rafmacalaba/datause-encoder-data huggingface dataset:
# **How have employment and incomes responded to this strong growth performance?**

Table 4 shows how the labor force has evolved since 1999.

**Table 4: GHS 1999 - 2006 - Sample Population aged 15 - 65 not in Schooling (in percent, weighted)**

|**Labor Force Status**<br>|**1999**|**2004**|**2006**|
|---|---|---|---|
|**Not in the Labor Force**|**25.3**|**23.0**|**25.2**|
|**In the Labor Force**<br>|**74.7**|**77.0**|**74.8**|
|**Unemployment Status**<br>|**1999**|**2004**|**2006**|
|**Employed**<br>|**97.8**|**97.0**|**97.4**|
|**Unemployed**<br>|**2.2**|**3.0**|**2.6**|

_Source: Francis Teal / Luke Haywood NLSS 2003-2004 and GHS 1999-2004_ 14. A key feature of Nigeria’s working-age population is the high share (approximately onefourth) of the population that is not in the labor force. As in other African countries, formal unemployment (measured as job seekers who cannot find a job) is extremely low. The vast majority of people outside the labor force consist of individuals who are either discouraged job seekers or who have not embarked upon a job search, as they do not consider the prospects to be promising. The share of people outside the labor force is a more suitable indicator of unemployment than the official unemployment rate, which consists of individuals that are looking for, but are unable to find employment. Nonetheless, actual unemployment will be less than 25 percent given that there are individuals who are not in the labor force that are genuinely not interested in work. However, given the pervasive poverty in Nigeria, that figure is not likely to be high.

15. Table 4 shows that in spite of the high growth performance, the share of the population that is not in the labor force has remained broadly unchanged. That means that the number of jobs has risen broadly in line with the labor force and that unemployment has remained basically unchanged.

16. Table 5 shows the evolution of employment broken down by family agriculture, nonagriculture self-employed (i.e., mostly urban) and wage employment.

9
impact of the rise in prices on poverty, and we interpret the results obtained when factoring in a proportional increase in incomes for net sellers or producers as a lower bound of the impact.

Table 1 provides the countries for which the estimations have been prepared. The data have been collected from the most recent available household survey for each country. The survey years range from 2003 in Guinea to 2007 in Liberia, so the data can reasonably be considered as accurately capturing the current consumption patterns of the population in the respective countries. The table includes the list of food items considered for the analysis in each country. The analysis is focused for the most part on rice, flour and bread, maize, vegetable oil, sugar, and milk, because these are food items that tend to be imported to a substantial extent, so that likely poverty impacts may be substantial (since there are no compensating impacts on the producer side). In some countries however, we consider also additional items, such as cassava and plantain in the Democratic Republic of Congo.

The fact that we consider different food items for the simulations in different countries implies that the poverty impacts estimated need not be strictly comparable, as typically we would tend to have higher estimated impacts in countries where we consider a larger number of food items so that these food items typically represent a larger share of total consumption. Thus, we certainly do not claim comparability between countries, but nevertheless the analysis does suggest some interesting results, including differences between countries in terms of the rough magnitude of the impacts that could be expected. For example if countries are highly dependent on rice imports for their food consumption they may well suffer more from increases in prices.

Finally, we consider here the impact of an increase in food prices of 25 percent and 50 percent. To have some consistency in the results, the same price increases are considered for the various countries, even though the actual price increases may be different in the various countries. The detailed country papers provide more simulations with varying degrees of price increases. The idea in those detailed country papers is to provide a sufficient number of brackets of price increases so that it is easy to approximate the impact for different actual price increases in any given country at different points in time, taking into account changes in prices that may occur over time. While the poverty impacts need not be linear in the level of the price increase, they are nevertheless for practical purposes monotonic in most cases, so that they can still be roughly interpolated from generic data provided for various levels of price increases.

