rafmacalaba/datause-encoder-data huggingface dataset:
in expectations are gradually eliminated, and rates of return on investment gradually equalize across regions, resulting in a gradual movement of economies towards steady state growth.

The _GMig2_ model extends the _GTAP_ model to consider skilled and unskilled bilateral labor movement across countries, and their impact on growth, remittances and the real incomes of migrants and permanent residents. The bilateral nature of the _GMig2_ model allows us to analyze the effect of changes in the destination country’s immigration policy, targeting particular migrant origin countries. The movement of labor of type _i_ from region _c_ to region _r_ (i.e. changes in labor force which are changes in LFi,c,r) can be determined exogenously, for example through changes in quotas, or endogenously in response to changes in relative real wages. Where migration occurs endogenously, workers (or labor supply) are assumed to respond to changes in the expected real wages between the home (RWi,c,c) and potential host (RWi,c,r) region according to equation (1).

_Ai,c,r_ is a coefficient which takes into account other factors in the migration decision (e.g., language, distance etc.) and is calibrated from the underlying GMig2 Data Base. _ESUBMIGi,c,r_ is a parameter reflecting the extent to which migration responds to changes in the relative expected real wages and is set to 1 in this paper. The extent to which migration is endogenous is dependent on this parameter. Increasing this parameter increases the number of migrants moving, but does not change the directions of their movements.<sup>5</sup> A low parameter value means that the ability of migrants to respond to changes in real wage differentials is limited, due to excessively high costs associated with such movements. Such costs could include difficulties finding a job in the host economy or distance from one’s family.

Note that Equation (1) is calibrated on actual data and incorporates the current state of restrictions on migration in the host country.<sup>6</sup> Figure 1 is used to further explain this. It is

> 5 The authors also tested the model with _ESUBMIG_ =0.4.

> 6 Given _ESUBMIGi,c,r_ =1.

10
# **1. Introduction**

Most economic decisions involve uncertainty and are therefore shaped not only by preferences but also by expectations of future outcomes. For example, if an individual takes an umbrella to work on a clear day, one may infer that the individual is extremely risk averse, but the fact would also be consistent with the individual believing that it may rain later in the day. Due to this fundamental identification problem (Manski, 2004), understanding the expectations that individuals have is of crucial importance. While elicitation of expectations questions have been increasingly used in major surveys in the United States (see Manski, 2004 for a recent review), most of the major surveys in developing countries are static in nature and do not contain information on the subjective expectations of individuals.<sup>1</sup> Possible reasons given for not collecting this information include fears that poor illiterate individuals do not understand probability concepts, that it takes far too much time to ask such questions, or that the answers are of little value. As data collection is becoming more common, a few innovative surveys have experimented with eliciting subjective expectations in developing countries. We review these experiences and conduct new analysis of expectations survey data collected in several developing countries. The results refute each one of the arguments made for not collecting expectations information. People in developing countries do appear to understand probabilistic questions, they are not prohibitive in terms of time to ask, and expectations do provide meaningful information about economic behavior.

We discuss the different methods used for eliciting such information, the relative costs, the key methodological issues involved, and the open research questions. The available evidence suggests that collecting expectations data is both feasible and valuable, suggesting that it should be incorporated into more developing country surveys The remainder of the paper is structured as follows. Section 2 describes and critiques the different methods which have been used to elicit expectations in developing country surveys. Section 3 assesses the evidence of whether typical survey respondents in developing countries are able to understand and answer probabilistic questions, while Section 4 discusses the time and interviewer requirements for implementing such questions in practice. Section 5 shows that the

> 1 For example, some of the best known large scale development surveys such as the World Bank’s Living Standards Measurement Surveys (LSMS), ORC-Macro’s Demographic and Health Surveys (DHS), and the Indonesian Family Life Survey (IFLS) do not include any probabilistic measures of expectations.

