rafmacalaba/datause-encoder-data huggingface dataset:
countries because PovcalNet does not yet use spatially adjusted welfare aggregates for many countries. Finally, the issue of negative and zero incomes is only relevant for countries of the European Union and Latin American and Caribbean (LAC). In LAC, negative incomes are recoded as missing and therefore, the EUSILC database was used to test the impact of negative incomes, while LAC countries were used to test the impact of zero incomes. The sections below investigate how changes in these particular adjustments may affect the shared prosperity index.

# **3.1. Grouped versus Microdata**

There is a systematic difference between estimates using grouped and microdata of mean consumption per capita of the bottom 40 percent. In almost all cases, except the first year in Armenia and the second year in Peru, means from grouped data are lower than means from microdata. The difference ranges from a very small negative 0.12 percent in the case of Iraq to negative 1.41 percent in the case of Rwanda. Given that the impact of using grouped data affects both means in two periods of time, the indicator of shared prosperity is not expected to be very much affected. In a way, the effect is similar to multiplying means in the shared prosperity formula by a constant number, which should not affect the rates of growth.

**Table 2. Grouped and microdata based daily mean consumption per capita of the bottom 40 percent of the population in $/PPP 2005 and difference between them in percent**

||**micro**|**grouped**|**micro**|**grouped**|**differen**<br>**(grouped/m**|**ce in %:**<br>**icro‐1)*100**|
|---|---|---|---|---|---|---|
||**b40 consu**|**mption, t1**|**b40 consu**|**mption, t2**|**t1**|**t2**|
|Rwanda|14.1|13.9|17.7|17.5|‐1.4|‐1.2|
|Colombia|64.7|64.0|83.2|82.4|‐1.1|‐1.0|
|Madagascar|12.8|12.6|10.1|10.1|‐1.0|‐0.8|
|Dominican Republic|78.2|77.5|87.7|87.2|‐0.9|‐0.6|
|Cambodia|32.1|31.8|45.6|45.2|‐0.9|‐0.7|
|Brazil|79.2|78.5|104.5|102.9|‐0.9|‐1.5|
|Bolivia|47.9|47.5|87.2|86.5|‐0.7|‐0.9|
|Congo|18.3|18.1|27.7|27.6|‐0.7|‐0.3|
|Sri Lanka|51.8|51.5|56.5|56.2|‐0.7|‐0.5|
|Russia|120.0|119.5|189.5|188.1|‐0.5|‐0.7|
|Kazakhstan|96.7|96.2|122.8|122.4|‐0.4|‐0.3|
|Indonesia|36.0|35.8|39.5|39.5|‐0.4|0.0|
|Paraguay|64.3|64.1|92.5|92.3|‐0.4|‐0.1|
|Pakistan|36.2|36.1|42.0|41.9|‐0.4|‐0.2|
|Tunisia|88.1|87.8|104.5|104.1|‐0.3|‐0.3|
|West Bank and Gaza|133.2|132.8|149.1|148.6|‐0.3|‐0.4|
|Peru|69.1|68.9|101.4|101.6|‐0.2|0.2|
|Spain|431.9|431.3|354.2|352.8|‐0.1|‐0.4|
|Iraq|57.1|57.0|58.1|58.0|‐0.1|‐0.2|
|Armenia|60.5|60.6|61.9|61.8|0.1|‐0.1|

Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations. Here and thereafter, the POVCAL<sup>11</sup> command was used to get grouped data estimates. Notes: Grouped data are based on ventiles.

Shared prosperity estimates calculated from micro and grouped data are shown in figure 5. Overall, the differences are not substantial in absolute terms (third column in the table of figure 5). The largest differences in grouped and microdata growth rates are observed for Peru followed by Brazil. However,

> 11 POVCAL is developed by Qinghua Zhao who kindly shared it with the team.

9
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent, the Central African Republic and Chad are in the order of 30 percent, and Sierra Leone is close to 40 percent.

There is also very limited mobile penetration in South Sudan. Household surveys from 2009 suggest that only 18 percent of South Sudan’s households owned a cell phone. In contrast, over a third of the population in Sudan owns a cell phone. Estimates for 2011 set mobile ownership up to 25 percent of the population still below African averages.