6
**Oi,** Jean C 1999. "Two decades of rural reform in China: An overview and assessment." _China Quarterly 159,_ 616-628

Putterman, L and G.L.Skillman 1993. "Collectivization and China's Agricultural Crisis." _Journal of Comparative Economics_ 17(2):530-539.

Rozelle, S. and G. Li. 1998. "Village Leaders and Land-Rights Formation in China." _American Economics Review_ 88(2): 433-38 Schwarzwalder, B., R. Prosterman, Y. Jianpmg, J. Riedmger, and L. Ping 2001, An Update on China's Rural Land Tenure Reforrs: Analysis and Recommendations Based on a 17-Province Survey, Rural Development Institute, Seattle, mimeo Turner, M. A., L. Brandt, and S. Rozelle. 1998. Property Rights Formation and the Organization of Exchange and Production in Rural China. (mimeo).

Yao, S. J. 1999. "A Note of the Causal Factors of China's Famine m 1959-1961." _Journal of Political Economy_ 106(6):1365-1369 34
**Table 1: Overview of the data available**

|**St**|**Ele**|**ctricity**|**Water &**|**Sanitation**|**Telecom**|**munication**|
|---|---|---|---|---|---|---|
|**ecor**|**Generation**|**Distribution**|**Water**|**Sanitation**|**Fixed**|**Mobile**|
|**_Product Definition_**|**Installed capacity**<br>**(MWs)**|**Number of customers**<br>**(connections)**|**1.**<br>**Urban po**<br>**2.**<br>**Countr**|**pulation covered (*)**<br>**y population (**)**|**Number**<br>**li**|**of working**<br>**nes**|
|**_Number of Players* _**|16 firms|12 firms|7 firms|7 firms|9 firms|9 firms|
|**_Number of Countries in sample_**|||||||
|**OECD**|19|12|23|20|18|25|
|**No OECD**|38|19|33|18|17|40|
|**Total**|57|31|56|38|35|65|
|**_Market Shares:*_**|||||||
|**World**|18%|31%|23%(**)- 34%(*)|23%(**)- 31%(*)|39%|37%|

* The market share represents the average of the sample market but only in countries in which the firms of the sample have an activity. The standard deviations are given for regional and country measures.

First it shows that we tried to report data on the sectors as unbundled as possible. Second, for the electricity and telecommunications, we managed to get market data close enough to what a competition agency would consider appropriate. For water and sanitation, we relied on two proxies: urban population and total population. The first probably gives a lower bound and the second an upper bound of the populations affected by the large operators covered by the database. We should thus get respectively a further level of upper and lower bound for the concentration index from these two measures. Third, the country coverage is relatively evenly distributed between OECD and non-OECD countries which ensures a reasonable sense of robustness when comparing the two country groups. Finally, the combination of the market shares and of the number of players suggests that there is no outrageous domination of any sector by a very limited number of actors and hence that the approximation method used here is unlikely to be very distorted at the global level.

At a more substantive level, a simple look at the line on the number of players suggests that for electricity and telecoms, the concentration issue is subject to debates since the number of firms is large enough according to the rule of thumb discussed earlier. For water and sanitation, the information on the number of firms already points to a fairly high concentration level. The rest of this section refines this first superficial assessment.

# **3.1 Electricity**

For electricity, we differentiate between the two main activities: generation and distribution—i.e. local transmission and retailing; we did not find enough information on 9
ERE W kin PaDer Series

