rafmacalaba/datause-encoder-data huggingface dataset:
- 65 -

Table 4.16b: SUBNATIONAL GOVERNMENT EXPENDITURES AS PERCENTAGE OF GDP a/

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE b/|||||||||||
||7.6|7.1|7.0|7.3|6.9|7.1|7.1|7.9|7.6|7.3|
|MARKET ECONOMIESb/|10.4|10.3|9.7|9.9|9.8|10.4|10.2|10.6|10.2|9.8|

--------------------------------------------------------------------

__-------__---------------------------_jI_ Total expanditures include expenditures and lending minus repayment. b/ Weighted averages. Comparabiltty over time affected by the availability of data.

Table 4.16c: SUBNATIONAL GOVERNMENT EXPENDITURES AS PERCENTAGE OF GENERAL GOVERNMENT EXPENDITURES a/

||1975|1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLEb/|||||||||||
||24.4|23.8|22.8|23.0|23.0|23.0|21.0|20.1|19.8|20.4|
|MARKET ECONOMIESb/|||||||||||
||26.5|26.5|25.4|25.5|25.1|25.4|24.7|24.7|23.8|24.0|

------------------------------------------------------------------__--------__-----------------------------

*/ Total expenditures include expenditures and lending minus repayment. Expenditures have been consolidated. _b1_ Weighted averages. Comparability over time affected by the availability of data.

Source: IMF, Government Finance Statistics yearb_ok, various years.

4.18 Constraints to Reducing Local ExRenditures. It is quite plausible that the availability of transfer resources feeds the growth of expenditures, but it is unlikely that stricter rationing of this type of funds would achieve the desirable effect of balancing off local budgets, especially in rapidly urbanizing areas, because of the concern with maintaining a basic level of welfare for their growing populations.361 The common trends of Argentina, Chile, Colombia and Panama, (Tables 4.18 a, b, c & d in Annex) show that social expenditures continued to rise faster than those in economic sectors. The devolution of responsibilities for services like primary education or health to the lower tiers of government took place not so long ago and was based on the assuimption that resource transfers supporting these expenditures would continue to grow over time. Additional problems arise from the repayment of capital loans received in the past by local governments and from the growing needs to replace a by now obsolescent capital stock. Certain capital improvements (e.g., water supply, sanitation, drainage, power) may be no longer postponed and are unlikely to take place through private sector initiatives. Deregulations of certain services may help eliminate gross local inefficiencies or restrictive practices and legitimize informal sector provision, but its applicability is limited and ii/ The legal/constitutional statutes of most LAC countries include the public obligation to provide specific local services. Several Latin American constitutions even state explicitly as a government responsibility that of providing all citizens with "decent" housing.
Policy Research Working Paper 11015

# **Abstract**

The availability of weather data from remotely sensed Earth observation data has reduced the cost of including weather variables in econometric models. Weather variables are common instrumental variables used to predict economic outcomes and serve as an input in modeling crop yields for rainfed agriculture. The use of Earth observation data in econometric applications has only recently been met with critical assessment of the suitability and quality of these data in economics. This paper quantifies the significance and magnitude of the effect of measurement error in Earth observation data in the context of smallholder agricultural productivity. The paper shows that different Earth observation sources use different measurement methods. The findings are not robust to the choice of Earth observation dataset, and the outcomes are not simply affine transformations of one another. Thus, the paper suggests that researchers should exercise caution in using these data and include robustness checks that test alternative sources of Earth observation data.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at jdmichler@email.arizona.edu or tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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
Production possibilities are determined by an underlying technological process. The maximum amount of output possible for an amount of inputs given the constraints imposed by the underlying technical process is the "production function." The relationship between plant growth and water or sunlight or fertilizer is determined by a biological process, the relationship between coal burned and heat produced is determined by a physical process. Similarly, applying the metaphor to education one can talk of the educational "production function" which is determined by an underlying pedagogical process.

Even though the production function is derived from technical, not behavioral relationships, one needs a behavioral theory to understand the results of estimating a production function for two reasons. First, the increment to output from additional inputs is not constant. The second algebra book per student will likely help less than the first, and the tenth much less (perhaps even have a negative impact). The contribution to output of an input depends on the rate of input utilization at which it is assessed. Nearly all education production function studies are not experiments in which input use is chosen by a researcher, but rather use data drawn from reality. In reality, the spending on inputs (e.g. teacher wages, class sizes, buildings, textbook use) is chosen for reasons other than research and since input use is chosen, the theory of the input choice predicts the observed input productivity and guides the interpretation of results.

