rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 1: GDP**|**per capita a**|**nd growth **|**by region **|**(weighte**|**d data)**||
|---|---|---|---|---|---|---|
|**Region**|**1975-80**|**1981-85**|**1986-90**|**1991-95**|**1996-2000**|**2001-05**|
||||**GDP pe**|**r capita**|||
|Sub-Saharan Africa|1,928|1,844|1,782|1,648|1,668|1,768|
|East Asia & Pacific|905|1,227|1,686|2,407|3,399|4,595|
|Latin America & Caribbean|6,020|6,295|6,315|6,450|6,978|7,205|
|Middle East & North Africa|4,179|4,180|4,055|4,326|4,651|5,197|
|South Asia|1,132|1,268|1,505|1,745|2,110|2,530|
|Low & middle income|2,278|2,560|2,881|3,045|3,513|4,219|
||||**Gro**|**wth**|||
|Sub-Saharan Africa|-0.06|-1.60|-0.21|-1.64|0.79|1.79|
|East Asia & Pacific|5.26|6.12|5.76|9.10|5.63|7.06|
|Latin America & Caribbean|3.31|-0.95|-0.43|1.61|1.53|1.21|
|Middle East & North Africa|-0.20|2.41|-1.20|1.18|1.91|2.78|
|South Asia|1.03|3.14|3.89|3.01|3.59|4.65|
|Low & middle income|2.79|1.99|1.93|1.56|3.23|4.58|

Note: All Sub-Saharan African countries are included in claculations.

Figure 3 shows that Africa has the lowest CV of GDP per capita, which is due to its long economic stagnation. Figure 4 shows that African countries have the least predictable growth, as suggested by the largest CV. Numerous factors can explain this outcome, such as higher exposure to climatic shocks, changes in the international economic environment, political economy issues, and high incidence of conflicts. Guillaumont, Jeanneney and Brun (1999) found that, for Africa, these sources of instability contribute to the induction of bad policies that result in instabilities of the rate of investment and the real exchange rate and in lower total factor productivity.

6
**Figure 17: IMR by prefecture within three of China’s provinces, 2007**

<!-- Start of picture text --> 35<br>30<br>Xinjiang<br>25<br>20<br>Inner Mongolia<br>15<br>10<br>5<br>Hebei<br>0<br>3000 30000<br>GDP per capita (RMB, log scale)<br>(per 1,000 of live births)<br>Prefecture infant mortality<br><!-- End of picture text --> 29
- 24 -

Figure 6. Monthly Earnings and GDP per Capita, 2002–2005 <!-- Start of picture text --> 16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-5 0 5 10 15 20 25 30<br>Avg. growth in monthly earnings (%)<br>Sources: International Financial Statistics ; and World Development<br>Indicators .<br>Avg. growth in GDP per capita (%)<br><!-- End of picture text -->

> Six variables are adopted for a proxy of _INF_ : road density, share of paved roads, electricity

> consumption per capita, teledensity, gross fixed capital formulation ( _GFCF_ ), and public GFCF. In addition, another variable representing educational access is also considered, since

> literacy could matter for effective sales and marketing communication (Williams _et al_ ., 2006).

> Ideally, _INF_ should be measured by “livestock-specific infrastructure” if such a variable existed. In reality, it seems difficult to capture livestock-related infrastructure by any single available measurement, particularly in the cross-sectional context. Infrastructure that farmers rely on for raising cattle may vary from country to country. If a modern system of “factory farming” is adopted, production efficiency is determined by feeds and investments in

> machinery and equipment (e.g., Ramsey _et al_ ., 2005). Then, the most important infrastructures would be electricity and water. If livestock or deadstock are mass-transported, another essential infrastructure would be railways network.

In Africa, however, this is not the case in general. Africa’s livestock industry is a more traditional type of livestock grazing. It seems most costly for local farmers to convey their livestock to a slaughterhouse; in this regard rural roads are the most important infrastructure besides abattoirs and warehouses, which are obviously part of essential infrastructure in this industry. Transport infrastructure from abattoirs to major ports may also be important. All in all, road network seems to be a key to enhance efficiency in livestock production and trade in
inflation. The models will be applied to changes in livelihoods and determinants of the impact mainly at the household level. Since different causes affected livelihoods in this period of instability in South Sudan, the difference-in-difference approach will identify the effect of inflation on livelihoods by correlating changes in prices with changes in livelihood indicators. The data sets contain information on security, economic conditions, education, employment, access to services, and perceptions. They also include comprehensive information on assets and consumption, to allow estimation of poverty based on the Rapid Consumption Survey methodology as detailed in Pape and Mistiaen (2018).

Table 1: High Frequency South Sudan Survey (HFSSS), survey dates and coverage

||Data collection<br>dates|Geographic coverage|Rural/Urban coverage|
|---|---|---|---|
|Wave 1|February 2015 -<br>September 2015|6 out of 10 states: Western Equatoria,<br>Central Equatoria, Eastern Equatoria,<br>Northern Bahr El Ghazal, Western Bahr<br>El Ghazal, and Lakes state.|Covered urban and rural<br>households|
|Wave 2|February 2016 –<br>June 2016|7 out of 10 states: wave 1 + Warrap<br>state. The other three former states<br>(Jonglei, Unity, and Upper Nile) could<br>not be surveyed due to security concerns.|Revisited urban<br>households interviewed in<br>Wave 1|
|Wave 3|September 2016 -<br>March 2017|7 out of 10 states: Same as Wave 2|Covered a new cross-<br>section of urban and rural<br>households|
|Wave 4|May 2017 -<br>August 2017|7 out of 10 states: Same as Waves 2 and<br>3.|Revisited urban<br>households from Waves 1<br>and 2|

Source: High Frequency South Sudan Survey

# **Prices and Inflation**

The consumption section of the household survey (HFSSS) collects information on items’ unit prices and quantities. As with all data collected from sample surveys, the household-reported prices are subject to sampling errors. Item non-response and measurement error will also lead to biased estimates (Dahlhamer _et al_ ., 2003; Garner _et al_ ., 2009). However, household-reported prices have a key strength: knowing precisely the prices paid by households who make expenditures themselves has an advantage in that it captures the parallel exchange rates, showing households’ real purchasing power. This is particularly important in the context of South Sudan with a strong parallel exchange market.

Based on the strengths and weaknesses of the three price data sources, we decided to use household-reported prices because it covers the entire sample and has price information for all items consumed by the household. Thus, for our analysis, inflation is calculated based on unit price household survey data (using Laspeyres price index). In addition to using the total inflation variable, we also break it down into food price inflation and non-food price inflation to explore which of the two might be driving the results.

4
In Table 1, we compare firm-level financing patterns in China with other developing countries in the world. The data for the countries are obtained from other Investment Climate Surveys (ICS).<sup>13</sup> As of 2006, there were 67 country surveys covering over 40,000 firms. Since the core survey instrument is the same across all countries, we have comparable information on financing sources across the different countries. The only difference is some surveys also have information on leasing arrangements and credit card financing which is not provided in the case of China. We combine these two categories along with Trade Credit and label it Operations Finance in our tables.

In panel A of Table 1, we present individual financing patterns and in panel B we present aggregate financing patterns. In both panels, we compare firm-level financing patterns (averaged across all firms) in China with those in other emerging markets such as Bangladesh, Brazil, India,<sup>14</sup> Indonesia, Nigeria, and the Russian Federation. Panel A shows that China has the least amount of Internal financing/Retained Earnings (only 15.24%) compared to all the other emerging markets. On the other hand, China also has the highest average amount of Other Financing (42.70%) compared to the other developing countries. The next largest dependence on Other Financing is in Indonesia, but even there, only 8.8% of new investments are financed by Other sources. These other financing sources could well represent the large underground informal lending in China, which represents several hundred billion dollars in bank deposits according to a recent

> 13 The Investment Climate Surveys are an on-going initiative by the World Bank to study the investment climate conditions in developing countries and their impact on firm productivity, investment and employment.

> 14 The information on financing choices for Indian firms comes from the World Business Environment Survey (WBES) which was also conducted by the World Bank as a precursor to the Investment Climate Surveys. The Indian ICS does not have detailed information on firm financing choices.

13
# **4. INTERGOVERNMENTAL FISCAL TRANSFERS**

Oil and gas production not only provides a significant amount of revenue to the central government. It also funds Indonesia’s far-reaching decentralization, using an array of formal revenue-sharing agreements with sub-national governments. Indonesia’s 434 districts and 33 provinces now spend as much as 36 percent of the country’s total public expenditures. But vertical fiscal imbalance in terms of revenue raising is very high: these units raise only about 5 percent of their expenditure themselves. Sub-national governments instead rely on various forms of revenue sharing, including sharing of oil and gas revenues, as well as an equalization block grant and smaller special, earmarked grants.

While these shared revenues appear as an expense for the central government, they flow directly to subnational governments and so have a little impact on Indonesia’s overall public sector fiscal position. The following paragraphs describe how the different procedures relating to oil and gas revenue function, how movements in oil prices impact on these transfers and hence central government expenditures, and how the formula for calculating these transfers influence the central government’s choice of budget assumptions.