|ide|AuAhor||Contact<br>for**pape**|
|---|---|---|---|
|WPS455 A Formal Fstirnation of the, Effect<br>of tile MFA on Clothing Exports<br>from LDCs|Junichi Goto|June 1990|M. T. Sanchez<br>33731|
|WPS456 Improving the Supply and Use of<br>EssentialD)rugsin Sub-Saharan Africa|S. D. Foster<br>|June 1990|Z. Vania<br>33664|
|WPS457 i-inancing Health Services in Africa:<br>An Assessment of Alternative<br>Approaches|Germano<sup>Mwabu</sup>|June 1990|Z. Vania<br>33664|
|WPS 58 Does Japanese Direct Foreign<br>Investment Promote Japanese Impor<br>from Developing Countries?|Kenji Takeuchi<br>ts|June 1990|J. Epps<br>33710|
|WPS459 Policies for Economic Development|Stanley<sup>Fischer</sup><br>Vinod Thomas|June 1990|WDR Office<br>31393|
|WPS460 Does Food Aid Depress Food<br>Production? The Disincentive<br>Dilemma in the African Context|Victor Lavy|July 1990|A. Murphy<br>33750|
|WPS461 Labor Market Participation, Returns<br>to Education, and Male-Female<br>Wage Differences in Peru|Shahidur<sup>R. Khandker</sup>|July 1990|B. Smith<br>35108|
|WPS462 An Aiternative View of Tax<br>Incidence Analysis for Developing<br>Countries|Anwar Shah<br>John Whalley|July 1990|A. Bhalla<br>37699|
|WPS463 Redefining Government's Role in<br>Agriculture in the Nineties|Odin Knudsen<br>John Nash|August 1990|K. Cabana<br>37946|
|WPS464 DoesAWoman's Education Affect<br>Her Husband's Earnings? Results<br>for Israel in A Dual Labor Market|Shoshana Neuman<br>Adrian Ziderman|August 1990|V. Charles<br>33651|
|WPS465 How Integrated Are Tropical Timber<br>Markets?|Panos Varangis|August 1990|D. Gustafson<br>33714|
|WPS466 Is There An Intra-Household<br>Kuznets Curve?|Lawrence Haddad<br>Ravi Kanbur|August 1990|J. Sweeney<br>31021|
|WPS467 Structural Adjustment and Living<br>Conditions in Developing Countries|Nanak Kakwani<br>Elene Makonnen<br>Jacques van der Gaag|August 1990|B. Rosa<br>33751|
|WPS468 Does the Structure of Production<br>Affect Demand for Schooling in Peru|Indermit Gill<br>?|August 1990|M. Abundo<br>36820|
|Noncontributory Pensions<br>Near-cash Transfers<br>Other|--<br>--<br>--|||
|---|---|---|---|
|Social Insurance of which||||
|Public Service Pension Fund|0.8|0.44|Yes|
|Education of which|9.4|5.1||
|Pre-school|--||No|
|Primary|--||Yes|
|Secondary|--||Yes|
|Post-secondary non-tertiary|||No|
|Tertiary|--||Yes|
|Health of which|4.5|2.4|Yes|
|Contributory<br>Noncontributory||||
|Housing and Urban|--|--||
|Subsidies of which|4.2|2.3||
|Energy of which|3.1|1.7||
|Electricity|0.4|0.2|Yes|
|Fuel|2.7|1.5|Yes|
|Food|--|--||
|Agricultural Inputs|1.1|0.6|Yes|
|Water|--|--||
|Infrastructure of which|6.2|0.0||
|Water and Sanitation|0.5|0.3|No|
|Rural Electrification|0.1|0.0|No|
|Rural Roads|5.6|3.0|No|
|Defense; Public Order; Safety Spending|3.2|1.8|No|
|Other|24.4|13.3|No|

_Source_ : Republic of Zambia Annual 2015 Economic Report (2016) <u>http://www.mof.gov.zm/index.php/publicfinancialmanagement/summary/91-detailed-annual-financial-reports/15046-financialreport-2014-detailed</u> and Republic of Zambia 2016 Budget Address, both from the Ministry of Finance. http://www.mof.gov.zm/index.php/budgetdata/summary/15-budget-speeches/9751-budget-speech-2016 _Note:_ Expenditures (and revenues) included may not be fully allocated within LCMS 2015 for various reasons. See sec. 3 for allocative methods and assumptions.

Some public expenditure elements have private analogues: for example, individuals who do not belong to the public contributory pension system may contribute and receive income from a private pension fund. Though such privately-arranged goods and services are included in measures of income, we do not attempt to determine their impact on welfare or inequality as they are not part of the fiscal system. Other public expenditure elements are part of the fiscal system but cannot be allocated because of data limitations. For example, the household survey may not record who utilizes post-secondary, non-tertiary education or available budget reporting may not have a separate entry for post-secondary, non-tertiary education expenditures (or both limitations may be present).

6
During the 1990s, many developing countries embraced financial globalization and, in particular, welcomed foreign banks into their banking sectors. Micco, Panizza, and Yañez (2006) report that the average level of foreign bank participation among developing countries (as measured by the share of assets held by foreign banks) rose from 18 to 33 percent between 1995 and 2002.<sup>1</sup> Arguably, nowhere has the increase in foreign bank participation been more dramatic than in the case of Mexico. Over this period, the share of assets held by foreign banks rose from 2 to almost 82 percent. Mostly, the increase in foreign bank participation in Mexico resulted from foreign acquisitions of domestic banks, as opposed to de novo foreign bank entry. By 2005, foreign bank participation was close to 83 percent of assets and five of the top six banks in the system had been acquired by foreigners.

This study examines how banking sector outreach or breadth – i.e., the extent to which the banking sector caters to a large percentage of the population throughout the country – changed during a period of drastic increase in foreign bank presence, driven by foreign acquisitions. Since we do not have information on the actual share of the population that has access to or uses banking services, we employ a number of proxy measures of outreach. In particular, we track the behavior of the number or share of municipalities where banks are present and the number of branches, loans, and deposit accounts. First, using quarterly countrylevel data we assess how the share of municipalities with branches, the number of branches, deposit accounts, and loan accounts per capita at the country-level changed as foreign bank participation increased due to foreign acquisitions. We also separately examine changes in outreach for domestic and foreign banks to determine whether domestic banks off-set foreign bank behavior. Second, using bank-level data, we examine how foreign bank outreach changed after foreign acquisitions. Again, we focus on the number of municipalities with bank presence,

> 1 Their sample covers 104 developing countries across all regions.

1
<!-- Start of picture text --> Annex I<br>35,000 35,000 Annex I<br>Non Annex I<br>Non Annex I<br>30,000 30,000<br>Global<br>Global<br>25,000 25,000<br>20,000 20,000<br>15,000 15,000<br>10,000 10,000<br>5,000 5,000<br>0 0<br>(a) Scenario 1 (b) Scenario 2<br>Million Metric Tons CO2 Million Metric Tons CO2<br>1980 1990 2000 2010 2020 2030 1980 1990 2000 2010 2020 2030<br><!-- End of picture text --> We also examined a question: how much CO2 reductions would be required from Annex I countries if they were alone to take responsibilities of reversing the global trend of CO2 before 2030. If Non-Annex I countries do not reduce their CO2 emissions and if global CO2 emissions path has to be maintained as specified under Scenario 1, Annex I countries, as a whole, require to limit their CO2 emissions 18 percent and 37 percent below from 1990 levels in 2025 and 2030, respectively. Similarly, to attain the global CO2 emissions path as specified under Scenario 2 in the absence of non-Annex I countries mitigation efforts, Annex I countries, as a whole, need to cut their CO2 emissions 15 percent, 33 percent and 52 percent below from 1990 levels in 2020, 2025 and 2030 respectively.

Table 5 presents the levels of CO2 reductions in various countries/regions from respective BAU cases to achieve the scenarios considered. To cause the global CO2 emissions declining stating from 2025 (i.e., Scenario 1), Annex I and non-Annex countries’ total emissions are required to be lowered by 42 percent and 10 percent

-25-
respectively, and extrapolating years before 1963 and after 1980. Simple proportional inter- and extrapolations were applied. The result - a far cry of course from the ideal indexes - is shown in Table 4.1, col.l. A corresponding exercise for the aggregate of manufacturing and mining produced the series presented in col.2. The two series are very similar, partly because mining is a much smaller sector than manufacturing, partly because nominal wage developments, contrary to the impression conveyed by the SII data,have been quite similar in the two sectors. I propose to use the lacter series in attempts to establish a Phillips relation. It is the broadest, well documented series outside government we can come up with for the whole period 1960-85. It would be a mistake, on the other hand, to take it for granted that this series is representative of a general, non-government sector wage rate.

ii) Labor market irndicators. Several such indicators are available. They were discussed in Section 1. The SPO estimates are clearly biased. The urban labor force surveys, however useful for judging other labor market indicators, are available for only seven years from 1967 to 1985 and do not cover the critical period 1970 to 1981. The series for registered unemployed is based on very small numbers until around 1975 and may for later years reflect government programs and their administration and use more than unemployment. We have further a choice between our two auxiliary placement office series and a general activity

- 64 -
Somewhat paradoxical, over the past decades, Guatemala has experienced relative macroeconomic stability. Guatemala has a rather low level of external indebtedness, inflation has been held back, and after a process of (uncompleted) structural reforms the economy is now fairly open and with low levels of protection. Thus, contrary to other Latin American countries, macroeconomic mismanagement may presumably not be regarded as the main factor to understand Guatemala’s modest performance in terms of per capita growth. Rather, other issues undermine Guatemala’s long-run growth patterns.<sup>7</sup> In addition to the factors already mentioned, one is low human capital endowment.

**Table 1. Guatemala: Sectoral Output Growth, 1951-2003** (in percent)<sup>a/</sup>

|Sector|1951-03|1951-75|1976-85|1986-03|
|---|---|---|---|---|
|Primary|3.2|4.2|1.6|2.7|
|Agriculture, forestry, livestock and fishing|3.1|4.2|1.5|2.6|
|Mining and quarrying|8.1|3.3|16.9|9.5|
|Industry|4.3|5.6|2.8|3.2|
|Manufacturing|4.0|5.9|2.4|2.2|
|Construction|4.0|3.9|5.4|3.9|
|Gas, electricity and water|8.4|9.7|6.0|8.2|
|Services|4.2|5.0|2.5|3.9|
|Wholesale and trade|3.8|5.0|1.3|3.3|
|Transport, storage and communications|6.2|7.5|3.4|5.9|
|Banking|6.9|8.3|6.1|5.3|
|Public administration and defense|4.6|4.5|5.6|4.5|
|Other services|3.4|4.2|2.4|2.9|
|Total GDP growth|3.9|4.9|2.3|3.5|

_Source:_ Author’s calculations based on data from Banco de Guatemala. a/ Information for 2002 (2003) is preliminary (estimated).

The current human capital base is essentially a product of past agricultural growth and eminently anti-distributional policies. The World Bank (2003a) and UNDP (2002) document that insufficient cheap labor, in particular for coffee, was the main barrier for the expansion of export crops during earlier periods. Hence, in order to create a low-wage labor force, the campesino and indigenous society was excluded from education. The plantation economy that resulted provided little incentives to accumulate human capital. Historically, the low level of schooling is also an outcome

> 7 During a very brief episode in the early 1990s, inflation increased and fiscal discipline eroded. More recently, the World Bank (2003b) presents arguments suggesting that short-run growth _may_ be related to the cyclical stance of fiscal and monetary policies.