- 2 -
TABLE 8. Cushioning Effects of CCTs: Marginal Effects on Schooling and Work

||<br>|Low Educati<br>|on<br>||All<br>||
|---|---|---|---|---|---|---|
||School|School|Work|School|School|Work|
||Overall<br>−0.103*|Rest|ricted|Overall|Restr|icted|
|Permanent Absence (α1)|*|−0.114*|0.0782*|−0.0801**|−0.0919*|0.0694*|
||(0.0341)|(0.0437)|(0.0355)|(0.0284)|(0.0376)|(0.0300)|
|Permanent Absence|||||||
|* CCTs (α2)|0.0818*|0.0833+|−0.0687+|0.0566+|0.0615|−0.0542+|
||(0.0381)|(0.0472)|(0.0356)|(0.0330)|(0.0420)|(0.0307)<br>−0.0257*|
|CCTs (α3)|0.0143|0.0057|−0.0262**|0.0121|0.0053|*|
||(0.0123)|(0.0148)|(0.0096)|(0.0112)|(0.0134)|(0.00902)|
|Time = 2|0.1080|0.1520|−0.2010*|0.0783|0.1180|−0.1500+|
||(0.0957)|(0.105)|(0.0847)|(0.0872)|(0.0964)|(0.0773)|
|Time = 3|0.0261|0.0689|−0.148+|−0.0025|0.0347|−0.1010|
||(0.1010)|(0.1100)|(0.0889)|(0.0927)|(0.1020)|(0.0809)|
|P value (α1+α2) = 0|0.3498|0.2636|0.6224|0.2633|0.2379|0.3854|
|N|28,027|21,464|21,464|32,186|24,531|24,531|

_Source: Familias en Accion_ Evaluation Data.

_<mark>Note:</mark>_ Marginal effects are presented from a fixed-effects linear-probability model (equation (4)), controlling for absence of father for unknown reason; absence of both parents; cubic in child age; sibling composition; dummies for crop losses, illness, and business shocks; and the inverse mills ratio, as computed in equation (2) (see table S3). “CCTs” indicates whether the household lived in a municipality that was receiving CCTs at the time of survey. N is the number of children in the sample pooled across three waves. We included 6,146 households in our initial sample, from which 426 fathers have subsequently departed. Schooling is observed for all children in sample ≥7 years ('overall' sample); work is observed for children ≥10 years ('restricted' sample). Robust standard errors clustered at the municipality level are in parentheses. + significant at 10 percent; *significant at 5 percent; ** significant at 1 percent.

39
# **4.2 Population and labor market**

Uruguay’s population is not expected to change substantially during year 1 and year 2. Projections have been obtained from the National Institute for Statistics and reflect an increase of only 1.4 percent or 38,000 people between the baseline year (2012) and year 2.

The adjustment of the labor market to past output changes is used to project future adjustments. To assess the household level adjustment to changes in the labor market on the macroeconomic level, it is assumed that changes in labor market conditions are proportional to changes in output, based on the estimated past relationship between output and employment, requiring the specification of sectoral and total output-employment elasticities. The elasticities are calculated using the past GDP and employment changes for each sector as the percentage change in employment in the sector between years _t-1_ and _t_ in response to a 1 percent change in sector output for the same period. The calculated parameters have fluctuated quite substantially over the last five years, especially during the 2008/09 crisis, leading to the omission of 2008 and 2009 in their calculation under the no-crisis scenario, but have settled down more over the period 2010-2011.

Employment shares are not expected to change drastically. Under the benchmark scenario, sectoral employment shares will stay basically unchanged with a small shift from manufacturing to commerce, tourism and transport, which are expected to grow at the fastest rate. In the crisis scenario, based on the assumed output response in the respective sectors, the decline in the employment share of commerce, tourism and transport as well as that of other services will be more pronounced.

# **4.3 Interaction of micro and macro data**

As explained in section 2, one of the key assumptions of the simulation is that microeconomic variables when faced with economic crisis do not change their behavior and the growth rates of labor income and profits are the same as the aggregate product rates for each sector. In order to validate the assumption that microeconomic data follows macroeconomic fundamentals, Figure 10 shows the real growth rate between GPD per capita and the per capita official mean income from the available household surveys. As can be seen, in general terms the mean income per capita follows GDP per capita. However, the movements of mean per capita income tend to lag and overreact vis-a-vis the movements in GDP per capita. This behavior does not violate the assumptions needed for the correct implementation of the model, but show that reactions to macroeconomic shock can be seen one period later than expected.

10
Policy Research Working Paper 8972

# **Abstract**

Enumeration areas are the operational geographic units for the collection, dissemination, and analysis of census data and are often used as a national sampling frame for various types of surveys. Traditionally, enumeration areas are created by manually digitizing small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labor intensive. In addition, creating enumeration areas requires considering the size of the population and area within each unit. This is an optimization problem that can best be solved by a computer. This paper, for the first time, produces an automatic designation of predefined census enumeration areas based on high-resolution gridded population and settlement data sets and using publicly available natural and administrative boundaries. This automated approach is compared with manually digitized enumeration areas that were created in urban areas in Mogadishu and Hargeisa for the United Nations Population Estimation Survey for Somalia in 2014. The automatically generated enumeration areas are consistent with standard enumeration areas, including having identifiable boundaries to field teams on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, the automated urban enumeration areas have no gaps. The paper extends this work to rural Somalia, for which no records exist of previous enumeration area demarcations. This work shows the time, labor, and cost-saving value of automated enumeration area delineation and points to the potential for broadly available tools that are suitable for low-income and data-poor settings but applicable to potentially wider contexts.

This paper is a product of the Poverty and Equity 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://www.worldbank.org/prwp. The authors may be contacted at upape@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
achieved by setting _κ_ = _λ_ 1 = 1. To smooth tax changes, we choose an intermediate adjustment of the consumption tax rate relative to its target ( _ζ_ 1 = 0 _._ 5) and a low responsiveness of the consumption tax rate to the debt-to-GDP ratio ( _ζ_ 2 = 0 _._ 001). The selection of values for these policy parameters should be guided by the policy scenario that the team wants to simulate as well as by what they consider feasible as a fiscal adjustment.

- _Public investment_ . Public investment efficiency is set to 50 percent ( ̄ _ε_ = 0 _._ 5), following estimates in Pritchett (2000) for SSA countries. The annual depreciation rate for public capital is 10 percent ( _δ_<sup>_G_</sup> = 0 _._ 10). The home bias for government purchases _ν_ and for investment spending above the initial steady-state level _ν_<sup>_g_</sup> are 0 _._ 6 and 0 _._ 4, respectively. The smaller degree of home bias in additional spending reflects that most of the investment goods are imported in LICs. The output elasticity to public capital _α_<sup>_G_</sup> is set at 0 _._ 12. The severity of public capital depreciation corresponds to _φ_ = 1 and the change in the depreciation rate of public capital is assumed to be a persistent process by setting _ρδ_ = 0 _._ 8. In the baseline, absorptive capacity constraints start binding when public investment rises above 60 percent from its initial steady state ( ̄ _γ_<sup>_GI_</sup> = 0 _._ 60). The calibration of absorptive capacity constraints with _ςε_ = 50 implies that the average investment efficiency approximately halves to around 25 percent when public investment spikes to around 200 percent from its initial steady state.

- _Education and Health Parameters_ Taking logs of the production functions in the non-traded and traded sectors we have

log ( _yj_ ) = _αjψEβj,E_ log( _g_<sup>_E_</sup> ) + _αjψH βj,H_ log( _g_<sup>_H_</sup> ) + other terms ; _j_ = _N, T_ (53) from which the elasticities of expenditure are given by _εi_ = _αjψiβj,i,_ ; _j_ = _N, T_ ; _i_ = _E, H_ from which given _ψi_ , _αj_ and _εj,i_ , we can pin down _βj,i_ . The value chosen for the concavity parameter is _ψi_ = 0 _._ 55, the efficiency parameters are _γE_ = _γH_ = 0 _._ 19, which imply an effective efficiency of 40 percent. In addition, we assume that health expenditures affect the stock of human capital after one year, while education expenditures after 5 years ( _jH_ = 1, _jE_ = 5). Last a larger elasticity is assigned to 20
INFRASTRUCTURE AND GROWTH IN AFRICA

# **Table 2. Infrastructure and growth: panel regression analysis**

_Dependent variable: Growth in real GDP per capita (annual average) Sample: 93 countries, 1960–2005 (non-overlapping 5-year period observations) Estimation: GMM-IV system estimation_

|**Variable**|**[IK1]**|**[IK2]**|**[IK1a]**|**[IK2a]**|
|---|---|---|---|---|
|_Infrastructure development:_|||||
|Infrastructure stock 1/|1.685 **|0.809 **|1.144 **|1.059 **|
|_(first principal component of stocks)_|(0.22)|(0.18)|(0.12)|(0.19)|
|Quality of infrastructure services 2/|0.630 **|0.590 **|0.173 **|0.304 **|
|_(first principal component of quality measures)_|(0.07)|(0.06)|(0.06)|(0.06)|
|_Control variables_|||||
|Initial GDP per capita|-2.796 **|-1.735 **|-2.072 **|-2.313 **|
|_(in logs)_|(0.17)|(0.18)|(0.14)|(0.18)|
|Education|1.305 **|0.852 **|0.982 **|1.650 **|
|_(secondary enrollment, in logs)_|(0.14)|(0.20)|(0.17)|(0.19)|
|Financial development|0.707 **|0.807 **|0.973 **|0.835 **|
|_(private domestic credit as % of GDP, logs)_|(0.08)|(0.09)|(0.12)|(0.12)|
|Trade openness|-0.461 **|-0.141 **|-0.109 **|-0.152 **|
|_(trade volume as % of GDP, logs)_|(0.06)|(0.07)|(0.05)|(0.07)|
|Institutional quality|3.217 **|2.923 **|3.063 **|2.610 **|
|<br>_(ICRG political risk index, logs)_|(0.22)|(0.19)|(0.19)|(0.23)|
|Lack of price stability|-1.843 **|-1.343 **|-1.934 **|-1.883 **|
|_(inflation rate)_|(0.12)|(0.19)|(0.20)|(0.20)|
|Government burden|-1.661 **|-2.003 **|-0.886 **|-0.537 **|
|_(Government consumption as % GDP, logs)_|(0.19)|(0.22)|(0.24)|(0.22)|
|Terms of trade shocks|0.056 **|0.056 **|0.063 **|0.055 **|
|_(first differences of log terms of trade)_|(0.01)|(0.01)|(0.01)|(0.01)|
|No. countries|93|93|93|93|
|No. observations|580|580|580|580|
|Specification tests (p-values)|||||
|(a) Sargan test:|(0.264)|(0.517)|(0.417)|(0.517)|
|(b) Serial correlation|||||
|Second-order|(0.224)|(0.272)|(0.244)|(0.235)|

_Our regression analysis includes an intercept and period-specific dummy variables (not reported here but available from the author). Robust standard errors are reported in parenthesis below the coefficient estimates. ** (*) implies significance at the 5% (10%) level. 1/ Each column of table 2 uses the different aggregate indices of infrastructure stocks reported in table 1. 2/ Our aggregate measure of infrastructure quality uses transformations of the following variables: waiting for main line installation (in years), electric power transmission and distribution losses (as a share of electric output), and the share of paved roads in total roads._ 45
Table 6: Average school participation

|(6)<br>(7)<br>All<br>All<br>2001<br>2001|0.039<br>0.055<br>(0.024)<br>(0.024)**|0.317<br>0.287<br>(0.017)***<br>(0.059)***|50<br>50<br>0.05<br>0.24|n the relevant age group (4-6<br> 2000. The sample is further<br>at most 7 visits in 2001.The<br>ative divisions. Participation<br>uted over the relevant visits.<br>d not have a pre-intervention<br>t 5 percent; *** signi...cant at|
|---|---|---|---|---|
|(5)<br>Enrolled<br>Present<br>2000|0.111<br>(0.033)***|0.499<br>(0.024)***|49<br>0.20|ools and were i<br> visit in early <br> in 2000, and <br>he 7 administr<br>ation was comp<br> One school di<br>** signi...cant a|
|(4)<br>Enrolled<br>Not present<br>2000|0.073<br>(0.036)**|0.298<br>(0.026)***|49<br>0.08|e sample sch<br>-intervention <br>most 6 visits <br>s for each of t<br>rage particip<br>ere averaged. <br>t 10 percent;|
|(3)<br>Not enrolled<br>Not present<br>2000|0.046<br>(0.009)***|0.009<br>(0.007)|49<br>0.35|baselines of th<br>chools in a pre<br>here were at <br>ummy variable<br>the sample ave<br>the children w<br> * signi...cant a|
|(2)<br>All<br>2000|0.085<br>(0.022)***|0.274<br>(0.015)***|50<br>0.24|were in the <br>nd in the s<br>mn head.<br>T<br>ographical d<br>ach child in <br>ion rates of <br>odel is OLS.|
|(1)<br>Pre-program status<br>of the sample<br>All<br>Time<br>Pre-program|Treatment<br>0.001<br>(0.029)|Constant<br>0.377<br>(0.021)***|Observations<br>49<br>R-squared<br>0.00|Notes:<br>The sample consists of children who <br>years) and of children who were fou<br>restricted as speci...ed in each colu<br>regression in column (7) includes ge<br>was calculated as follows: ...rst for e<br>Then for each school, the participat<br>visit in early 2000. The regression m<br>1 percent|

21
# **Contents**

|1.<br>**Introduction .......................................................**<br>I|
|---|
|2.<br>What do we know about the distributional impact of the crisis<br>_._<br>_3_|
|3.<br>Data**.6**|
|_3.1. 1998APIS survey: an opportunity?_<br>_.6_|
|_3.2._<br>_Direct self-reported measures of shock_<br>_.7_|
|_3.3._<br>_Alternative specifications of the shock variable_<br>_.8_|
|_4._<br>**Methodology..9**|
|**_5._**<br>**Results.14**|
|_5.1._<br>_What madefor lesser or greater impact?._<br>_15_|
|_5.2._<br>_Impact on poverty and inequality .16_|
|_5.3._<br>_El-Nino or El-Peso?.21_|
|_5.4._<br>_Income vs. consumption impact .21_|
|6.<br>Conclusion.24|
|Table 1: Macro-economic indicators, by quarter: 1997-1998 .27|
|Table 2: The incidence of crisis-related economic shocks .27|
|Table 3: The estimated consumption and income models (1998 APIS) .28|
|Table 4: Impact of the crisis on_consumption_poverty and inequality .31|
|Table 5: Impact of the crisis on_income_poverty and inequality .32|
|Figure 1: Some key macro indicators in recent years, by quarter .33|
|Figure 2: Change in the cumulative distribution function due to the crisis .34|
|Figure 3: Percentage change in the cumulative distribution function due to the crisis .34|
|Figure 4: The relative magnitudes of income and consumption shocks .35|
|**References ...................................................**<br>**36**|
|Table Al: Descriptive statistics of model variables (1998 APIS) ..................................................<br>38|
Two issues arise in the implementation of the poverty alleviation strategy are targeting and institutional arrangements. Targeting seeks to distribute transfers or other benefits only to those identified as the beneficiary group in the interest of efficiency. The administrative mechanism must not cost so much to operate that it effectively absorbs the savings from excluding the non-needy. In Bahia, geography is one of the indicators with the greatest value as a targeting mechanism. Another good targeting mechanism is individual or family characteristics. Some characteristics are closely associated with poverty and suggest the types of assistance, which would mainly benefit the poor. Nearly all households, which draw their water from wells, are poor. Other housing characteristics closely associated with poverty include the use of latrines and the absence of household lighting or sanitation facilities.

The magnitude and seriousness of the poverty in Bahia call for the active participation of all resources, including non-governmental organizations (NGOs). NGOs have several strengths. They can mobilize and utilize substantial levels of resources, and they are also able to reach underserved populations effectively. NGOs are a strong constituency for the promotion of poverty alleviation goals and programs. There are many NGOs in Bahia covering different fields and areas of the state.

NGOs also have some weaknesses. They are often vulnerable and have difficulty formulating policy proposals. Collaboration with the government is constrained by real and perceived barriers. The dispersion of government responsibilities over a broad range of state agencies makes NGO entry difficult. Also, NGOs differ as to their management and administrative capacities. Some government agencies view NGOs as competitors instead of potential executing agencies. Mistrust hinders collaboration. The government should establish clear and efficient mechanisms for NGO collaboration. Emerging NGO consortia provide one mechanism, which should be explored for fostering greater coordination, dialogue, and joint planning with the government.

# **7.  Poverty Correlates**

The previous sections examine the disparities in key assets between the poor and non-poor. This section takes the analysis a step further and analyzes the relative importance of these and other correlates of poverty in a multivariate setting, and investigates the marginal impact of each individual attribute on the likelihood of a household falling below the indigence poverty line, taking into account other characteristics. The section analyses the impact of experience, labor market association, different levels of education, etc. on the likelihood of being poor for Bahia and Brazil as a whole. The status of the household—poor or non-poor—is regressed on relevant individual and household characteristics using the probit 41