This is particular important in educational research, as there is a crucial distinction between testing whether inputs have low productivity at their current rate of application versus testing whether they are "inputs" at all. One could study the effect of either fertilizer or Mozart piano concertos on corn production. Even though at sufficiently high rates of 4
|Decision number|||(13.83)|||(13.83)|(19.82)|
|---|---|---|---|---|---|---|---|
|Treatment X||||23.96||44.3|44.3|
|Risk preference||||(151.1)||(151.9)|(151.9)|
|Treatment X|||||-1.560|-1.608|-2.516|
|Religious preference|||||(1.39)|(1.41)|(1.70)|
|Religious preference X|||||||0.339***|
|Decision number|||||||(0.11)|
|Treatment X|||||||0.140|
|Religious preference X Dec|ision number||||||(0.15)|
|Constant|2930.1***|2962.2**|3092.8***|2992.3**|2946.1**|3131.9***|3311.7***|
||(98.90)|(1195.20)|(1196.00)|(1215.40)|(1193.90)|(1214.60)|(1216.00)|
|Within R2|0.241|0.241|0.245|0.241|0.241|0.245|0.264|
|Between R2|0.000|0.071|0.071|0.071|0.082|0.082|0.082|
|Overall R2|0.165|0.187|0.191|0.187|0.191|0.194|0.207|
|P|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Observations|1464|1464|1464|1464|1464|1464|1464|

Notes: Random effects panel (GLS) regressions. Dependent variable is amount (in tokens) invested in mutual fund Y. The baseline is where the fund with fixed returns and risk is the traditional fund, the treatment is where the fund with fixed returns and risk is the socially responsible fund. * 10%, ** 5%, *** 1% significance level.
Policy Research Working Paper 7141

# **Abstract**

Poverty estimates based on cross-section data provide static snapshots of poverty rates. Although a time series of crosssection data can offer some insights into poverty trends, it does not allow for an assessment of dynamics at the household level. Such a dynamic perspective on poverty generally calls for panel data and this kind of analysis can usefully inform poverty reduction policy, notably the design of social protection interventions. Absent actual panel data for Senegal, this paper applies new statistical methods to construct synthetic panel data from two rounds of crosssection household surveys in 2005 and 2011. These data are used to study poverty transitions. The results suggest that, in marked contrast to the picture obtained from cross-section data, there exists a great deal of mobility in and out of poverty during this period. More than half the population experiences changes in its poverty status and more than two-thirds of the extreme (food) poor move up one or two welfare categories. Factors such as rural residence, disability, exposure to some kind of natural disaster, and informality in the labor market are associated with a heightened risk of falling into poverty. Belonging to certain ethnicities and factors such as migration, working in the non-agriculture sector, and having access to social capital are associated with a lower risk of falling into poverty.

This paper is a joint product of the Poverty and Inequality Team, Development Research Group; and the Urban, Rural, and Social Development Global Practice. 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 authors may be contacted at hdang@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. 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
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 7. Port indicators for Senegal compared against those of select ports in Africa, 2006**

|Port|Dakar,<br>Senegal|Cotonou,<br>Benin|Abidjan,<br>Côte<br>d'Ivoire|Tema,<br>Ghana|Apapa,<br>Nigeria|Harcourt,<br>Nigeria|Lome,<br>Togo|
|---|---|---|---|---|---|---|---|
|Container dwell time—average (days)|7|12|12|25|42||13|
|Truck processing time for receipt and delivery of<br>cargo (turnaround time)—average (hours)|5|6|2.5|8|6||4|
|General-cargo vessel preberth waiting time—<br>average (hours)|24|48|2.9|9.6|36|38.4||
|General-cargo vessel stay time (turnaround time)—<br>average (hours)|60|48|2.2|48|40.8|45.6||
|Container cargo—total handled (TEU, annual)|331,191|158,201|500,119|420,000|336,308|7,900|460,000|
|Container-cargo-handling charge (ship to gate)—<br>average ($ per TEU)|160|180|260|168|155||220|
|General-cargo-handling charge (ship to gate)—<br>average ($ per tonne)|15|8.5|13.5|10|8|8|9|
|Bulk dry-handling charge (ship to gate or rail)—<br>average ($ per tonne)|5|5|5|3|||5|

_Source:_ Derived from the AICD port operator database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ *Concession for building and operating container terminals, not the whole port. TEU = 20-foot equivalent unit.

