rafmacalaba/datause-encoder-data huggingface dataset:
also those characterized by the highest concentration of migrant population. This is valid in 1984 and, as the Kwankye et al. (2009) suggest, also before independence.

Net migration over time also suggests that the patterns of migration remain constant in direction of migration. Using data from the 1960, 1970, 1984, and 2000 census, Kwankye et al. (2009) notice that in addition to the north-south major migration flow there were some net migration flows within southern regions: from Eastern, Central, and partly Volta regions to Greater Accra and Ashanti regions. The main migration recipients over time were Western, Greater Accra, Brong Ahafo, and Ashanti (except 1984). Over the past 50 decades people migrated from Central, Eastern, Volta (except 1984), Northern (except 1984), Upper East, and Upper West regions.

In section 3 the 1984 census is used as an exclusion restriction for the self-selection equation into migration. Therefore, although relatively close in time to the decision to migrate (in one model 16 years in the other 6), we argue it is a good instrument since it reflects an historical migration pattern that did not change significantly since the colonial period. In other words, had we have access to earlier census data on internal migration, unfortunately not available, the shares of migrant population by district wouldn’t be too different from those reported by the 1984 census. Moreover, the construction of the railroad system had as a by-product some areas developing different migration rates than others. This in turn fostered the development of networks of migrants that via lower migration costs leads to different levels of migration rates nowadays.

# 2.3 Internal Migration and Patterns of Consumption

In Ghana some 56 percent of inter-regional migrants goes to urban areas, yet among this group 2 out of 3 already resided in an urban location before migrating. It is interesting to note that more than 1 out of 3 migrants ending up in rural areas is originally from an urban center. Overall, the large majority of migrants undertake an urban-to-urban path (over 48 percent), followed by some 26 percent that relocate from an urban to a rural location, about 18 percent who stay in a rural area, and only a minority, about 7.5 percent, move to an urban area.

The Greater Accra and Ashanti region together attract over 50 percent of inter-regional migrants, who represent about 33 and 25 percent of the current resident population (in our sample), respectively. About 4 out of 10 migrants select Inland Ghana as their destination as opposed to 60 percent who move to Coastal Ghana (Figure 3). Over 50 percent of migrants are originally from Coastal Ghana: precisely, about 16 percent from the Central region, Volta and Eastern region contribute about 13 percent each, and the Greater Accra and the Western region about 5 percent each. As to the regions of Inland Ghana, Ashanti, Northern, and Brong-Ahafo contribute about 1 out of 3 migrants, whereas just about 16 percent of the migrants move from the two poorest regions in the North (Upper East and West).

8
# THE MANAGEMENT OF PUBLIC EXPENDITURES: AN EVOLVING BA1%K APPROACH

# Introduction

1. World Bank lending and advice to support institutional and policy reform of public expenditure management is of recent origin and of limited, but rapidly expanding, scope. The area where this concern has been most sharply focussed is on the management of public investment programs. Such programs are frequently a drag on public sector activities and hence in need of attention. However, in seeking to improve the decision-making process with a view, for example, to curbing inappropriate or excessive investment, or ensuring a more economic use of public resources, the Bank has inevitably been led away from exclusive concentration on the investment program E= seand towards wider expenditure issues. At the most superficial level, this can be seen in the accelerating substitution of public expenditure reviews at the expense of public investment reviews. More significantly, it reflects an increasing awareness that the difficulties of investment programing cannot be dealt with in isolation from the more general problems of public expenditure management, including the budgetary process, the institutional capacity of both core and line agencies, relations between core and line agencies, and the political economy of budgetary reform. This intensifying focus on institutional issues arises from the well-founded belief that reforms are unlikely to be durable unless the related institutional capacity is strengthened. This type of change is often difficult to effect given characteristic shortages of skilled manpower in most developing countries. It therefore requires both a longer time period for implementation than most Bank-supported activities and presents a new challenge in terms of devising appropriate lending instruments. It is leading the Bank into areas of increasing institutional complexity and political and technical controversy where there are no easy guidelines or precise solutions.

2. This paper attempts to address some of these issues in the light of recent Bank experience. Despite the fact that that experience is short, the paper attempts to develop some guidelines for the future direction of the Bank's work in this area. However, the conclusions should be taken as indicative, not only because prescriptions should be tailored to fit each country's circumstances, but also because institutional reform by its nature does not lend itself to strict models or blueprint approaches.

3. The paper begins with a brief overview of the Bank's recent work on public expenditures both in the context of country economic and sector work (CESW) and lending operations. Itis based primarily on the projects and documents listed in Annex I. This analysis indicates the lack of a consistent or systematic approach to the institutional and procedural aspects of public expenditure management. As background to an attempt to develop more consistency, the paper goes on to discuss the Bank's changing approach towards public expenditures, starting with the concepts of economic
headquarters were taken after the fieldwork was done.<sup>10</sup> More details on the experiment are described by Beegle et al. (2012) who use the same experiment to compare consumption, poverty and inequality numbers across the different methods.