South Sudan records a low 7 users of the Internet per 100 people (table 16). Very low literacy rates in part drive low Internet usage. Further, less than 1 percent (0.7 percent) of the population owns a computer.

**Table 16. Benchmarking South Sudan’s ICT performance indicators**

|Indicator|Units|South<br>Sudan|Resource-<br>rich<br>countries|East Africa|Low-<br>income<br>countries|Sub-<br>Saharan<br>Africa|
|---|---|---|---|---|---|---|
|||Various<br>years|2008|2008|2008|2008|
|**Access**|||||||
|GSM coverage|% population under signal||78|0.02|63|72|
|International bandwidth|bits/person||18|11|25|39|
|Internet|users/100 people||9|5|3.5|5.5|
|Landline|subscribers/100 people|0.11|0.9|0.24|0.8|1.4|
|Mobile phone|subscribers/100 people|12|38.2|21|24.4|33.1|

_Source:_ Data for landline subscribers is from 2005 taken from the Central Bureau of Statistics South Sudan; Mobile phone subscriptions based on World Bank staff estimates for 2011; Internet users is for 2011 taken from Infoasaid (2011). _Note:_ ICT = information and <u>communication technology; GSM = global system for mobile communications.</u> Consumers pay rather high prices for the ICT services that they have access to. Access to international ICT networks is via microwave links to satellite connections in neighboring countries. Gemtel has a microwave link to Uganda from where it connects to the fiber network of Uganda Telecom. International bandwidth is very limited in South Sudan and, as a result, prices are very high. Zain charges about 250 SDG for the modem and 50 SDG per month for a 2 GB connection. Vivacell and MTN also offer Internet services at roughly similar rates. VSATs<sup>6</sup> are far more expensive, costing anywhere between $500 and $4,000 a month depending on specifications.

The quality of existing service is also rather poor. Mobile networks are still embryonic in nature. As a result, mobile phone users often have multiple SIM cards to combat patchy services and poor interconnections. Internet connectivity is also rather poor because modems link to the Internet via the mobile phone network, but connection speeds remain slow (Infoasaid 2011).

Institutional obstacles imposed by the governments of Sudan and South Sudan have stymied the growth of the telecommunications market in the past. Despite a demand for fixed-line telephones, licensing arrangements precluded growth in landline connections. In 2005 total capacity of the telephone exchange was almost 9,400 customers and registered customers were as many as 8,856. The neglect of

> 6 Very small aperture terminal.

36
Tanzania; cocoa, synthetic spinning fiber and wool from Ghana; and alcohol from South Africa.<sup>7</sup> Although China’s thirst for African oil (especially from Nigeria, Sudan and Angola) is well-reported, it has to be mentioned that the 674 Chinese state companies involved in Africa have also invested in booming sectors such as copper mines, fishing, construction and telecommunications. In 2004, Chinese investments represented $900m of the $15bn of FDI in Africa.<sup>8</sup> Kaplinsky et al point out that there is a danger of overestimating the historic and present impact, and underestimating the potential future impact of China’s rise on Sub-Saharan Africa. By creating a synthetic framework distinguishing three primary channels of transmission – trade, FDI, and aid – Kaplinsky allows us to focus on the “complementary-competitive” dimensions as well as on the direct and indirect impacts. This primary focus of this paper is on trade links.<sup>9</sup> <u>Table 1: Kaplinsky’s Synthetic View of the China-Africa Trade Channel</u>

|||**Direct**|**Indirect**|
|---|---|---|---|
|**Trade**|**Complementary**|-inputs for industries<br>-cheap consumer<br>goods|-higher global prices<br>for SSA exports|
||**Competitive**|-displacement of<br>existing and potential<br>local producers by<br>cheaper Chinese<br>producers|-Competition in<br>external markets –<br>falling prices and<br>falling market shares|

<u>Source: Kaplinsky et al, “The Impact of China on Sub Saharan Africa,” April 2006, p. 25</u> Table 1 above sums up the threats and opportunities facing Africa caused by this growing Asian interest. On the one hand, there are direct and indirect ‘complementary’ effects of Africa-Asia trade, which should be seen as opportunities. But on the other hand, there are direct and indirect ‘competitive’ effects which might threaten Africa’s future economic development.