The main intergovernmental transfer affected by oil and gas receipts is ‘revenue sharing’. Its size relative to the national budget has slightly increased from 2001 to 2007 (from 6 percent to 9 percent of total central government expenditures). It continues to represent just above a quarter of total transfers in the country, as other types of transfers have kept pace with its growth (Table 8, see Annex 5 for transfers in Rupiah). The regions where the oil and gas is extracted receive additional shares of oil and gas revenue, as do the special autonomy regions of Papua and Aceh.

**Table 8.  Intergovernmental transfers**

_<u>Percentage of total national expenditures</u>_

||**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008***|
|---|---|---|---|---|---|---|---|---|
|Total Transfer to Regions|24%|31%|32%|31%|29%|34%|33%|30%|
|Revenue Sharing|6%|8%|8%|9%|10%|10%|8%|8%|
|General Allocation funds (DAU)|18%|22%|20%|19%|17%|22%|22%|18%|
|Special Allocation funds (DAK)|0%|0%|1%|1%|1%|2%|2%|2%|
|Special Autonomyand Adj. Funds|0%|1%|2%|2%|1%|1%|1%|1%|

*2008 Revised budget (APBN-P). _Source: World Bank calculations base by Ministry of Finance publications._

## _Sharing of tax and non-tax revenues_

Revenue sharing is determined by formula that was formalized into legislation in 2004 and 2005 (Article 11, Law No. 33/2004 and GR No. 55/2005). In summary, the following proportions of government revenue are shared: 20
# **Table 3. Panel A. Basic Regressions with Savings-Related Measures, ICA data**

|Dependent<br>variable|(1)<br>Operating<br>Income to<br>Sales|(2)<br>Any<br>Investment|(3)<br>Any<br>Investment|(4)<br>IK|(5)<br>IK|(6)<br>Ret.<br>Earnings<br>WK|(7)<br>Ret.<br>Earnings<br>WK|(8)<br>Ret.<br>Earnings<br>Invest|(9)<br>Ret.<br>Earnings<br>Invest|
|---|---|---|---|---|---|---|---|---|---|
|Small size|-0.02|-0.14***|-0.13***|-0.09***|-0.08***|14.62**|15.24**|-7.44|-18.48|
||(0.121)|(0.000)|(0.000)|(0.000)|(0.000)|(0.024)|(0.022)|(0.697)|(0.364)|
|Foreign|0.02|0.09**|0.08**|0.05|0.04|-36.59***|-36.54***|-23.98|-58.71*|
||(0.530)|(0.014)|(0.040)|(0.117)|(0.218)|(0.000)|(0.001)|(0.466)|(0.094)|
|Exporter|-0.01|0.09***|0.11***|0.09***|0.09***|-20.78***|-18.22**|10.30|16.07|
||(0.578)|(0.000)|(0.000)|(0.000)|(0.000)|(0.004)|(0.013)|(0.628)|(0.474)|
|Manufacturing|-0.35***|-0.28***|-0.28***|||61.91***|66.26***|-26.76|-37.04|
||(0.000)|(0.000)|(0.000)|||(0.000)|(0.000)|(0.225)|(0.175)|
|Government|-0.06|-0.02|-0.02|-0.02|-0.02|-22.13|-21.13|-84.03*|-108.92**|
||(0.179)|(0.699)|(0.727)|(0.680)|(0.618)|(0.183)|(0.233)|(0.083)|(0.038)|
|Year 2005|-0.01|-0.19***|-0.20***|-0.18***|-0.18***|-30.64***|-27.57***|-358.75***|-350.18***|
||(0.520)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Year 2007|0.08***|0.03|0.03|-0.00|0.00|23.61***|15.22**|31.79|14.44|
|Operating|(0.000)|(0.179)|(0.139)|(0.973)|(0.982)|(0.002)|(0.047)|(0.161)|(0.546)|
|<br>Income to Sales|||-0.04||0.01||18.32**||18.65|
||||(0.123)||(0.718)||(0.036)||(0.484)|
|Constant|0.51***|||-0.15***|-0.14***|132.27***|120.66***|461.95***|476.17***|
||(0.000)|||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|3237|3609|3153|2885|2600|3833|3237|3573|3012|
|R-squared|0.147|||||||||

Note: regression one is estimated by OLS, 2 and 3 by Probit and all others by Tobit.