The food quantity estimates were transformed into food energy availability using food composition tables (Lukmanj et al. 2008). The total food energy available was converted to per capita daily averages, adjusting for meals eaten out of the home and meals shared with non-household members. In regards to household calorie needs, we control for age, gender, and whether a woman is breastfeeding, but do not have data on body weight, height, or level of physical activity. The average daily energy requirement is 2068 kcal per capita, averaged across all households. Since households were administered one of the eight modules through random selection, we do not see any differences across modules in this aspect. A household is categorized as food energy deficient or hungry if total dietary energy available is lower than the energy requirement for that household.

# **5. RESULTS**

We utilize this experiment to explore the survey design implications for measuring hunger. We consider both the HCES-direct method that is advocated by numerous researchers and the FBS-CV method that is used by the FAO when making global hunger counts. Since the HCES modules we use are typical of those found in low-income countries where concerns about hunger are most apparent, and our survey setting is also typical of these conditions, the results should be broadly informative about the degree of sensitivity of hunger statistics to variation in survey design.

Table 2 presents hunger estimates derived from HCES alone. The calorie measure displays a great amount of variability across the different survey methods. The estimated amount of daily per capita kilocalories available varies from 1793 Kcals ( module 1, the long list of 58 food items with 14 day recall) to 2677 Kcals for the resource intensive personal diary (module 7). As a consequence, the estimates of hunger prevalence (the proportion of the individuals living in hungry households) vary by a factor of 3.6 and range from 18.8 to 68.4 percent, depending on the survey design. Following the pattern for calorie levels, the highest hunger is again measured with module 1 (68.4% of the population) and the lowest with module 7, with a prevalence of 18.8%. In general, recall modules record lower per capita calories and hence higher hunger prevalence. The usual month approach (module 4) records the second highest

> 10 See Capéau and Dercon (2005) for an econometric approach that can be used when direct measurements are not available.

14
3 Certainly one can use the OECD or World Bank price distortions as inputs into national partial or general equilibrium models to estimate the trade- and welfarereducing effects of a country’s agricultural policies. However, such models are computationally intensive, and the results can be contentious if there is no consensus on what model specification and parameters such as elasticities to use. Even more problematic is that typically they are calibrated only for a particular past year and so are not able to provide a time series of estimated economic effects.

An alternative is to use the raw data in the OECD and World Bank studies to calculate indexes of the trade- and welfare-reducing effects of policies. Anderson and Neary (2005) specify a simple, elegant and theoretically meaningful methodology to provide such measures as a supplement to aggregate PSEs and CSEs.

The goal of this paper is to demonstrate how the Anderson-Neary methodology can be applied using no more information than that assembled already to generate price distortion estimates for OECD member countries. The method may have been ignored to date because it was traditionally thought that price elasticity estimates were necessary to estimate such indices. However, it has recently been shown by Lloyd, Croser and Anderson (2010) that by assuming domestic price elasticities of supply are equal across commodities within a country, and likewise for price elasticities of demand, the index number formulae simplify to a share-weighted function using shares of production and consumption as weights. The resulting measures thereby can be generated as supplements to the current policy monitoring indicators generated by the OECD Secretariat without having to tackle the contentious questions associated with the size of price elasticities (such as whether they refer to the short or long run) and without having to continually update a sector or economywide model.

Drawing on the Anderson and Neary framework, we estimate two indexes which go by the precise descriptors of a trade reduction index (TRI) and a welfare reduction index (WRI). The TRI and WRI are each computed from sub-indices of the production and consumption sides of the market (the Producer and Consumer Distortion Indexes, PDI and CDI), which are derived from nominal rate of assistance (NRA) and consumer tax equivalent (CTE) estimates for individual products,
LTV ratio caps are in place in many countries and have been adapted to deal with real estate cycles through time-varying requirements over the cycle in jurisdictions such as China; Hong Kong SAR, China; and the Republic of Korea. The LTV ratio has the effect of directly curtailing the amount of credit that can be issued and containing leverage, reducing the potential probability of default by screening out high-risk borrowers, as well as reducing the potential losses in the event of actual default.<sup>55</sup> The countercyclical effect can be further strengthened with adjustments of the LTV cap over different stages of the cycle, which can be further differentiated by the size, type (for example, primary residence or not), location, and age of the property, and fine-tuned for different stages of the cycle (see box 3 for Korea‘s approach to LTV regulation). In the Republic of Korea and Hong Kong SAR, China, time-varying LTV ratios—combined with debt-to-income (DTI) requirements—have been credited for helping curtail recent real estate booms. Some countries (such as Australia, India, South Africa, and Spain) also link capital requirements to the LTV ratio by assigning higher risk weights to mortgages with higher LTV ratios.

A sectoral approach to dealing with cyclicality may be appropriate in many developing countries, as credit exposures are often concentrated in key sectors. This approach enables authorities to more accurately identify the source of systemic risk and focus their policy actions. Targeted sectoral measures are also useful when the development of a specific sector diverges from overall macroeconomic conditions (for example, a real estate boom in the middle of an economic recession). These measures can be tailored to various degrees of complexity and are relatively easy to implement, which is a desirable feature in developing country context.

However, there are a few caveats to consider, which will affect the effectiveness of the sectoral measures.<sup>56</sup> First, the effectiveness of the measures depends on the design and effective monitoring. For example, both the LTV and DTI ratios need to be evaluated based on the borrower‘s total exposures, which is not always possible. Countries with centralized credit registry may be better equipped to enforce such measures effectively. Second, there are also concerns that effects may be short-lived, which calls for consecutive interventions. Third, the narrow target increases the concerns of regulatory arbitrage and migration of risk to nonregulated sectors. Last but not the least, many of the sectoral measures that have been experimented with are fully discretionary, and concerns exist about the uncertainty and timeliness of policy actions. The more discretion is applied, the more demanding it is for the supervisor in terms of the ability and willingness to act.

> 55 See Crowe and others  (2011).

> 56 For more discussion about the effectiveness of the LTV and DTI regulation and its limitations, see Crowe and others (2011).

29
# C. Meidco

||(ildsystemi|NewSystemi<br>|
|---|---|---|
|Structure|PAYG DB|PillarII:Fundedindividual<br>accounts(IA's)<br>PillarI:Socialquota(SQ)+<br>minimumpensionguarantee<br>MPG)<br>|
|Contributionrate|8.5%(incl..425%from<br>govt.)+2%(SAR, 1992)|6.5%(incl..225%from govt.)<br>+5.5%ofminimumwage<br>from govt(SQ)+5%to<br>INlFONAVIT;SQ+MPG<br>financedfromgeneral<br>revenues'<br>|
|Benefits|DB:80-100%ofbasesalary<br>(formulaonseparatepage)|PillarII:annuityfrom IA+<br>llIFONAVIT<br>PillarI:annuityfromSQ+<br>MPG|
|Basesalary|Averageoflast250<br>workingweeks|Notrelevant<br>|
|Pensionableage|65|65<br>|
|Yearsforeligibility|10years|25 yearsforMPG;no<br>minimumrequirementforIA<br>|
|Pensionifworkedfeweryrs|0|AnnuityfromIAaccumulation<br>|
|Indexationprovisions|none|Annuity, SQandMPGare<br>price-indexed<sup>2</sup>|
|Minimumnpension|1minimum wageafter10<br>years'contributions <sup>2</sup>|1minimumwageafter25<br>years'contributions <sup>2</sup><br>|
|Widows|90%ofhusband'spension<br>+ownpension|60%ofhusband'spension<br>(jointannuity)+ownannuity|

## Notes-

1. The social quota started at 2.2% of<sup>theaveragewage.Inthefuture,if the</sup> minimum wage goes up with price inflation<sup>while the averagewage risesfaster because</sup> of real wage growth, this percentage<sup>willfall. INFONAVIT is a housingfund from which</sup> workers can borrow to help finance<sup>thepurchaseof ahome.Thisexistedbeforethe</sup> pension reform. As part of the reform,<sup>any balance in the worker'sINFONAVIT account</sup> at time of retirement is incorporated<sup>intotheperson'sretirementaccount.Therateof</sup> return on INFONAVIT has been<sup>verylow-lessthantherateofinflation. Thus,</sup> INFONAVIT will add something<sup>totheworker'spension,butmuchlessthana5%</sup> contribution to the retirement account<sup>would.</sup>

2. Under the old system the minimum<sup>pension=minimumwage. Linkageof</sup> minimum wage to average wage was<sup>adhoc.Undernewsystem,SQand MPGare</sup> formally linked to CPI. Mexico also<sup>plans forprivateannuity to beprice-indexedbut</sup> feasibility and cost remain to be determined.

43
This is not surprising given that the ASI is more narrowly focused while the HDI captures macro changes in an economy’s overall human development.

# **VIII. ESTIMATES OF DEADWEIGHT LOSSES**

The coefficient value of ASI indicates that a 1% (i.e., .01) increment in the value of the ASI changes per capita GDP by $5,650.2 (2010 U$) x .01 = $56.50. This value represents the average impact of a marginal change in ASI on per capita GDP. Given that the ASI has a mean value of .47 and standard deviation of .10, this implies that if a country can improve its ASI standing by one standard deviation, this would result, on average, in a per capita GDP increase of $565. Another way of conceptualizing this is to contrast the maximum value of the ASI with the existing value. Given that the ASI has a maximum value of .64, this value can be used as an aspirational or frontier value in estimating what is, in fact, achievable if the skills system was functioning in an optimal manner. The difference between the existing skills system and an optimal system represents a form of “dead weight loss” - opportunity losses stemming from the underutilization of human capital. Table 11 reports these losses.

The losses range from a low of 11% (Indonesia) to a high of 171% (Nepal). The magnitude of these losses may seem surprising, but it should be noted that skills systems represent the entire economic and social framework within which real output is generated. A smooth functioning skills system generates economic output; a poorly functioning one substantially hinders it. The losses estimated here, in fact, probably underestimate actual deadweight losses. These losses would be substantially higher if the countries analyzed here were compared to a higher performing cluster.

27
Nampula and Zambezia, with the magnitude of the welfare reduction being higher compared to that observed in the previous section. Small, negative welfare incidence is seen across the urban areas in all provinces, with a net welfare reduction of 0.1% for a 10% price increase. The results vary the most for rice price rises. The large positive welfare gains seen in the province of Sofala disappear. Instead, we see moderate negative welfare reductions in both rural and urban areas across all provinces.

These results remain robust to varying the distance within the neighbors that are matched. In Table 8 we show the overall rural-urban CR, PR and NBR estimates using 5 km radius and a 15 km radius cut-off, along with the 10 km radius results from above. We find a very small change in going from 5 to 10 km radius, but the effects remain qualitatively the same. As we increase the radius beyond a certain threshold the sample of neighbors start to remain unchanged as we exhaust all neighbors within a sample cluster. As a result, the estimates remain virtually unchanged between 10 km to 15 km radius. Increasing the cut-off to higher numbers is likely to introduce as neighbors’ households that may be exposed to different microclimatic and soil conditions. In the absence of other common characteristics to match IOF-2014/15 and AIS-2015 households on, this simple spatial imputation appears to provide a robust compromise.

# **4.4 Net welfare effects based on spatially imputed values**

Table 9 presents the welfare effects of the observed 2016 price rises using the NBR elasticities obtained from spatially matching households in the IOF-2014/15 and the AIS-2105. The results for rural areas indicate a slightly higher negative incidence of the price rise, with an overall average reduction in household consumption of 13%. Once again, we find that the effect is by and large driven by the impact of maize price increases, except for the substantial role of cassava in Nampula and Zambezia. The evidence also suggests that there is a negative welfare incidence on urban households of nearly 2.6% of household consumption on average.

Table 10 shows the estimated net welfare incidence of the price increases across quintiles (based on per capita expenditure levels) for maize, rice, cassava and the overall effect. Negative welfare effects tend to decline with an increase in the income quintile. However, the largest negative welfare reduction is seen amongst the 2nd and 3rd quintiles in the rural areas. This net effect is 22
28

# **Wind Electricity Generation**

# 70.

70. The wind generation costs quoted in Figure 3 are representative anualized costs derived from actual 1986 installed costs of 100 kW turbmes in California wind farms, estimated O&M costs, and annual energy production at t25% capacity factor.Y' 71. The oil-based electricity generation costs shown in Figure 3 were derived by finding the least cost generation mix, of large fuel oil steam base load plants and diesel oil gas turbine peaking plants, to meet a hypothetical load duration curve at representative 1985 fuel oil and diesel import prices (shown below) and at fractions of those prices. As fuel prices can change much more quickly than actual generdtion capacity, **this** least **cost generation mix** win **represent a lower bound to actual oil-based generation costs for any given utility.** <!-- Start of picture text -->
- -<br>Hypothetical  System  Load Profile<br>%  of Peak System  Capacity<br>1207<br>0%<br>80%<br>80X<br>40% -<br>___._.  _._  .__<br>o  2  4  6  8  10<br>Hours/year  @ % of Peak Capacity ('000)<br><!-- End of picture text -->

# Thermal Plant Cost Assumptions

400 MW 50 MW Fuel Oil Steam Diesel Oil Gas Turbine Capital Cost including Reserve ($/k%) 1,100 _450_ Annual O&M (% of Cap) 2.5% 3% Lifetime (yrs) 25 15 Annual cost @ 10% ($/kW) 148.50 72.50 Fuel Price US$ 30/bbl (20 e/l) 30 e/l Thermal Efficiency 36% 28% Fuel Cost (e/kWh) 4.7 e/kWh 10.9o/kWh Fuel Price Thermal Efficiency Fuel Cost (e/kWh)

> **25/** 'Study of the Potential for Wind Turbines in Developing Countries, Phase I Draft Report, for USDOE and the World Bank by Strategies Unlimited, December 1986.