rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2: SSEPS Timeline**

||_Year_|J F MA MJ J A S O N D J F MA MJ J A S O N D<br>2010<br>2011|
|---|---|---|
|**Project Component**|**Duration**||
|Project Planning/ Equipment Procurement<br>Pilot<br>Enumerator Training<br>Phone Delivery & Baseline Survey|5 months<br>1 month||
|Data collection(monthly)|4 months||

Households targeted for the SSEPS consisted of those that had been surveyed in round 4 of the Household Budget Survey (HBS), which were a representative sample drawn from the ten state capitals. These households were drawn from 10 enumeration areas in each of the state capitals, with 12 households per enumeration area for a total of approximately 1200 households. Of these households, 1007 were located and agreed to participate in the SSEPS. Given that the bulk of South Sudan’s urban population lives in the state capitals, results from the SSEPS are broadly representative of the conditions in urban areas. Selection of the individual respondent within each household to receive the mobile phone and answer calls was performed during an initial household visit made by enumerators from NBS.

To assess the effect of incentives on participation, sampled enumeration areas were randomly assigned to two incentive groups: one receiving pre-paid credit of 5 Sudanese Pounds (SDG)—approximately US$2.17—for each call leading to a completed survey and another receiving pre-paid credit of 10 SDG (US$4.35) for each call leading to a completed survey.<sup>4</sup> This credit was transferred on a weekly basis: the call center would send a list of participants that had completed the survey in the previous week to partners at the mobile phone operator (Zain), which would then transfer the credit to households. In the month of December, some of the transferred credits were delayed by two weeks as the purchased credit had not been activated. In addition, enumeration areas were randomly assigned to receive either a basic Nokia phone or a Safaricom solar-rechargeable phone. Given these combinations, four groups defined by phone type and incentive amount were formed: (1) Nokia, 5 SDG incentive group, (2) Nokia, 10 SDG incentive group, (3) Safaricom, 5 SDG incentive group and (4) Safaricom, 10 SDG group. A last incentive which was offered to all respondents came in the form of a lottery. All sampled households

> 4 US Dollar equivalent figures are based on annual exchange rate average for of 2.3 SDG per USD for 2010.

8
Instead, temporary or permanent growth effects of capital accumulation and productivity improvement are supported by financial intermediaries (banks, capital markets) that actively mobilize savings and channel these towards profit-maximizing investment opportunities. Another strand of the growth literature that (implicitly) emphasizes a services dimension stresses the importance of human capital (education) and R&D (a “service” activity) in generating (endogenous) growth (e.g., Lucas, 1988, Romer, 1990).

**<mark>Table 4: Reduced form panel estimates of FDI stocks regressed on policy reform variables</mark>**

|Estimation technique|OLS fixed effects|OLS fixed effects|
|---|---|---|
|Dependent variable|FDI/GDP|FDI/GDP|
|Model No.|(1)|(2)|
|Independent variables|||
|(a) Service reform index|18.92<br>(20.39)***||
|(b) Investment climate index||17.13<br>(15.04)***|
|R<sup>2</sup>|0.61|0.46|
|No. of obs.|203|203|

<mark>Note: t-values in brackets, asterisks denote significance at 10*,5**, and 1*** percent levels. Sample includes 17 transition economies. See Annex for definition of variables.</mark> The role of producer-services of the type captured by the infrastructure services reform index in the growth process has not attracted much attention in the theoretical or empirical growth literature. Francois (1990) develops a model that points to the importance of such producer services for economic growth, although his model is not dynamic. He argues that the increasing importance of producer services in modern (growing) economies reflects economies of scale and specialization. As firm size increases and labor specializes, more activity needs to be devoted to coordinating and organizing the core businesses of a company. This additional activity is partly outsourced to external service providers. The associated organizational innovations and expansion of “logistics” (network) services yields productivity gains that in turn should affect economy-wide growth performance by enhancing the efficiency of production in all sectors. The associated cost reductions can have the effect of upgrading overall productivity, and are likely to be enhanced by, if not conditional on, increased FDI in services (Konan and Maskus, 2004; Markusen et al., 2005).<sup>8</sup>

> 8 Most of the quantitative analyses of the impact of services policy reforms has used static applied general equilibrium models. These find that services policies are important for welfare—e.g., Konan and Maskus (2004).

