’s public pension system was established in 1946 to provide retirement benefits for public sector servants and includes one scheme for the Armed Forces and one for the Civil Service. The first covers all military officials and the second includes all civil servants: central government officials, police and prison officers, members of the judiciary, doctors, primary and secondary school teachers. There are 45,200 beneficiaries out of a total of 274,200 enrolled individuals. Beneficiaries receive payments (part lump sum on retirement and an annual pension) based on length of service and salary during service. Survivors receive benefits in case of death before retirement. On average, the annual pension is about 3,460,400 UGX, or 1.34 times the GDP per capita (World Bank, 2019b). There were no public pension contributions in fiscal year 2016/17; pension transfers are mainly funded by general government revenues.

11