# _B. Threats_

It is useful to keep in mind that China has predominantly imported only a limited number of products – mostly oil, minerals and precious metals – and that from a small number of Sub-Saharan countries. In return, it primarily exports

> 7 Ibid. and UN Comtrade Database, UN Statistics Division, 2006 – own calculations

> 8 Jean-Christophe Servant, “China’s trade safari in Africa,” _Le Monde Diplomatique_ , May 2005. Online available at : <u>http://mondediplo.com/2005/05/11chinafrica</u>

> 9 Kaplinsky, McCormick and Morris, “The Impact of China on Sub Saharan Africa _,_ ” _Institute of Development Studies_ , University of Sussex, April 2006. Available online at: <u>http://www.ids.ac.uk/ids/global/AsianDriverpdfs/DFIDAgendaPaper06.pdf</u>

5
# **6. References**

- Abebe, Girum, A. Stefano Caria, Marcel Fafchamps, Paolo Falco, Simon Franklin, and Simon Quinn. 2021. "Anonymity or Distance? Job Search and Labour Market Exclusion in a Growing African City." _The Review of Economic Studies_ 88 (3):1279-1310.

- Abebe, Girum, Stefano Caria, Marcel Fafchamps, Paolo Falco, Simon Franklin, Simon Quinn, and Forhad Shilpi. 2023. Matching Frictions and Distorted Beliefs:Evidence from a Job Fair Experiment. Queen Mary University of London, School of Economics and Finance.

- Abel, Martin, Rulof Burger, and Patrizio Piraino. 2020. "The Value of Reference Letters: Experimental Evidence from South Africa." _American Economic Journal: Applied Economics_ 12 (3):40-71.

- Agarwal, Neha, and Subha Mani. 2024. _New Evidence on Vocational and Apprenticeship Training Programs in Developing_ . Edited by Utteeyo Dasgupta and Pushkar Maitra, _Handbook of Experimental Development Economics_ .

- Agte, Patrick, Arielle Bernhardt, Erica M. Field, Rohini  Pande, and Natalia Rigol. 2022. "Investing in the Next Generation: The Long-Run Impacts of a Liquidity Shock." _(No. w29816) National Bureau of Economic Research._

- Akresh, Richard  , Damien   de Walque, and Harounan  Kazianga. 2016. "Evidence from a Randomized Evaluation of the Household Welfare Impacts of Conditional and Unconditional Cash Transfers Given to Mothers or Fathers." _World Bank Policy Research Working paper_ 7730.

- Alfonsi, Livia, Oriana Bandiera, Vittorio Bassi, Robin Burgess, Imran Rasul, Munshi Sulaiman, and Anna Vitali. 2020. "Tackling Youth Unemployment: Evidence From a Labor Market Experiment in Uganda." _Econometrica_ 88 (6):2369-2414.

- Alzúa, Maria Laura, Soyolmaa Batbekh, Altantsetseg Batchuluun, Bayarmaa Dalkhjav, and José Galdo. 2021. "Demand-Driven Youth Training Programs: Experimental Evidence from Mongolia." _The World Bank Economic Review_ 35 (3):720-744.

- Amin, Sajeda, J. S. Saha, and J. A. Ahmed. 2018. "Skills-Building Programs to Reduce Child Marriage in Bangladesh: A Randomized Controlled Trial." _J Adolesc Health_ 63 (3):293-300.

- Andrew, Alison , Sonya  Krutikova, Gabriela Smarrelli, and Hemlata Verma. 2022. "Gender Norms, Violence and Adolescent Girls' Trajectories: Evidence from a Field Experiment in India." _(No. 22/41). IFS Working Papers._

- Araujo, M. Caridad , Mariano  Bosch, and Norbert Schady. 2018. Can Cash Transfers Help Households Escape an Intergenerational Poverty Trap? In _The Economics of Poverty Traps_ , edited by C. Barrett, M.R.  Carter and J.P. Chavas: Unviersity of Chicago Press.