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- 25 -

To illustrate the way of thinking of the best bidding strategy, the OLS estimates involving multiple joint bidding variables are suggestive to represent where the potential competitive effect of joint bidding generates (Figure 6). In road procurements, local firms have the large cost advantage. If they prefer to form a joint bidding, some of this advantage would be lost. But if it is a coalition with other local firms, the cost advantage will be retained. In addition, if that coalition involves a foreign company, they would secure additional competitiveness. In the water sector, local firms also have the cost advantage, but not so large. Joint bidding will affect negatively. If it is a local consortium, then they could maintain their cost advantage. However, if it is a consortium between local and foreign firms, they would lose almost all cost advantage.

From the viewpoint of foreign firms, it is clear that they have the cost _dis_ advantage. Joint bidding does not help much in both sectors. Even worse, it is costly to arrange a bidding coalition with other foreign firms. Only the way of improving their competitiveness is joint venture involving local enterprises. This would bring about sizable benefits, especially in road procurements.

Figure 6. Estimated Joint Bidding Effects <!-- Start of picture text --> 60 60<br>50 Local firm 50 ForeiJoint biddinggn firm<br>Joint bidding<br>40 Jointly bidding local firm 40 Jointly bidding foreign firm<br>Foreign firm jointly bidding with local firm(s)<br>30 Local firm jointly bidding with foreign firm(s) 30<br>20 20<br>10 10<br>0 0<br>-10 -10<br>-20 -20<br>-30 -30<br>-40 -40<br>-50 -50<br>-60 -60<br>Roads Water & sewage Roads Water & sewage<br>Source: Author’s calculation.<br>Estimated impact on bid Estimated impact on bid<br><!-- End of picture text --> The policy implications are straightforward:

- Joint bidding practices are not always pro-competitive in infrastructure projects, except several cases. It means that auctioneers need to be cautious about amplified
occur at the macro level as a result of shocks (most importantly, changes in employment) to the micro component of the analysis. More generally, the assumption that within-group rank ordering of households and individuals are unchanged and unaffected by policy shocks implies that workers are withdrawn from the sector of origin in a representative manner (leaving the distribution of income there unchanged) and that, as they move from one sector to another, they assume the income distribution characteristics of the sector of destination (in particular, the variance of income in that sector is assumed to apply to all new entrants).<sup>1</sup> Thus, some workers may be poor not because of their personal characteristics, but rather because of the economic circumstances that characterize their sector of employment. At the same time, however, the nature of the practical gains entailed by dropping the assumption of a stable within-group distribution and accounting fully for heterogeneity at the micro level remain a matter of debate. In a context where skills and resources are limited, it is thus important to compare alternative approaches, using standard poverty and income distribution measures.

This paper contributes to this debate by comparing three approaches aimed at linking macro and micro levels to analyze the poverty and distributional effects of policy and exogenous shocks in applied general equilibrium models. The first approach is the one followed in most of the IMMPA applications described earlier, and consists of introducing group-specific changes in income and consumption in a household income survey and computing postshock poverty and distributional indicators on the basis of the “adjusted” household data. The second approach extends the first in the sense that it not only incorporates changes in income and consumption occurring at the macro level in the household survey, but it also accounts for changes in the employment structure predicted by the macro component. This is done by modifying the weight given to each household in the survey. The third approach, which was pioneered by Adelman and Robinson (1978) and Dervis, de Melo and Robinson (1982), and more recently by Decaluwé, Dumont and Savard (1999), and Decaluwé, Patry, Savard, and Thorbecke (1999), imposes a fixed, parametrically estimated distribution of income within each group and assumes that shocks shift the mean of these distributions without, however, modifying their shape. Poverty indicators are then computed based

> 1It also implies that income transfers between households in any given group are ignored. In practice, intra-group income reallocation may be large in periods of hardship and may represent an important factor in understanding the poverty effects of adverse economic shocks.

4
**Table 2: Effects of Health Care Worker Deaths from Ebola on Maternal Mortality**

Doctors, nurses, & midwives Maternal mortality ratio (per 100,000 live births)

||Stock<br>pre-Ebola|Stock<br>post-<br>Ebola|% change|Pre-<br>Ebola<br>ratio<br>(2013)|May<br>2015<br>ratio|% change|<br>Lower<br>bound %<br>change<br>(95%)|<br>Upper<br>bound %<br>change<br>(95%)|
|---|---|---|---|---|---|---|---|---|
|Guinea|5395|5317|-1%|650|897|**38%**|26%|50%|
|Liberia|1029|946|-8%|640|1347|**111%**|76%|145%|
|Sierra Leone|1153|1074|-7%|1100|1916|**74%**|51%|97%|

Source: Author calculations based on Ebola mortality data from the WHO,<sup><u>8</u></sup> population and maternal mortality data from World Development Indicators,<sup><u>9</u></sup> and health worker-mortality coefficients from Speybroeck et al.<sup><u>18</u></sup> Note: Data on pre-Ebola stock of health workers is for the most recent years available for each country: 2004 (nurses and midwives) and 2005 (doctors) for Guinea, 2008 for Liberia, and 2010 for Sierra Leone.

**Table 3: Effects of Health Care Worker Deaths from Ebola on Infant Mortality***

||Doctors, n|urses, &|midwives|Infan|t mortality|rate (per 1|,000 live|births)|
|---|---|---|---|---|---|---|---|---|
||Stock<br>pre-Ebola|Stock<br>post-<br>Ebola|% change|Pre-<br>Ebola<br>rate<br>(2013)|May<br>2015 rate|<sup>% change</sup>|<br>Lower<br>bound %<br>change<br>(95%)|<br>Upper<br>bound %<br>change<br>(95%)|
|Guinea|5395|5317|-1%|64.9|69|7%|-2%|15%|
|Liberia|1029|946|-8%|53.6|64|20%|-4%|43%|
|Sierra Leone|1153|1074|-7%|107.2|121|13%|-3%|29%|

Source: Author calculations based on Ebola mortality data from the WHO,<sup><u>8</u></sup> population and infant mortality data from World Development Indicators,<sup><u>9</u></sup> and health worker-mortality coefficients from Speybroeck et al.<sup><u>18</u></sup> Note: Data on pre-Ebola stock of health workers is for the most recent years available for each country: 2004 (nurses and midwives) and 2005 (doctors) for Guinea, 2008 for Liberia, and 2010 for Sierra Leone. *Health care worker mortality coefficients in Speybroeck et al. are not statistically significant for infant mortality.

8
Figure 1: Towards a Modem Industrial Relations System <!-- Start of picture text --> Supply price of labor<br>Statutory labor rights<br>Voice and representation<br>Ill Modem<br>industrial<br>relations i<br>system.<br>11 Closing the Statutoryrightsareconsistent<br>-<br>gap between with widespread practice.<br>statutory rights - __ - - - - - - - -. - V -Voluntary negotiations.<br>and voluntary I --C ~ ~~ | .onflict resolution is influenced'<br>agreemnents. t / .- j bv market conditions.<br>~~ .......--------------- beregulation l<br>I Fromn tradition to 1)rglto<br>I Increasing wage employment<br>statutory rights. Large informal sector Rising demands for<br>Barriers to entry modem sector representation<br>Controlled representation<br>1945 1985 1996 2010<br><!-- End of picture text -->
**Summary**

The lack of private financial flows to Sub-Saharan Africa in recent years has made countries in the region increasingly dependent on official development assistance (ODA), in the form of either grants or highly concessional lending. Meanwhile, "aid fatigue" has become a common concern among many in the field of development, and aid flows have fallen in recent years as budget constraints in the major donor countries become increasingly tight.

Given the increasing demand from developing countries for additional aid flows on the one hand and the limited resources available from industrial countries on the other, aid effectiveness is a critical issue. In this context official donors have stepped up their efforts to provide larger amounts of aid (with partial or no repayment obligation) and to provide official debt forgiveness, bilaterally and through the Paris Club.

This paper evaluates the effect these initiatives had on recipient countries based on data accumulated during 1989-93. The analysis focuses on the potential tradeoff between pure grants and bilateral ODA debt forgiveness, in thirty-two poor African countries.

The analysis first compares countries that have received significant amounts of ODA debt forgiveness in recent years with those that have not, and then compares the two groups of countries during 1984-88 and 1989-93 (ODA debt forgiveness surged during the latter period). The effect of the two types of aid is measured by the recipient countries' import capacity.