## **Figure 8. Evolution of traffic and vessels moving through the Port of Dakar**

<!-- Start of picture text --> a. Traffic, 2005–09<br>900000<br>800000<br>700000<br>600000<br>500000<br>400000<br>300000<br>200000<br>100000<br>0<br>2005 2006 2007 2008 2009<br>Global transit Transit to Mali<br>Imports Exports<br><!-- End of picture text --> <!-- Start of picture text --> b. Vessels, 2003–09<br>3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>2003 2004 2005 2006 2007 2008 2009<br>Vessels Container ships<br>Carriers Tankers<br>Others<br><!-- End of picture text --> _Source:_ Port Autonomme de Dakar 2010.

# Air transport

## **Achievements**

Senegal is an emerging hub and a major player in air transportation in West Africa (table 8, figure 9), ranking only second to Nigeria. Senegal’s air transport sector accounts for more than 2.6 million seats per year across all traffic categories and has experienced rapid growth over the past few years. The Senegalese market grew more than 50 percent between 2001 and 2007, as the number of seats available 17
# **Non-technical summary**

Based on a unique firm-level survey carried out between late 2007 and early 2008 within the framework of the Wage Dynamics Network, we analyse the flexibility of wages across 14 countries of the European Union (EU). Our objective is to examine the extent and determinants of downward nominal and real wage rigidity.

Downward nominal wage rigidity (DNWR) is defined on the basis of the frequency of nominal wage freezes. Firms freezing nominal base wages at any point during the five-year period prior to the survey are considered to be subject to nominal wage rigidity. Downward real wage rigidity (DRWR) is defined on the basis of wage indexation. Firms that have an automatic link between nominal base wages and past or expected inflation are regarded as subject to downward real wage rigidity. Our survey-based measures of downward nominal and real wage rigidity are closely related to the alternative measures derived by earlier studies on the basis of the wage change distributions.

We find that the incidence of both types of wage rigidity is quite substantial in Europe – approximately 10% of firms experienced wage freezes and 17% of firms applied wage indexation mechanisms. Thus, indexation (DRWR) is much more prevalent in the EU countries than wage freezes (DNWR). This is consistent with other evidence on wage rigidity in most continental European countries, as opposed to the US and the UK. Overall, we find that the non-euro area member states of the EU are more likely to experience wage freezes compared to the euro area member states, whereas indexation mechanisms are more widely used in the euro area countries included in our sample.

Next, we analyse how DNWR and DRWR are related to a number of firm-level and institutional characteristics of labour markets in the countries covered by our sample. We employ the multinomial logit estimation method, which makes it possible to assess these relationships simultaneously for both types of rigidities. Our estimations indicate that countryspecific factors appear to be significant determinants of downward wage rigidities and that institutional differences between countries are an important factor behind this finding. For example, high collective bargaining coverage is positively related with real wage rigidity, while the estimated relationship with nominal wage rigidity is insignificant. A possible interpretation of this finding is that unions have the capacity to provide their members with information about inflation expectations and explain the importance of maintaining the real income level to workers. Thus, union coverage reduces the prevalence of money illusion.

2
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Angola’s existing infrastructure spending is quite high**

<!-- Start of picture text --> SSA<br>LIC-NoFragile<br>LIC-Fragile<br>MIC<br>ECOWAS<br>Resource-Rich<br>Zimbabwe<br>Liberia<br>Cape Verde<br>Ethiopia<br>Angola<br>Kenya<br>Ghana<br>Mali<br>Uganda<br>Botswana<br>South Africa Capex O&M<br>Nigeria<br>Cote d'Ivoire<br>Congo, Dem. Rep.<br>% of GDP 0 2 4 6 8 10 12 14 16 18 20<br><!-- End of picture text --> _Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ LIC = low-income country; MIC = middle-income country; ECOWAS = Economic Community of West African States; SSA = Sub-Saharan Africa; GDP = gross domestic product; O&M= operations and maintenance; CAPEX = capital expenditure.