29
Table 5 – Ordered logistic regression for Risk Level

|**Mexico**|High Risk<br>Level|High Risk<br>Level|
|---|---|---|
|Low parent education (1)|0.48<br>(0.012)**|0.485<br>(0.012)**|
|Being rural (rural =1, urban=0)|0.401<br>(0.023)**|0.303<br>(0.023)**|
|Age||0.135<br>(0.003)**|
|Gender (female=2, male=1)||0.128<br>(0.019)**|
|Observations|37903|37903|

Standard errors in parentheses * significant at 5%; ** significant at 1%

# **Figures**

Figure 1a. Cluster Group Means – Mexico <!-- Start of picture text --> Mexico Clusters<br>Type III Type II Type I Type 0<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Behaviors Protective Risk Factors Rural (binary) Low parental<br>Average Factors Average Average Factors education<br>Group Means<br><!-- End of picture text --> Figure 1b. Cluster Group Means – Chile <!-- Start of picture text --> Chile Clusters III II I 0<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>Behaviors / Protective Risk Factors live in a rural indigenous low economic low parental<br>Consequences Factors Average area class (nse) education<br>Factors<br>Average Average<br>Group Means<br><!-- End of picture text --> _Note_ : The values of the group means are on a 0 to 1 scale as described in the variable construction section. Thus, the relative scale of the means between the different x values (primarily factors) is not interpretable. What is important is the relationship between the graphs for each risk level. As seen above, the Type I and 0 behaviors have a higher mean than the Type II and the Type II behaviors have a higher mean than the Type III.

38
The average employment ratio for CEE is presently about 64 percent, virtually the same as for the European Union (before the enlargement) and for the OECD. In the CIS it is even higher, above the OECD average. This somewhat surprising observation reflects the fact that under central planning the employment ratio tended to be at an unusually high level. The transition has been thus associated with a movement of employment rates towards a level characteristic of market economies. Such process is called a regression toward the mean. In many transition economies the current employment rates may be close to the equilibrium rates, which implies that there will be no return to the high employment rates prevailing before the transition.

Obviously, there is a substantial variation in employment ratios across countries. On the one hand, there are transition economies where the ratio is around 70 percent, which is high by international standards. These include the Czech Republic, Estonia, Lithuania in CEE, Belarus and Russia, and Azerbaijan and Kazakhstan among the other CIS (Figure 2).

# **Figure 2 Employment rates vary among transition economies**

## **Change in the Employment Rate, 1989-2001**

<!-- Start of picture text --> 0<br>CEE BRU 0ther CIS<br>-2<br>-4<br>-6<br>-8<br>1995-2001<br>1989-1995<br>-10<br>-12<br>-14<br>-16<br>-18<br>percentage points<br><!-- End of picture text --> Source: UNICEF TransMONEE database; Bank staff calculations On the other end of the spectrum are countries such as Bulgaria, Poland and Slovakia among the CEE countries, and Armenia and Tajikistan among the CIS, where the employment ratio is significantly below the OECD average.

# **High employment rates in slowly restructuring countries may not be sustainable**

High employment rates may imply a successful transition (Estonia is probably a good example), but may also imply slow and delayed restructuring. In the latter case many existing jobs are of low productivity and probably are not viable in the longer-run. The symptoms indicating the existence of low-productivity and non-viable jobs include high prevalence of wage 5
Policy Research Working Paper 6093

# **Abstract**

The very poor in developing countries often make intertemporal choices that seem at odds with their individual self-interest. There are many possible reasons why. This paper investigates several of these reasons with a lab-in-the-field experiment in rural Malawi involving large stakes. It makes two contributions. First, it constructs a new dependent variable: revisions of prior choices regarding the allocation of future income. This allows us to directly examine intertemporal choice revision and its determinants. In particular, this dependent variable permits a novel test for the existence of self-control problems. It turns out revisions of money allocations toward the present are positively associated with measures of present-bias from an earlier baseline survey, as well as the (randomly assigned) closeness in time to the first possible date of money disbursement. Second, the paper investigates other potential determinants of revision, aside from self-control problems. It finds little evidence that revisions of money allocations toward the present are associated with spousal preferences for such revision, household shocks or the financial sophistication of respondents.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xgine@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# I. Introduction

For quite some time, global food security issues have been in the center of a policy debate in the economic literature. One of the major aspects of this debate has been the role of China, a giant economy currently with a population of 1.30 billion, 20% of the world’s population, and with expected population growth rate of 1.2-2.3 percent per year into the next decade (CIA 2007). China’s share in the world’s production of primary agricultural commodities is significant, mainly in grains, soybean, and cotton. In 2003, China’s share was 15, 30, 17, 19, and 31 percent for wheat, rice, maize, soybean, and cotton, respectively (Winters and Yusuf, 2007:16). According to Census data (CNBS, 2001), shares of these crops in five of China’s provinces (Hebei, Henan, Shandong, Anhui and Jiangsu), considered the bread basket of China, range between 70-80 percent of the area sown.

With projected increases in population and standard of living in China, feeding larger numbers of more affluent people could become a challenge if not accompanied by increased supply (Paarlberg (1997). Several studies provide grain production projections into the not so distant future, but variations in the estimates are quite wide. Fan and Agcaoili-Sombilla (1997) compare several studies with projections of grain production in China (Brown 1995; Rosegrant, Agcaoili-Sombilla, and Perez, 1995; Huang, Rozelle, and Rosegrant 1997; Tuan 1994; Mitchell and Ingco, 1993; and OECF, 1995). The reasons for the differences in projections among these studies are beyond the scope of this paper. However, one common feature of all these studies is that they do not take into account the potential effect of future climate change on agricultural production.

As scientific evidence becomes more convincing that rising greenhouse gases will warm the planet (IPCC 2007), it has become ever more important to understand the impacts of global warming. The impacts to the agriculture sector from climate change are among the largest and best documented. Agronomic studies suggest that crop yields may fall if the same crops are grown in the same places under various climate change scenarios (Reilly et al. 1996, McCarthy et al 2001). Studies applying the Ricardian Approach in Africa (Kurukulasuriya et al 2006) and South America (Seo and Mendelsohn 2007) suggest that warming will reduce farm net revenues. However, no single country is more important than China in terms of the number of people at risk and the impact on the world economy that may result from future climate change. Will China continue to be able to feed itself as the climate warms?

2
16 countries as South Korea, Turkey, Costa Rica, South Africa vary from US$20 to a maximum of US$ 350.3

||BO|X I||
|---|---|---|---|
|1993|(1)<br>Essential Public<br>Health Package|(2)<br>Minimum<br>Clinical Package|(3)<br>Discretionary<br>Clinical Package|
|Burden of Disease||||
|Averted (%)|14.8|14.0||
|Per capita cost (US$)|3.7|6.3|30-45|
|(Annual)||||
|2020||||
|BOD Averted(%)|18.2|25.1||
|Per capita cost (US$)|8.8|20.2||
|(Annual)||||

Note: Major interventions under each package are outlined in Technical Annex A Epidemiological and demographic data are used to calculate the benefits as disability adjusted life years saved (DALY) from interventions under essential PHP and minimum clinical package. The estimates show that interventions under (1) and (2) above packages would reduce the burden of diseases including most of the "remaining" and "emerging" diseases (Table 3 Annex 2) by about 28 percent in terms of total DALY saved observed in 1993. The itemized costs of specific interventions were provided by the MOH and Chinese Academy of Preventive Medicine (CAPM) and estimated from project preparation activities. By applying similar procedure, a rough projection shows that essential PHP and minimum clinical interventions would reduce BOD by 43 percent in 2020, an overall gain by 15 percent. If legislative measures, outside the purview of the PHP, are implemented, further reduction in the BOD from injuries and COPD would be possible.

For methodologies, see C.J.L Murray and A.D Lopez in their report, "Global Burden of Disease" Bulletin of the World Health Organization, 1990, 72 (3).

The government should increase its spending level to meet these service requirements not only because such interventions are the most cost-effective but also because of a salutary impact of government expenditure on improving health status of Chinese population, as revealed by our regression estimates with panel data. Moreover, resource requirements are modest compared with 3 These figures capture roughly 2 to 7 percent of those countries GNP. See World Development Report 1993, World Bank, Oxford.