There are five major conclusions of the paper:

- Grants have allowed recipient countries to significantly expand their import capacity over the past decade (1984-93). In fact, grants and import capacity-inclusive of international reserves accumulation-have been increasing since 1984. Yet the increasing share of concessional lending and debt relief in recent years has not allowed these countries to reduce their total indebtedness and solve their debtoverhang problem. In fact, arrears increased significantly during this period for all the countries in our sample.

- The 32 countries in the sample can be grouped according to the type of ODA debt relief they have received from donors. Of the three groups, the highest recipients have received the lion's share of debt forgiveness and the largest increase in pure grants. The paper investigates the rationale for such a distributional pattern and finds that standard economic hierarchies (such as per capita income, foreign exchange income, indebtedness, and access to altemative financial resources) do not satisfactorily explain the allocation of debt forgiveness.
**Table 1:** _Real cost of moving goods in and out of Eurasia_

||Real Export<br>Cost<br>(man days<sup>1</sup>)|Real Import<br>Cost<br>(man days<sup>1</sup>)|Documents<br>to Export|Documents<br>to Import|Time to<br>Export<br>(days)|Time to<br>Import<br>(days)|
|---|---|---|---|---|---|---|
|**Resource-rich**<br>**(Eurasia)**|||||||
|Russia|24|26|9|12|21|36|
|Kazakhstan|45|45|10|13|89|76|
|Uzbekistan|429|466|13|15|86|104|
|Azerbaijan|75|73|8|10|43|48|
|**Non Resource-rich**<br>**(Eurasia)**|||||||
|Armenia|45|54|6|8|30|24|
|Georgia|48|46|8|7|12|14|
|Belarus|35|42|9|10|18|35|
|Ukraine|47|48|6|8|30|33|
|Moldova|95|112|7|7|32|35|
|Kyrgyz Republic|331|359|14|16|64|75|
|Tajikistan|363|493|11|12|71|65|
|**Comparators (world)**|||||||
|Poland|7|7|5|5|17|18|
|China|20|24|8|6|21|24|

Sources: World Bank, World Development Indicators; ILO, Employment Data. Notes:<sup>1</sup> Real Export/Import Cost indicates total cost divided by GDP per worker in the economy per day. Includes costs associated with documents, customs administrative fees, customs broker fees, terminal charges, and inland transport.

Eurasia, which accounts for 6 percent of the world’s GDP but 8 percent of its energy demand, is one of the most energy-inefficient regions in the world both in terms of consumption and production of energy. Sector assets employ old and outdated technologies; most thermal plants, especially coal-fired plants, have been operating well beyond the design life. Russia is one of the largest emitters of CO2 in the world, after the United States and China. Yet, Russia and other hydrocarbon producers in the region will not be able to supply Western Europe with the energy it needs unless there is a significant ramp-up in the new upstream oil and gas investments, as well as pipeline investments. Together with rising gas prices and concern about the reliance on Russia for fuel, Eurasia is tending towards a growth pattern based on more polluting, but locally available coal, and resistance to shutting down aging nuclear reactors. About half of today’s energy infrastructure should be rehabilitated by 2030 (World Bank 2011). The renewal of sector assets provides an opportunity to curtain the carbon footprint and increase the resilience of the sector to climate change.

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# **3.3 Data**

We estimate our empirical model using energy and bilateral trade data for 10 manufacturing sectors in 43 countries from 1991 to 2012. The full set of countries included in our analysis is listed in Table 1.

We use data on electricity and natural gas prices from the International Energy Agency (IEA) Energy Prices and Taxes database, the Energy Regulators Regional Association (ERRA) Tariff Database, and various government and media reports. The IEA database records after-tax prices for residential and industrial consumers, while the ERRA database records after-tax prices for residential and non-residential consumers. For a subset of nine countries covered by both the IEA and the ERRA, we cross-check the price data from the two datasets and find that the data are fairly consistent. We use IEA data on industrial energy prices wherever available. We use ERRA data on nonresidential energy prices for one country (Saudi Arabia) that is not covered by the IEA. Table 2 lists the sources of energy price data for each country.