-6-
**<u>Table 2: Area transferred to and cultivated by commercial farms in different regions</u>**

||**Ar**<br>**Transferred**|**ea**<br>**Cultivated**|**Tigray**|**Afar**|**Amhara**|**Oromia**|**Somali**|**Benish’gl**|**SNNP**|**Gambella**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Total (ha)**|1,768,540|1,008,809|200,310|70,651|186,360|284,141|1,487|70,343|142,019|53,498|
|**...**ha**/**farm|267.5|152.6|195|239.1|100.4|405.9|24.8|86.9|84.2|317.9|
|**By Year**|||||||||||
|Before 91|216,278|155,873|107.5|74|133.1|1333.8|22.7|22|114.8|86.3|
|1991-92|71,703|36,369|91.1||114.9|51|||138.7||
|92-2002|492,181|254,134|339.6|191.2|123.8|244.2|3|146.3|97.9|280.8|
|02-2006|293,027|158,101|105.7|501.6|89.4|231.3|14.5|131|116.8|260.4|
|07-2008|394,800|227,945|142.7|275|132.9|276.8|3.3|124.5|65.4|526.4|
|09-2010|<br>180,043|<br>104,569|275.4|132.3|85|165.4||59.3|67.2|245.7|
|11-2013|120,508|71,816|90.9|166.3|68.5|292.5|120.3|43.5|88.1|78.5|
|**By Size (ha)**|||||||||||
|< 20 ha|45,409|23,921|29.5|166|40.4|126.7|66.9|9|24.1|141|
|20-50|252,779|129,940|188.1|133.4|51.9|131|16.3|85.9|43.4|51|
|50-100|329,036|176,438|90.9|131.7|76.4|306.2|51|39.2|37.3|182.3|
|100-500|682,137|396,692|264.9|489.4|161.5|319.6|179|52.1|115.8|397.3|
|500-1000|155,970|88,106|628.1|1115|498.1|916|2|157.1|320.9|279|
|>1000 ha|303,211|193,712|1762|514.7|1497.3|3633.1||697|382.3|577|
|**By owner's n**|**ationality**||||||||||
|Ethiopian|1,570,323|859,211|192|238.7|98.3|304.6|24.8|87|79.4|292.7|
|Foreign|80,445|47,677|1441||476.5|497.7||57.2|143.4|2,212.3|
|<br>Joint|<br>11,989|<br>7,087||||198||148.4|200||
|**By ownership**|**type**||||||||||
|Government|175,760|120,658|31.0|15,638.1|320.8|1,509.3||21.5|220.9|101.4|
|Private|1,495,524|833,108|194.0|202.7|97.4|298.5|31.7|89.8|78.2|337.8|
|Cooperatives|87,440|49,276|232.1|519.0|170.8|243.8|8.9|56.7|191.4|60.0|
|Others|9,815|5,767|483.8|||177.1||102.0||1,593.7|
|**By main crop**|**cultivated**||||||||||
|Maize|165,995|93,493||41.6|182.5|209.8|9.1|73.3|70.1|6.9|
|Sorghum|<br>117,612|<br>62,316|71.9|58|74.2|163.7|8.6|76|29|13.3|
|Wheat|118,816|85,419|41||262.2|670.7|15.1||23.9|0.8|
|Sesame|607,417|314,268|290.1|18.9|83.1|160.8||51.4|28.4|18.4|
|Coffee|209,152|124,579|||204|245.1|||92.3|219.3|
|Cotton|263,526|163,442||253.3|369|326.3||1125.9|703.2|1,069.8|
|Other|286,021|165,293|57.1|528.5|216.2|854.5|43.6|84.2|47.6|69.1|
|**By mode of o**|**riginal land acq**|**uisition**|||||||||
|Direct neg.|125,118|64,669|141.7|208.2|106.7|227|39.5|64|51.2|235.6|
|<br>Woreda|759,636|396,857|185|568.8|89.1|147.9|6.8|54.4|64.3|53|
|Region|585,337|342,313|348.1|89.9|136.4|468.7||93|83.9|440.3|
|Fed’l Gov’t|175,203|100,952|4158|1257.4|549.2|670.8|120.3|302.7|188.8|310.3|
|No. of obs.|6,612|6,612|1,027|296|1,857|700|60|810|1,686|168|

_Source:_ Own computation from 2013 CSA large farm survey 16