34
22 _Table 9: Results for logit of labor market participation_

|VARIABLE|_labor mar_|_ketparticip_|_ation by he_|_ad(total ho_|_urs > 0)_|
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**-0.150**||**-0.210**|**-0.190**|**-0.190**|
||**-2.710**||**-2.610**|**-3.000**|**-3.000**|
|SAH_POS_CH_LGE||-0.189||||
|||-0.600||||
|SAH_POS_CH_SML||0.059||||
|||0.430||||
|SAH_NEG_CH_SML||-0.317||||
|||**-2.360**||||
|SAH_NEG_CH_LGE||**-0.464**||||
|||-1.570||||
|POOR X SAH_CHANGE|||0.122|||
||||1.030|||
|HI X SAH_CHANGE||||0.196||
|||||1.320||
|URB_HI X SAH_CHANGE|||||0.231|
||||||1.290|
|RUR_HI X SAH_CHANGE|||||0.137|
||||||0.600|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|-0.299|||||
||-1.624|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.308||||
|||-0.786||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.170||||
|||0.466||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||-0.087|||
||||-1.067|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||0.006||
|||||0.046||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||0.040|
||||||0.246|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||-0.054|
||||||-0.247|
|N|1,726|1,726|1,726|1,726|1,726|

_Note: Results estimated with household-level fixed effects logit. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t-statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (two-sided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
region’s rapid convergence of telephone penetration rates.<sup>2</sup> In the past 10 years, some countries in Africa, such as the Democratic Republic of the Congo, Equatorial Guinea, Ghana, Mali, Mauritania, Nigeria and Somalia, experienced a marked improvement of telephone penetration with more than 40 percent of annual growth rates, but the growth in other countries, such as Eritrea, Ethiopia, Guinea-Bissau and Namibia, was much more moderate at less then 15 percent per annum (Figure 2). Notably, this is not simply because the initial levels of installed network were low in the former group of countries. By regional standards, the telephone penetration rates in Botswana and Gabon were not so low in 1996, but their growth has been kept relatively high at about 30 percent a year.<sup>3</sup> Certainly, some countries that had higher subscription rates at the initial stage are faced with quick market saturation (e.g., Namibia, South Africa, and Seychelles). There are several countries that are confronted by a serious challenge of poor initial conditions coexisting with slow growth, e.g., Eritrea, Ethiopia and Guinea-Bissau.

<!-- Start of picture text --> Table 1. Total Telephone Subscribers per 1,000 by Region, 1983-2003<br>1983 1993 2003<br>Average Std. dev. Average Std. dev. Average Std. dev.<br>East Asia and Pacific 94.2 121.7 174.3 191.7 573.1 568.8<br>Latin America and Caribbean 66.2 67.6 132.3 121.1 421.7 238.6<br>Sub-Saharan Africa 8.1 14.6 15.8 29.4 105.3 168.3<br>Source: World Development Indicators .<br>Figure 2. 10-Year Evolution of Telephone Penetration Rates in Africa, 1995-2005<br>MRT<br>50<br>ZAR<br>SOMMLI<br>NGA<br>GHAGNQ<br>40 TCDUGASDNCMR<br>TZA<br>RWAGINBFATGOCOGLSOCIVSEN<br>30 MOZAGOBENKEN GABBWA<br>NERMDGMWI GMBSWZ<br>CAF<br>BDI<br>20 COMZMBZWESTP CPV ZAF MUS SYC<br>NAM<br>ETH<br>ERI<br>10<br>GNB<br>0<br>5 30 55 80 105 130 155 180<br>Telephone penetration rate (per 1,000) in 1995<br>Source: International Telecommunication Union .<br>Annual average growth rate (percent)<br><!-- End of picture text --> Figure 2. 10-Year Evolution of Telephone Penetration Rates in Africa, 1995-2005 2 Africa’s dynamic evolution in telecommunications provision is also evident in comparison with other infrastructure services, as illustrated by Estache (2005).

3 Botswana’s governance and institutions are among the best by regional and global standard. The quality of regulation is generally acceptable; of particular note, the Botswana Telecommunications Authority (BTA) of 1996 has been praised as one of the first independent regulatory authorities in Africa (ITU, 2001).

3
elsewhere that a sharp increase in the level of government cash transfers to individuals can lead to decline in inequality (Keane and Prasad, 2001). Keane and Prasad (2001) show that despite growing labor earnings inequality following transition to market economy, the rise in transfer expenditures in Poland in the early years of transition mitigated the increase in overall income inequality. Transfers from families and friends have equally important effect on inequality. For instance, without transfers from family members and friends networks, the Gini coefficient would increase by 59, 67 and 67 percent respectively in 2002, 2003 and 2004.

There is important policy implication from the findings under hypothesis 1. The fact that inequality increases significantly without transfers implies that transfer incomes (particularly those from government sources) are reasonably targeted well. As equalizing effect of increased from 2002 to 2004 further suggests that the efficiency of targeting improved over time. As a result, the incomes of households in the lower wealth echelons were lifted more so than the incomes of the rich.

**Table** **<u>6.</u>** Transfers have welfare equalizing effect

|**Gini coefficient**||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>friends|
|Azerbaijan|31.8|27.5|28.1|32.9|27.9|29|31.8|27.2|27.2|
|Baku|33.4|30.3|30.8|33.8|29.1|30.8|29.5|27.5|25.7|
|Non-Baku urban|31.6|26.6|27.5|34.2|28.7|29.8|31.2|27.2|26.8|
|Rural|30.5|25.9|26.7|31|26.1|27.1|31.8|25.8|26.8|

Source: Azerbaijan HIES data.

# **Table 7.** Percent increase in the Gini coefficients after transfers are removed

|**Gini coefficient**||**2002**<br>|||**2003**<br>|||**2004**<br>||
|---|---|---|---|---|---|---|---|---|---|
||without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>and<br>friends|without<br>all<br>transfers|without<br>social<br>transfers|without<br>transfers<br>from<br>family<br>friends|
|Azerbaijan|73|50|53|81|53|59|95|67|67|
|Baku|56|52|44|69|45|72|65|38|43|
|Non-Baku urban|82|44|75|85|55|90|99|47|71|
|Rural|81|55|79|86|56|82|114|55|80|

Source: Azerbaijan HIES data.

# **_Possible Reasons for Missing Observations_**

Testing hypotheses 2, 3 and 4 are relatively more complicated than hypothesis 1. It requires knowledge of the number of missing observations, preferably at the lower level of geographic aggregation. Fortunately the sampling framework for Azerbaijan HIES established identical 9
7

**<u>Table 3:  Estimated Cost Breakdown of USO Services in Low-volume Environments</u>**

||**Cost of**<br>**Piece**<br>10|**Mail/Piece ($)**<br>**s/Capita**<br>5|<br>1|
|---|---|---|---|
|Mail processing|0.06|0.07|0.16|
|Transportation|0.02|0.03|0.07|
|Window service|0.08|0.13|0.34|
|Delivery|0.76|1.19|3.06|
|Other|0.44|0.69|1.77|
|**Total**|**1.36**|**2.11**|**5.40**|

Making this problem more acute is the fact that a far greater percentage of people in developing countries are difficult-to-serve rural customers—69 percent in low income countries (World Bank, 2002). Income density—an important measure of “demand for postal services per square kilometer”—is far lower in developing countries than rich ones—$39,000 per square kilometer in Sub-Saharan Africa as compared to $658,000 in high income countries, for example (Kenny, 2002). Both of these factors suggest that even less stringent USO may cost more to meet in poor countries than they would in rich ones.

There is significant variation in all of these numbers—some postal operations are more efficient than others, and deliver more mail at the same level of GDP than others. As we have seen, postal performance (in terms of letters per capita) is correlated with greater operational efficiency (measured by letters per staff, for example) and trust in the postal network (itself reflecting quality of service). At the same time, some developing countries have very large, very dense postal markets where delivery is far more straightforward.

Nonetheless, the point remains that, in most cases, even less stringent USOs would be more expensive per letter than similar obligations in the developed world, while the profitable market that might provide cross-subsidy is far smaller, because of the lower level of urban development and large corporate customers. Even in some of the richer, most population-dense, urbanized developing countries about 90 percent of routes are loss-making.<sup>9</sup> This situation would be worse in countries where only 31 percent of the population were urban (the case in low income countries). The cost and complexity of service delivery, especially in small markets, will help account for the fact that post offices frequently make significant losses in the developing world. Forty-five percent of countries with GDP per capita under $5,000 see revenues below operating expenses (let alone total costs).<sup>10</sup> UPU data suggests that 42 postal incumbents had negative net revenues in 2003, totaling nearly half a billion US dollars. In Nigeria, annual losses are above 0.05 percent of GDP.

Putting revenues and indicators of costs together suggests the scale of the problem. Low income countries will average total postal volumes lower than 10 million pieces a year. Imagine a

> 9 Traffic is measured by revenues, in this case.  British Postal Consultancy Service (2002)

> 10 Raising prices to the levels suggested by the Cohen et. al. (2003a) study would be an unworkable solution—this would doubtless reduce demand in favor of alternate providers, thereby further raising per-unit costs.