Energy consumption data are from the IEA World Energy Statistics and Balances database. This database reports final electricity and natural gas consumption for 10 manufacturing sectors at the two-digit level of the international Standard Industrial Classification, Revision 3.1 (ISIC Rev. 3.1). One of the sectors is labelled “non-specified". According to IEA documentation, this sector includes rubber and rubber products as well as any manufacturing activities not covered by the other nine sectors. Because there may be measurement errors associated with the energy intensity of this miscellaneous category, we conduct a robustness check by excluding the “non-specified” sector from the sample.<sup>5</sup> We gather data on manufacturing value added, employment costs and number of employees from the United Nations Industrial Development Organization (UNIDO) Industrial Statistics Database (INDSTAT2). The sector-specific wages are calculated by dividing employment costs by the total number of total employees. Factor intensities are measured as the value of factor inputs divided by value added.<sup>6</sup>

> 5The IEA notes that for some countries, “non-specified” could be a sector average where a sector-wise breakdown is not possible.

> 6About 1.5 percent of the observations have factor intensity values below zero or greater than one. We dropped these observations as outliers.

14
28 Figure 3: Nominal rate of assistance to farmers and consumer tax equivalents in developing country regions and in Europe’s transition economies (ECA), all covered farm products, 1960 to 2007 (percent, averaged using weights based on the gross value of production or consumption at undistorted prices)

- (a) Nominal rates of assistance

<!-- Start of picture text --> 10<br>1960-64 1970-74 1980-84 1990-94 2000-04<br>-10<br>-30<br>Africa Asia LAC ECA<br><!-- End of picture text -->

# (b) Consumer tax equivalents

<!-- Start of picture text --> 10<br>1960-64 1970-74 1980-84 1990-94 2000-04<br>-10<br>-30<br>Africa Asia LAC ECA<br><!-- End of picture text --> Source: Anderson and Valenzuela (2008).
qualifications (university and diploma) increased by more than 190 percent, with the proportion of workers with university degrees increasing by almost 4 times. The Philippines, prior to 1990 was the most educationally advanced among the countries examined, with about 10 percent of its working population having university degrees by 1988. Subsequently, the proportion of workers with upper secondary or higher qualifications increased further, albeit at a slower rate compared to Indonesia and Thailand, having started from a higher base.

Vietnam faced considerable constraints to competitiveness and growth due to insufficient education and labor skills in its workforce. The proportion of university educated working population increased from 1.8 percent in 1992 to about 3.6 percent in 2006, while the corresponding increase for those employed for wages (i.e., excluding the selfemployed) was from 6.7 percent in 1992 to about 13 percent in 2006. Smaller increases are observed in upper secondary qualifications.

Table 1: Percentage change in highest education qualifications over time (workers with <u>positive reported earnings)</u>

|_Education_||||% change||||
|---|---|---|---|---|---|---|---|
||Indonesia<br>1994-07|Philippines<br>1988-06|Thailand<br>1990-04|Vietnam<br>1992-06|Cambodia*<br>1997-07|China**<br>1999-05|Mongolia<br>1998-2007|
|Years of schooling|29.3||23.8|15.6|5.1|4.6|-|
|Less than primary complete|-69.8|-33.7|-32.9|-33.2|4.0|-59.3|-|
|Primary|-32.7|-30.9|31.4|-12.1|2.3|-45.0|31.2|
|Lower secondary|24.6|5.3|45.2|11.6|-12.2|-16.6|9.8|
|Upper secondary general|59.3|45.0|100.0|20.2|-22.1|19.3|59.7|
|Upper sec. voc./higher voc.|-9.6|-|14.7|42.2|-|-|180.6|
|Tertiary|190.3|37.1|98.4|94.0|65.0|15.2|-44.5|

* Registered declines in the proportion of workers with secondary qualifications between 2003-4 and 2007 in Cambodia are probably attributable to differences in the composition of the sample of workers by type of employment. ** Urban areas only.

Establishing the evolution of educational attainment in Cambodia and comparing it to other countries in SE Asia proved problematic: first, the time frame (1997-2007)<sup>2</sup> is shorter and does not extend to the early 1990s; second, comparison between cross sections is problematic, as the composition of the (smaller) 2007 sample of workers with

> 2 Pending acquisition of the 1993 CSES.

6