**Figure 23. Capital investment in infrastructure in Angola and comparator countries**

Investment in infrastructure sectors as percentage of GDP, by source <!-- Start of picture text --> Angola<br>4.0<br>3.5<br>3.0 Resource-rich<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation 38
<!-- Start of picture text --> 2010 2015 2020 2025 2030<br>Year<br>.015<br>.01<br>.005<br>Expected oil rents in GDP<br>0<br><!-- End of picture text -->

|**Period**<br>**Av. **|**expected oil**|
|---|---|
||**rents**|
|2010-14|0.0086|
|2015-19|0.0096|
|2020-24|0.0072|
|2025-29|0.0032|

**Table 7** – Expected oil rents in GDP, 5-year averages

**Figure 6** – Expected oil rents in GDP

_If education, openness, investment, government consumption and the non-oil resource rents behave as they did in the period 1980-2009 and inflation and the democracy index remain at their 2005-09 level, average growth rates of approximately 5.5% can be reached in the upcoming two decades._

**Scenario 3:** This scenario predicts per capita output growth for Ghana under the assumption of positive oil production and otherwise the same assumptions as in Scenario 2. Projections of the expected oil rents in GDP are obtained by comparing GDP projections for the case of zero oil production to GDP projections that account for oil production<sup>4</sup> . Figure 6 and the table to the right of it show that oil rents are assumed to reach around 0.9% of GDP for the first periods, 2010-20, and fall to 0.7% and 0.3% in the periods after.

The model predicts that oil production can improve Ghana’s per capita GDP growth rates by around 0.9%-points until 2020, which implies growth rates of approximately 6-6.5%. The long term difference in growth rates between Scenario 2 and 3 is 0.6%-points, reflecting a) the expected decline in oil production and b) a stronger convergence effect due to higher GDP levels in Scenario 3.

_Assuming oil rents in GDP of 0.9%, 1%, 0.7% and 0.3% for the periods 2010-14, 2015-19, 202024 and 2025-2029, the model predicts an increase in long term average growth rates of 0.6%-points as compared to the scenario without oil production._

> 4A brief explanation of the methodology used can be found in the Appendix.

17
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38

# **Table 4.26: Decomposition of Change in Regional Poverty (Pa, 1985-93) into Growth and Redistribution Components**

|**P. Indicator**<br>**Total Change**|**Growth**<br>|**Redistribution Component**|**Residual**|
|---|---|---|---|
||**Component**|||
|**_Northern Belt decomposition_**||||
|P0<br>-0.031|-0.082|0.040|0.01O|
|P,<br>-0.037|-0.044|0.053|0.001|
|P2<br>-0.006|-0.026|0.048|0.002|
|**_Middle Belt Decomposition_**||||
|P0<br>-0.087|-0.165|0.054|0.024|
|P,<br>-0.052|-0.081|0.069|-0.040|
|P2<br>-0.016|-0.048|0.062|-0.030|
|**_Southern belt Decomposition_**||||
|**P0**<br>-0.126|-0.159|0.030|0.003|
|P,<br>0.003|-0.067|0.048|0.022|
|P2<br>0.016|-0.034|0.043|0.007|

Source: NCS, 1985/86 and 1992/93.

The decomposition of poverty in terms of geographical belts is presented in Table 4.26. The highest decline in incidence has taken place in the Southern belt showing a decline of 12.6 percentage points most of which is due to the growth component (-15.9), and partly due to the redistribution component(3.0). In the middle belt, there was a decline of 8.7 percentage points (- 16.5 and +5.5) due to growth and redistribution respectively. In the northern region, the decline was 3.1 percentage points, -8.2 due to growth and +4.0 due to redistribution. Looking at the other measures it is worth noting that the decline in P, in South of 0.3 percentage points is predominantly due to growth. We find that growth reduced the depth of poverty by 4.9 percentage points and redistribution by 0.8 percentage points, while the residual increased the depth by 2.2 percentage points. In most of the measures, the residual is small relative to both growth and redistribution components, implying that the distribution is quite insensitive to a change of reference from the initial to final year (Ravallion and Datt, 1991).

The relationship between population shifts and intra-sectoral shifts and interaction between sectors and poverty is examined in Table 4.27. It attempts to answer some empirical question as to how much of observed changes in poverty are demographic - due to movements within regions or sectors. The decompositions allow for another rigorous quantification of the contribution of population shifts to poverty alleviation, controlling for intra-sectoral shifts and interactions within sectors and the contribution of intra-sectoral shifts to poverty alleviation controlling for popvlation shifts and interaction between sectors.

The changes in national poverty over the period 1985-92 can be readily decomposed as follows: